AMERICAN THORACIC SOCIETY
DOCUMENTS
Quantitative Imaging Metrics for the Assessment of Pulmonary
Pathophysiology
An Official American Thoracic Society and Fleischner Society Joint
Workshop Report
Connie C. W. Hsia, Jason H. T. Bates, Bastiaan Driehuys, Sean B. Fain, Jonathan G. Goldin, Eric A. Hoffman,
James C. Hogg, David L. Levin, David A. Lynch, Matthias Ochs, Grace Parraga, G. Kim Prisk, Benjamin M. Smith,
Merryn Tawhai, Marcos F. Vidal Melo, Jason C. Woods, and Susan R. Hopkins; on behalf of the American Thoracic
Society and the Fleischner Society
THIS OFFICIAL WORKSHOP REPORT WAS APPROVED BY THE AMERICAN THORACIC SOCIETY ON OCTOBER 1, 2022, AND THE FLEISCHNER SOCIETY ON SEPTEMBER 20, 2022

Abstract
Multiple thoracic imaging modalities have been developed to link
structure to function in the diagnosis and monitoring of lungdisease. Volumetric computed tomography (CT) renders threedimensional maps of lung structures and may be combined withpositron emission tomography (PET) to obtain dynamic
physiological data. Magnetic resonance imaging (MRI) using
ultrashort–echo time (UTE) sequences has improved signal
detection from lung parenchyma; contrast agents are used todeduce airway function, ventilation–perfusion–diffusion, andmechanics. Proton MRI can measure regional ventilation-perfusion
ratio. Quantitative imaging (QI)–derived endpoints have beendeveloped to identify structure–function phenotypes, including
air–blood–tissue volume partition, bronchovascular remodeling,emphysema, fibrosis, and textural patterns indicating architectural
alteration. Coregistered landmarks on paired images obtained at
different lung volumes are used to infer airway caliber, airtrapping, gas and blood transport, compliance, and deformation.

This document summarizes fundamental “good practice”
stereological principles in QI study design and analysis; evaluatestechnical capabilities and limitations of common imaging modalities;
and assesses major QI endpoints regarding underlying assumptions
and limitations, ability to detect and stratify heterogeneous,overlapping pathophysiology, and monitor disease progression andtherapeutic response, correlated with and complementary to,functional indices. The goal is to promote unbiased quantiﬁcationand interpretation of in vivo imaging data, compare metrics obtainedusing different QI modalities to ensure accurate and reproducible
metric derivation, and avoid misrepresentation of inferred
physiological processes. The role of imaging-based computationalmodeling in advancing these goals is emphasized. Fundamental
principles outlined herein are critical for all forms of QI irrespectiveof acquisition modality or disease entity.
Keywords: ventilation-perfusion-diffusion; lung mechanics;
computed tomography; positron emission tomography; magnetic
resonance imaging

ORCID IDs: 0000-0003-1995-1407 (C.C.W.H.); 0000-0002-6062-6377 (J.H.T.B.); 0000-0002-1236-4210 (B.D.); 0000-0001-5461-0646 (S.B.F.);
0000-0003-3007-9947 (J.G.G.); 0000-0001-8456-9437 (E.A.H.); 0000-0001-5915-6745 (D.L.L.); 0000-0002-6329-2325 (D.A.L.);
0000-0002-0936-6979 (M.O.); 0000-0002-5619-4145 (G.P.); 0000-0002-6344-4720 (G.K.P.); 0000-0002-0619-5799 (B.M.S.);
0000-0002-3211-6337 (M.T.); 0000-0003-1988-6844 (M.F.V.M.); 0000-0002-8080-7511 (J.C.W.); 0000-0002-8672-0986 (S.R.H.).
You may print one copy of this document at no charge. However, if you require more than one copy, you must place a reprint order.
Domestic reprint orders: amy.schriver@sheridan.com; international reprint orders: louisa.mott@springer.com.
Correspondence and requests for reprints should be addressed to Connie C. W. Hsia, M.D., Department of Internal Medicine, Division of
Pulmonary and Critical Care Medicine, 5323 Harry Hines Boulevard, Dallas, TX 75390-9034. E-mail: connie.hsia@utsouthwestern.edu.
This article has a data supplement, which is accessible from this issue’s table of contents at www.atsjournals.org.
Ann Am Thorac Soc Vol 20, No 2, pp 161–195, Feb 2023
Copyright © 2023 by the American Thoracic Society
DOI: 10.1513/AnnalsATS.202211-915ST
Internet address: www:atsjournals:org

American Thoracic Society Documents

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Contents
Overview
Key Conclusions andRecommendations
Introduction
Innovation in Lung Imaging
Challenges
Goal and SignificanceMethods
Stereological Principles in
Quantitative Image Analysis andBasic Measurements
Optimizing Accuracy and Precision
Only Unbiased Sampling Can Yield
Unbiased Data
Basic Stereological Measurements
Defining the Reference Space
Assessing Heterogeneity with
Stereology
Assessing Lung Parenchyma
CT Attenuation
Volume Measurement
Small Airway Disease
Interstitial Lung Disease (ILD)
Textural Metrics

Overview
Quantitative analysis of functional andstructural lung imaging complements
traditional visual assessment, playing animportant role in enhancing theunderstanding of physiology andpathophysiology, as well as diagnosis andmonitoring of lung disease and treatment
response. Structural features such as airway
dimensions and branching patterns can bequantiﬁed down to the seventh generation,and different approaches, including
threshold, histogram, and morphologicaland textural patterns, can be used toevaluate parenchyma. Functional imaging
with computed tomography (CT), positronemission tomography (PET), single-photon
emission CT (SPECT), and magnetic
resonance imaging (MRI) use endogenous
molecules, injected contrast agents, orradioactive isotopes as tracers formeasuring changes in airway caliber andmechanical properties, regional ventilationand perfusion distributions, and their
spatiotemporal relationships. Processes suchas gas diffusion across alveolar septal tissue,
through blood, and into capillary red blood
cells (RBCs) can be quantiﬁed using

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Assessing Lung Parenchyma:
MRI-Derived Metrics
Assessing Airway Structure andFunction
Assessing the Vasculature
Vessel Density, Tortuosity, andFractal Dimension
Pulmonary Artery (PA) Size andDistensibility
Large-Vessel Blood FlowAssessing the Pleura
Assessing Regional Lung Mechanics
General Considerations
Measurement Principles andMethods
Assessing Ventilation and Inhaled
Gas Distribution
General Principles
PET
MRI
Assessing Pulmonary Perfusion
CT
PET
Single-photon Emission Computed
Tomography (SPECT)

MRI
Perfusion Metrics
Assessing Ventilation and Acinar
Air–Tissue–Blood Diffusion
Metrics
Assessing Regional
Ventilation–Perfusion Mismatch
PET
SPECT
MRI
Metrics
Validation Approaches
Applying Imaging Metrics in
Computational Modeling to
Predict Lung Function
General Principles
Applications of Modeling to
Imaging-Based Metrics
Conclusions

hyperpolarized gas MRI and used toestimate oxygen transfer. Many of thesetechniques require expensive equipment
and sophisticated computations while
hardware and analytical approaches
are rapidly advancing. Major challenges inthe development and application ofimaging metrics include technical andanalytical standardization across modalities
and platforms, effects of differing lunginﬂation states, image acquisition,
segmentation and measurement techniques,quality of training data, lack of gold
standards for validation of imaging-derived
measurements, and the need to verify
interpretation of imaging-based conclusions
against physiological and clinical outcomes.
This workshop statement summarizes theprinciples and approaches for quantifying
imaging-based data to yield meaningfulphysiological insight, their advantages andlimitations, and future directions.

monitoring of lung disease, providing
insights linking structure to function.
Volumetric CT is routinely used to render
three-dimensional (3D) maps of lungstructures (e.g., individual lobes and thebronchovasculature), and combined with
PET to obtain functional data. Beyondroutine visual assessment, quantitative
imaging (QI) endpoints could be used inclinical trials to identify anatomical andphysiological phenotypes (e.g., airway
remodeling, progressive emphysema andﬁbrosis, textural patterns quantifyingarchitectural alterations) (1, 2). Intravenous
iodine and dual-energy CT (DECT) are used
to separate blood and tissue volumes. In
addition to imaging static structure,
coregistered landmarks on paired images
obtained at different lung volumes are used toinfer regional air trapping, lung compliance,and deformation. MRI using ultrashort–echotime (UTE) sequences improves signal
detection from lung parenchyma, andcontrast agents (e.g., oxygen, hyperpolarized[HP] noble gases) are used to deduce
physiological processes (e.g., distributions ofventilation, perfusion, and alveolar diffusion).
Proton MRI is used to measure regional
perfusion and ventilation-perfusion ratio

Introduction
Innovation in Lung Imaging

Thoracic imaging is a rapidly evolving ﬁeld
with a central role in the diagnosis andAnnalsATS Volume 20 Number 2 | February 2023

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Key Conclusions and Recommendations

 Imaging modalities (CT, PET, SPECT, and MRI) offer innovative quantitative information on lung structure and function,
including regional air and tissue volumes, airway and vascular dimensions, lung mechanics, regional alveolar ventilation,
perfusion, ventilation–perfusion matching, and alveolar–capillary diffusion.
 State-of-the-art imaging and analytical techniques evolve rapidly; assumptions and limitations of each should be clearly stated
to avoid misinterpretation of results.
 Lung tissue density is higher in gravitationally dependent than nondependent regions. Lung volume and posture during image
acquisition affect tissue, air and blood distributions, physiology, and quantification. Acquisition conditions (e.g., lungvolume, breath-hold duration, image resolution, both nominally and functionally) should be documented and standardized
when possible and considered in data interpretation.
 Quantitative analysis of imaging data should follow basic principles of unbiased study design and sampling scheme to ensurethat each part of the structure or region of interest has equal probability of being sampled. Measurements should be related
to a well-defined anatomical or volumetric reference space (e.g., per milliliter of lung or lobar volume) and normative datawhen appropriate.
 Optimizing the accuracy of landmark segmentation, registration, and automated image analysis protocols are major areas inneed of research and validation. Methodology should be clearly described.
 Established guidelines (e.g., for lung densitometry) and characterized reference standards should be followed to maximize
reproducibility among sessions, scanners, and modalities.
 Automated exhaustive voxel-by-voxel image analysis increases precision (reproducibility of measurements) but not necessarily
accuracy (closeness to the “true” value). High-precision measurements may exacerbate systematic bias and reduce theaccuracy of results.
 Care should be taken to select appropriate physiological parameters for correlation with imaging metrics, and the limitationsof comparisons should be considered (e.g., between imaging-derived supine end-inspiratory lung volume and upright totallung capacity by plethysmography).
 Gas exchange is determined by alveolar ventilation (rate of fresh gas delivery to the alveoli), diffusion (rate of gas transfer
across the alveolar–capillary–blood barrier), and perfusion (rate of blood delivery to pulmonary capillaries). Terminology
describing these functions inferred from imaging should be precise to avoid confusion, e.g., specific ventilation withalveolar ventilation, pulmonary blood volume with perfusion.
 Functional lung imaging remains emergent. Many techniques lack rigorous validation or gold standards. There is a need formore studies using robust animal models of disease to directly validate conclusions on the basis of in vivo imaging against
quantitative structural analysis.
 Computational models of physiological processes, grounded in the laws of physics, may be combined with imaging metrics to
aid the prediction, validation, and mechanistic interpretation linking the imaged findings to function.

_ without injected contrast, while(V_ A/Q)
13
N-nitrogen PET provides dynamic data on
physiological processes, yielding novel
insights into physiological andpathophysiological mechanisms.
Challenges

Advances in QI are crucially dependent ontechnology and computational power.
Massive data sets are run through “black box”
algorithms, distilled into selected“biomarkers” that may be correlated with
global function, e.g., forced vital capacity(FVC), 6-minute-walk distance, or ﬁndings
on biopsy samples, to evaluate their ability
to detect, stratify, and monitor lungdisease. Numerous imaging modalities,
manufacturers, platforms, and algorithms may
not integrate smoothly (3, 4). Emergent QI
metrics vary among centers in the methods ofAmerican Thoracic Society Documents

acquisition, quantiﬁcation, interpretation, andextrapolation; all involve assumptions of how
the imaged structures relate to function.
Technological advances contribute to
difﬁculty in standardization. In addition, theuse of medical jargon makes it difﬁcult for
the less initiated to evaluate the potential andlimitations of QI-derived metrics. Systematic
errors may arise at each step (Table 1) ofimage acquisition, processing, and analysis,
including 1) technical issues (e.g., depth of
inspiration, acquisition protocol, signal-tonoise ratio, resolution); 2) nonrepresentative
sampling of parts of interest (e.g., comparing
regions, lobes, or airways); 3) lack of a welldeﬁned reference space (e.g., lobe volume);
4) inaccurate landmark coregistration forevaluating paired images obtained at
different lung volumes; and 5) failure toconsider basic physiology and/or biologicalor spatiotemporal heterogeneity (e.g.,
anatomical or gravitational gradients,
respiratory cycles, nonuniform disease, or
mixed pathology such as coexisting
emphysema and ﬁbrosis). Tables 1 and 2
summarize the basic steps in lung imaging
and analysis with examples, caveats, andpractice principles.
The lung deforms under its own
weight, much like a vertically stretched
SlinkyV spring (5). Lung tissue density ishigher in gravitationally dependent thannondependent regions, as alveolar size at
resting volume is smaller (6–10) except at
total lung capacity (TLC); standardizing lungvolume at acquisition is important. In
addition to ghravitationally based hydrostatic
gradients, variation in alveolar size also
creates apparent perfusion gradients because
capillaries within alveolar walls are closer
R

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Table 1. Steps and considerations in imaging the lung, examples, and practical principles
Imaging and Analytical Steps
Optimize image acquisition

Highlight structures of interest
Delineate structures of interest on images
Filter images
Measure attenuation/density/signal
intensity/rate of signal change
Select and measure imaged structures(e.g., airway, blood vessels,
parenchyma)
Distill data into selected “imaging
biomarkers”

Incorporate relevant assumptions andmodels to infer function
Establish reproducibility of biomarkers
Validate imaging biomarkers against
existing standards depending on
modality
Correlate imaging biomarkers with regionaland global structural and functional
indices
Assess predictive value of imaging
biomarkers

ExamplesPractical Principles and Caveats

End-inspiration vs. end-expiration, supine
vs. prone, before or after bronchodilator
treatment
Use of contrast agents, spin states, or
exogenous tracers
Segmentation, landmark registration andcoregistration, image warping
Reduce noise, sharpen edges, andenhance selected features
Compare global or regional histograms.
Select threshold values. Look forarchitectural patterns.
Determine quality, quantity (e.g., number,
length, diameter, volume), and theirdistribution. Compare groups at global,
stratified, or regional level.
For example, LAA, fSAD, Pi10, VDP

Rates of gas or blood delivery,
conductance or uptake
Repeated measures across platforms,
subjects, and time
Compared with, for example, lung water
gravimetry, microparticle deposition,
multiple-breath gas washout,multiple–inert gas elimination technique
For example, postmortem morphometry inanimal models, surgical or biopsy
specimens, physiological tests andstandards
Monitor serial changes across time,
pathological conditions, or before andafter intervention

Standardize when possible

Describe methodology in detail
Algorithms are available in the publicdomainAvoid overfiltration, which may introduce
distortion or bias
Discrete threshold values may not fully
describe a continuum of changes
Include measures of heterogeneity(e.g., coefficient of variation or error)

Different metrics are at varying stages ofstandardization and validation. Vendorspecific algorithms may not fully
harmonize with one another despite
each being validated
Examine whether assumptions are free ofbias and have been rigorously validated
Examine factors causing variability
Examine limitations of existing standards
to ensure proper use

Ensure correlation with thecomparable/equivalent parameters

May require multicenter trials

Definition of abbreviations: fSAD = region of functional small airway disease; LAA = low attenuation area; Pi10 = wall thickness of airways with
inner perimeter of 10 mm; VDP = ventilation defect percentage.

together in dependent lung, which will varywith lung volume (11). Variation in alveolar
size also creates gradients in regional
alveolar ventilation (V_ A) and fresh gas delivery
to the alveolar space because alveoli innondependent regions expand less for a givenchange in pressure and are less well ventilated.
These deformable properties of the lung also
vary with posture (6–8, 10, 12, 13). The wedge
shape of the lung means that more lungvolume lies in the dependent third in supine
posture than prone posture (13, 14), thus
affecting gravitational deformation. The lungmatches shape with the thorax, resulting in
greater expansion of nondependent lung while
supine, compared with dependent lung (14).
Lung volumes measured by spirometry also
vary with posture (15, 16), thereby

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complicating comparisons of imaging
data obtained supine with those obtainedprone or on upright pulmonary function tests.
Overcoming these challenges iscritical to ensuring that 1) imaging metricderivation is accurate and reproducible and2) measurements accurately represent(patho)physiology to guard against
misinterpretation of results in all QI
applications irrespective of modalities or
disease entities.
Goal and SignificanceThis American Thoracic Society(ATS)–Fleischner Society workshop aims topromote unbiased quantiﬁcation andphysiologically accurate interpretation ofin vivo imaging results to enhance

understanding of pulmonary
structure–function relationships and tocompare what are believed to be comparable
metrics obtained using different imaging
modalities. Addressing the challenges of QI
metrics has a high physiological and clinicalimpact because these imaging modalities are
not risk free. CT and PET involve radiation
exposure, especially when specialized
protocols (e.g., inspiration–expiration, preand postcontrast) are used. MRI may be time
consuming and cumbersome. Contrast
injections may cause adverse reactions or
impair renal function. Advanced imaging
and analysis require sophisticated equipment,
infrastructure, and manual steps or edits that
substantially increase the ﬁnancial burden of

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Table 2. Common sources of systematic errors in imaging and analysis
Type of Error

Examples of Error

Technical issues during acquisition

Biased sampling of structure for analysis

Failure to define an appropriate reference space
Issues with segmentation or registration

Inaccurate assumptions or simplifications

Failure to consider spatiotemporaland biological heterogeneity

Differences in hardware and software, acquisition protocol, signal-to-noise ratio,
and resolution
Differences in posture (supine, prone) and lung volume (full inspiration or
expiration)
Motion artifacts
Not spatially randomized
Not representative of the whole lungComparison of mismatched airways (e.g., from different hierarchical locations)
Expressing results only as a ratio of two quantities without the absolute volumeof the lung, lobe, or region of interest
Inaccurate identification of landmarks
Landmarks have been altered by disease or are congenitally absent
Inability to resolve distal structures (e.g., capillaries from venules on CT,
intraacinar airways on micro-CT)
Assuming alveolar airspaces are spheres
Ignoring postural differences (e.g., between supine imaging and upright lungfunction tests)
Assuming blood contained in large vessels represents local perfusion
Anatomical or gravitational gradients in tissue density, ventilation, and perfusion
Variations in respiratory or cardiac cycles
Nonuniform or mixed pathology

Definition of abbreviation: CT = computed tomography.

health care. It is imperative that thefundamental challenges be systematicallyaddressed to facilitate meaningful advances.

Methods
A multidisciplinary panel of thoracic
imaging, pulmonary medicine, physiology,
pathology, stereology, bioengineering, andbiophysics experts was convened to examine
the 1) accuracy of major current QI
biomarkers derived from CT, PET, and MRI
in representing underlying structure andphysiology; 2) caveats in methodology,interpretation, and extrapolation of QI
biomarkers to structure–function endpoints;
and 3) key knowledge gaps for validation andfurther exploration. Participants reviewed theliterature and focused on QI metrics used toinfer major pulmonary structural andphysiological processes: volume partition,
attenuation-based parenchymal andbronchovascular analysis, mechanics
ventilation, perfusion, ventilation–perfusion
matching, and diffusion. Emerging
technology and applications (17, 18) werediscussed. General principles for accuratequantiﬁcation are emphasized for all
modalities and diffuse lung diseases
irrespective of speciﬁc diagnoses. For each
modality, the committee reviewed current
QI metrics in terms of accuracy, precision
American Thoracic Society Documents

and reproducibility, assumptions, limitations,validation against independent structural orfunctional measures, and how QI metrics
could be incorporated into biophysicalmodels to predict lung function. Discussion
facilitated consensus on current capabilities,
limitations, and future directions. Findings
were synthesized into this report. Althoughspeciﬁc guidelines are not part of this report,
general “good practice” approaches fordeveloping and evaluating emergent metrics
are outlined (Table 1).
Lung nodule analysis, covered
separately by both the ATS and the
Fleischner Society, is not included. The
nascent ﬁeld of artiﬁcial intelligence (AI) or
machine learning (ML) is beyond the scopeof the present report (see Reference 19 forfurther information); some AI algorithms are
publicly available. The committee believes
that a robust understanding of good practiceprinciples and the assumptions andlimitations of QI metrics are prerequisites forthe successful development and applicationof AI and ML to image quantiﬁcation.
Potential conﬂicts of interest were disclosed
and managed in accordance with ATS
policies. Workshop participants with relevant
industry-funded research participated in thediscussion and writing but were recusedfrom comparing the imaging or analysis
methods with others in the relevant sectionsof the report.

Stereological Principles in
Quantitative Image Analysis
and Basic Measurements
Stereology is the statistical science that
forms the basis of a set of practicalmethods (see below) for extracting accurate
and precise quantitative structural
information from imaging data sets ofirregular objects. Stereological methods
make no assumptions regarding shape,size, orientation, or spatial distribution of
objects and are unbiased by design insteadof relying on geometric model assumptions(e.g., assuming that alveolar airspaces
are spheres) that may introduce bias.
Stereology is the method of choice forquantifying lung structure in microscopy,
for which standards, principles, andpractical applications have been rigorously
deﬁned (20–25). The same principles apply
to CT, micro-CT, PET, MRI, and othermodalities (“in vivo morphometry”) (26),used in the brain (27–30), heart (31),and lung (26, 32, 33), among otherorgans.
Optimizing Accuracy and Precision

In a quantitative data set, “accuracy” refers to
the closeness of the data to the true value anddepends on both sensitivity and speciﬁcity (34)that is, accuracy = number of (true-negative 1
true-positive)/(all negative 1 all positive)
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Accuracy
High

Low

Precision

High

Low

Figure 1. Precision, bias, and quality of study design. In an ideal study design (left uppertarget), there is no bias, and the measurements (solid dots) cluster tightly (high precision) inand around the target “true value” (high accuracy). In a less optimal design (left lower target),precision is lower but without bias, as the measurements scatter near the target. In a poor
study design (right upper target), highly precise but biased measurements ensure that thetarget will never be hit. In another poor study design (right lower target), bias combined withlow precision allows a small possibility of hitting the target. Therefore, more measurements do
not increase accuracy if bias exists at higher levels of study design (e.g., sampling ofstructures or regions, or intersubject heterogeneity). Automated exhaustive measurements
could aggravate inaccuracy and reduce efficiency because of the time and effort required toachieve a given coefficient of error (the “do more less well” principle) (25, 36, 37). Adapted bypermission from Reference 35.

measurements. “Bias” refers to systematic
errors leading to inaccuracy, such as improper
sample selection, assumptions thatsystematically deviate from reality, incomplete
visualization of the structures of interest, or
overprojection. “Precision” refers tomeasurement reproducibility and depends
mainly on sampling design and size anddistribution of the sample (35). Bias can beneither detected in the data nor decreased bymaking more measurements and is avoided apriori only by rigorous study design and using
unbiased methods throughout the study. In
contrast, precision can be checked and, if
necessary, adjusted by increasing the sample
size or number of measurements. It is more
efﬁcient to invest extra effort into the higher
levels of study design (e.g., more subjects per
study group, more regions of interest per
subject) than the lower levels (e.g., more voxels
per ﬁeld of view), because the former morerobustly addresses interindividual andintraindividual variability. Smart data are more
efﬁcient than big data. By applying unbiased
sampling techniques, it is not always necessary
to exhaustively analyze each voxel (the “domore less well” principle) (25, 36, 37). In awell-designed study, precision is tuned suchthat the coefﬁcient of error within subjects
166(“noise”) is signiﬁcantly smaller than thatbetween subjects (biological variation) within astudy group (“signal”); exhaustive
measurements to further increase precision
beyond this point are inefﬁcient because of theextra time and effort required (37) and may
amplify systematic errors (Figure 1). However,repetitive imaging can often be made to assess
response to intervention. Baseline betweensubject variability becomes less important inlongitudinal studies in which subjects act astheir own controls.
Only Unbiased Sampling Can Yield
Unbiased Data

The practical stereological methods are
sampling and measurement. Unbiased
sampling designs guarantee statistically
representative samples and generally must be
spatially randomized (each part of thestructure of interest has equal probability of
being sampled). For anisotropic lungstructures (airways and vessels), someparameters (surface area, length) also require
spatial orientation randomization. Speciﬁcsampling designs have been developed for
different purposes, such as systematic
uniform random sampling for global
assessment, isotropic uniform random

sampling for randomization of orientation(e.g., surface area of conducting airways),
stratiﬁed sampling for targeting
subcompartments deﬁned by anatomy(e.g., lobes) or pathology (e.g., normal vs.
diseased regions), fractionator sampling for
estimation of particle numbers independentof tissue deformation (e.g., alveoli), andproportionator sampling for weighted
sampling with known probability according
to deﬁned tissue characteristics (e.g., markerlabeled regions) (20, 35, 38, 39). Voxel-byvoxel automated image analysis ensuresneither accuracy nor precision of the results,
nor does it obviate the need to strictly follow
stereological sampling principles. Systematic
sampling of micro-CT images has been used
to explore differences between surface andcore alveoli in mouse lung (32) and tocorrelate ex vivo imaged small airway gas
trapping to in vivo lung tissue measurementsin chronic obstructive pulmonary disease(COPD) to characterize terminal bronchiole
loss, narrowing, and obstruction (33).
Basic Stereological Measurements

First-order parameters are volume (threedimensional [3D]), surface (two-dimensional[2D]), length (one-dimensional [1D]), andnumber (zero-dimensional [0D]).
Measurements are performed using
geometric test probes, such as points (0D),lines (1D), planes (2D), or volumes (3D).
The probes interact with the imaged pixels or
voxels, creating “events” that can be counted.
Raw counts provide ratios (volume, surface,length, and number densities) that are
multiplied by the reference space volume toobtain absolute measures for the lung or
subcompartment.
Defining the Reference Space

It is crucial to deﬁne and measure abiologically meaningful reference space(e.g., volume of the lung or lobe) as thestarting point for analysis and endpoint fordata reporting. Measurements expressed
only as ratios (e.g., “number per unit
volume”) are subject to the “reference trap,”
whereby a change in the ratio can be due toa change in the numerator, the denominator,
or both, leading to interpretive ambiguity(40). An efﬁcient way of measuring volume,
point counting (41), is used to quantify
acinar components by micro-CT (42, 43)
and extrapulmonary organ volume by CT(44, 45), a forerunner of voxel-by-voxel
summation of the volume of the imaged
structure.

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Assessing Heterogeneity with
Stereology

Object size may be expressed as a number- orvolume-weighted mean volume. When both
number- and volume-weighted volumes are
known, an object’s size distribution can be
estimated. Spatial distribution of objects in
subcompartments within a reference space
may be expressed by a “relative labelingindex,” that is, (observed number of objects
per subcompartment)/(expected number ofobjects per subcompartment if randomly
distributed) (46). Stereology has been applied
to quantify acinar structures by HP gas MRI
combined with modeling of alveolar gas
diffusion to infer regional gas exchange andits heterogeneity in normal and pathologicalstates (26).

Assessing Lung Parenchyma
CT Attenuation

CT lung attenuation (measured in
Hounsﬁeld units [HU]) of a voxel comprises
air, tissue, and blood and is extensively usedin assessing emphysema, air trapping,ﬁbrosis, and inﬁltrative conditions (47)
(Table 3). Interscanner variability incalibration, radiation dose, acquisition andreconstruction protocol, lung mask, andother parameters affects comparisons acrossplatforms and institutions and may beminimized by frequent calibrations of air andwater and by using lung-speciﬁc phantoms
to ensure measurement consistency (48). The
QIBA (Quantitative Imaging Biomarkers
Alliance) has published a detailed proﬁle forlung densitometry with standardized
protocols to minimize these issues (49).
Volume Measurement

Segmentation of lungs from other structures
allows calculation of lung volumes (50).
Lung volumes measured using physiologicalmethods typically refer to gas volume,
while conventional CT attenuation–derived“anatomical” lung volume also includes
tissue volume and, presumably,
microvascular blood in vessels below thelimits of CT resolution. Lobes may besegmented on the basis of bronchial andﬁssure anatomy (51). Total lung or lobe
volume may be partitioned into air andair-free “tissue” volumes (10, 52); the latterincludes microvascular blood volume(53–56).
Lung segmentation algorithms vary intheir inclusion of central vasculature,
resulting in differences in volume
measurement (57). The QIBA protocol
provides a basis for standardizing thesoftware for these metrics (49). Incompleteor partially obliterated ﬁssures could impede
lobar segmentation. Approaches combining
bronchial and ﬁssure anatomy with deeplearning can minimize errors (58, 59).
Parenchymal attenuation varies withlung volume (10). CT images are typicallyacquired at end-inspiration or endexpiration, with potential variations intranspulmonary pressure at a given lungvolume, thereby introducing variability inresults. Submaximal inspiration leads tounderestimation of pulmonary emphysema;
both lung inﬂation and tissue loss inﬂuence
CT densitometry (60). Although measuringtranspulmonary pressure is impractical inmost studies, it is imperative to recognize
errors caused by lung volume variations andtrain the subject with detailed breathing
instructions (48, 49). Spirometric gating
facilitates a consistent lung volume but isoften impractical for large multicenterstudies (61–64). For longitudinal studies,density metrics may need to be adjusted forlung volume. Progressive emphysema or
ﬁbrosis will itself change lung volume.
Alternatively, lung volume may be included
as a covariate in multivariate analysis.
The attenuation of air is 21,000 HU,
and that of water is 0 HU (65). Tissue is acomposite of materials (blood, protein, fat)Table 3. Types of quantitative computed tomography analysis in diffuse lung disease

First-order metrics

Higher order metrics

ExamplesUtility

Attenuation histogram
Mean
Median
Skew
Kurtosis (peakness)
Threshold (low or high attenuation)
Matched images at different lung volumes for
PRM or DPM techniques
Fractal dimensions
Run-on matrices
Gray-level co-occurrence matrices

Summarize statistics of attenuation distribution forwhole lung or ROIs
Calculate lung or lobar volumes and regional
tissue density (fractional tissue volume)
Assess absolute or percentage of low- or highattenuation volumes
Distinguish among air trapping, obliteration ofalveoli, and parenchymal abnormalities
Texture analysis
Reticular infiltrate
Consolidation
Ground-glass opacity
Honeycombing
Traction bronchiectasis
Describe spatial relationships among features
Optimize (semi)automatic detection andclassification of anatomical features
Detailed comparisons among ROIs within andamong subjects
Use complex mathematical and statistical models
Measure dimensions (e.g., diameter, cross-sectionalarea, length, wall thickness)
Describe branching patterns (e.g., angles,
symmetry) and spatial relationships

Model based (supervised and/or
unsupervised machine learning algorithms)

Specific structures

AirwaysConducting blood vessels

Definition of abbreviations: DPM = disease probability measure; PRM = parametric response mapping; ROI = region of interest.

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Relativefrequency (%)
20
Emphysema
Normal15
Change in voxel
index (–950 HU)
10between normal (—)
and emphysematous
5(- - - -) lung0
–1,000 –950

–800

Change in 15th
percentile point
between normal (—)
and emphysematous(- - - -) lung

–700

–600

–500

Hounsfield Units (HU)

Figure 2. Attenuation histograms in normal lung and in emphysema and the derivation ofdensitometry indices. In this example, the 15th percentile point (in Hounsfield units [HU]) is
defined as the cutoff value below which 15% of voxels with the lowest density are distributed.
The voxel index at a threshold of 2950 HU is shown and defined as the percentage of voxels
with values less than 2950 HU. Adapted by permission from Reference 368.

with nonzero attenuation. Variability in tissue
attenuation can arise from ﬂuctuations inregional perfusion, blood volume, andextravascular compartments. Highattenuation regions (66) may reﬂect
atelectasis, blood, ﬂuid, cells, or ﬁbrosis.
CT-derived lung tissue volume overestimatesanatomical extravascular lung tissue volume(55) and correlates poorly with in vivo
alveolar gas-exchanging tissue (including
microvascular blood) volume measured using
physiological methods at a similar meantranspulmonary pressure; discrepancy
remains even after subtracting pulmonary
blood volume (PBV) and may be due partly
to residual blood within microvessels below
the CT resolution limit (,1 mm) (56). Thesesources of attenuation changes contribute tovariability in CT-based metrics for assessing
the tissue compartment. Some differentiationof blood from extravascular tissue is possible
through the use of intravenous contrast agent
or DECT (67, 68). Four-dimensional dynamic
CT (69) permits the quantiﬁcation of regional
tissue nonlinearity and hysteresis (reﬂecting
difference between inspiratory and expiratory
lung compliance), although at a lower
resolution and higher radiation exposure.
MRI also provides dynamic imaging, but withlower resolution, relatively lower air/tissue
contrast, and fewer standardized metrics.
First-order CT-derived metrics based onvolume and the attenuation histogramprovide statistical summaries for regions of
168

interest. Selected attenuation thresholds havebeen adopted to quantify low- and highattenuation parenchymal volumes asreﬂecting alveolar obliteration and/or small
airway obstruction and parenchymal
inﬂammation and ﬁbrosis, respectively.
The mean lung attenuation with or without
spirometry gating (70) and absolute or
fractional lung volume below selectedattenuation thresholds at full inspiration(e.g., 2910 HU [71], 2950 HU [72, 73], and2960 or 2970 HU [74]), or the lowest 15th
percentile of attenuation histogram (75),have been used to index terminal air spaceenlargement and/or alveolar obliteration inemphysema (Figure 2). As both radiationdose and reconstruction algorithm affectdensitometry results, threshold selection
should be adapted to local protocol (76).
CT-derived total lung volume (TLV) may bestatistically adjusted to a “predicted”
physiological TLC (77–79) or the log(TLV)
analyzed as a covariate (79).
Caveats: Formal guidelines are lacking
for the selection of pulmonary function tests
in validating QI metrics. Nonetheless, there
are strong correlations between CT-derived
TLV and plethysmographic TLC in anormal patient population (80) and inindividuals with idiopathic pulmonary
fibrosis (81). CT-derived indices also
correlate robustly with expiratory subcompartments (e.g., functional residual

capacity, residual volume, residual volume/
TLC) in representing gas trapping in small
airway disease (82, 83). In COPD, measures from inspiratory CT (e.g., lowattenuation area < 2950 HU) show larger
discrepancies against spirometry than plethysmography and in predicting spirometry
as COPD severity increases (84), whereas
measures from expiratory CT (e.g., lowattenuation area < 2856 HU) correlatewith spirometry in smokers (85). In COPD,
TLC by plethysmography is significantly
larger than that by end-inspiratory highresolution CT (86, 87); discrepancy correlates with air trapping and hyperinflation,
possibly reflecting a postural difference
between modalities and/or plethysmographic underestimation of alveolar pressure as airway resistance increases (88, 89).
Improving the accuracy of segmentation,
landmark registration, and automatedimage analysis is under active research.
Advanced imaging techniques and analysis are often at early developmental stages
and not rigorously validated. Vendorspecific algorithms may provide metrics
incongruent with one another regardlessof separate validation of each.
Small Airway Disease

Pathology of small conducting airways(<2 mm in diameter) may be detected on
CT images as mosaicism (heterogeneousparenchymal attenuation on inspiratory CT),
air trapping (expiratory CT), ground-glass
opacities, centrilobular nodules, tree-in-bud
opacities, and subsegmental atelectasis(90, 91). To quantify air trapping, paired
inspiratory/expiratory CT may be aligned forlandmark coregistration. Individual voxels
are classiﬁed as low attenuation, functional
small airway disease (fSAD), or normal using
either ﬁxed attenuation thresholds(parametric response mapping [PRM])(92–99) or continuous distributions ofvolume-dependent attenuation changes(disease probability measure [DPM])(92, 100). Air-trapped regions on expiration,
without voxels classiﬁed as low attenuation
on inspiration, indicate fSAD with normal
attenuation on inspiration but reduced
absolute attenuation on expiration (PRM) or
relative attenuation changes with lungvolume changes (DPM). fSAD from either
method correlates with multivariate lungfunction endpoints (95–98, 100). DPM and
PRM yield consistent results in normal
subjects. In subjects with COPD, DPM

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AMERICAN THORACIC SOCIETY DOCUMENTS
A

B

C

Fraction of pixels in CT image

0.08
0.07
0.06
0.05
0.04
0.03
0.02
0.01

Hounsfield units

0

00
–8
00
–7
00
–6
00
–5
00
–4
00
–3
00
–2
00
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00

00

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,0

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–1

00

00

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00

–2

00

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00

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00

–5

00

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00

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00

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0

,0
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00
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00
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00
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00

–5

00

00

–6

00

–7

–8

00
,0

–9

–1

00

0

Hounsfield units

Figure 3. (A–C) Coronal computed tomography (CT) reconstructions and corresponding CT histograms from (A) a healthy individual, (B) apatient with mild lung fibrosis, and (C) a patient with advanced lung fibrosis. In the healthy individual with no lung fibrosis, the CT histogram is
sharply peaked and skewed to the left compared with a Gaussian normal distribution. In the patient with mild fibrosis, the curve is less peaked(less kurtotic) and less skewed. This tendency is even more substantial in the patient with advanced lung fibrosis. Reproduced by permission
from Reference 101.

classiﬁes more voxels as emphysematousand gas trapped than PRM and may be lessinﬂuenced by scanner or respiratory effort
variability (92, 100). Compared withmultibreath nitrogen washout testing andairway oscillometry, both PRM and DPM
correlate with airway resistance andreactance; DPM correlations are slightly
stronger. Emphysema (%) for both correlates
best with multibreath nitrogen washout
measure of acinar airway inhomogeneity(100). The practical signiﬁcance of thedifferences between DPM and PRM
remains unclear; more comparisons
are needed.
Caveats: Image coregistration requires
reliable tracking of landmarks; the accuracy of landmark identification is usually
impossible to validate. Landmarks may
be affected by disease (e.g., fissures for
tracking lobar volume may be obliterated
in advanced COPD or interstitial lungdisease [ILD] or congenitally incomplete
or absent).

American Thoracic Society Documents

Interstitial Lung Disease (ILD)

Thin-section CT is the standard method for
imaging ILD subtypes, severity of inﬁltrates,
inﬂammation, injury, or ﬁbrosis andmonitoring progression and therapy. Subtle
parenchymal changes in subclinical orregional ILD can be quantiﬁed from wholelung attenuation histograms (Figure 3).
Architectural distortion and regional
heterogeneity in reticular inﬁltration,
ground-glass opacity, or consolidation may
be captured using texture-based metrics(101) (Table 3). Common metrics include
the fractional volume of high attenuation(e.g., between 2600 and 250 HU) (102)and reticular/honeycomb abnormalities(103, 104). In addition, regional tissue
density calculated from the attenuationdifference between intrathoracic air and airfree tissue, its coefﬁcient of variation (54),and the skewness and kurtosis of attenuation
histogram (54, 101) can be used to assess
local or global heterogeneity of attenuation,
discern divergent patterns between
emphysema and ILD progression (Figure 4),

and detect subclinical abnormalities inkindred with telomerase mutations
predisposing to pulmonary ﬁbrosis (105).
Caveats: Current smoking status increases
baseline lung attenuation independent ofunderlying pathology (77). A change insmoking status could affect the measurement and interpretation of changes inlung attenuation. Selection and validationof approaches remain empirical.
Textural Metrics

Measurement of attenuation alone provides
insufﬁcient information. Textural metrics
characterize the 3D architecture of andrelationships among airspaces, lung tissue,
and bronchovasculature (106–112).
Emerging techniques incorporate imaging
data into machine learning and statisticalmodels to classify disease types and severity,with optimization algorithms that iteratively
reﬁne model predictions. The optimized
models undergo clinical/physiologicalvalidation against an external unlabeled data
169

AMERICAN THORACIC SOCIETY DOCUMENTS
A

C

Interstitial Lung Disease
Mild (II)Moderate (III)Severe (IV)

8,000

8,000

6,000

6,000

*§†

Tissue volume, mL
FTV
CV of FTV
among lobes

0.1

1,200
*

900

*
600

300

300

0

0

0.6

0.3

*§

*§†

*§†

0.2

Mild (II)Moderate (III)Severe (IV)

More Severe (V)CV of FTV
within lobes

*

*

§†‡

0.2
*§†‡

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0.4

*§†

*§
*#

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*

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Normal (I)

*

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600

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2,000
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*§

4,000

*§

2,000

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Emphysema

*

*§
4,000

0

B

Emphysema

Interstitial Lung Disease

More Severe (V)Air volume, mL

Normal (I)

*
*

0.3
0.1

I

II

III

IV

V

I

II

III

IV

V

Disease Severity

Disease Severity

Figure 4. (A and B) Computed tomography (CT) quantification of disease severity in patients with (A) interstitial lung disease (ILD) (idiopathic
pulmonary fibrosis) or (B) emphysema. In A and B, CT images (upper row), fractional tissue volume (FTV) color maps (middle row), and threedimensional renderings (lower row) are shown for representative patients with different degrees of disease severity. (C) Side-by-side comparison
between ILD and emphysema for whole-lung air volume, tissue volume, FTV, and CV of FTV among and within the lobes at prone endinspiration. Data are mean 6 95% confidence interval. ILD severity was classified according to forced vital capacity (percentage predicted) as
mild (stage II, >80%), moderate (stage III, 50–80%), severe (stage IV, 30–50%), or more severe (stage V, ,30%). Emphysema severity wasclassified according to forced expiratory volume in 1 second (percentage predicted) as mild (stage II, .75%), moderate (stage III, 51–75%),
severe (stage IV, 21–50%), or more severe (stage V, <20%). *P , 0.05 versus stage I (normal), §P , 0.05 and #P = 0.06 versus stage II (mild),†
P , 0.05 versus stage III (moderate), and ‡P , 0.05 versus stage IV (severe) by analysis of variance. Reproduced by permission from
References 53 and 54. CV = coefficient of variation.

set (104, 107, 113, 114). These approaches(Table 3) seek to standardize theclassiﬁcation of overlapping heterogeneous
disease phenotypes and validate the utility ofcomputational and statistical models forpredicting outcome (93, 112).
Caveats: Algorithms for textural analysis
rely on expert training and a fixed or a
mixture of imaging protocols that may
not be transferable to other cohorts thatuse a different mix of protocols and scanners. Standardization of proprietary
image reconstruction algorithms poseschallenges in multicenter investigations
regardless of individual validation. Vendor-based differences in image generation
and analysis must be accounted for, often
handled as a statistical model in whichcovariates include the scanner and/or
analysis source.

170

Figure 5 presents a composite ofCT-based methods for assessing lungstructure from which inferences about lungfunction may be made.

Assessing Lung Parenchyma:
MRI-Derived Metrics
Conventional 1H MRI measures tissue signal
intensity using short–echo time and UTE
methods (115–117) but with longer
acquisition times and lower spatial resolution
than CT (comparisons are provided inTable 4). MRI is advantageous whenradiation exposure is to be avoided oriodinated contrast is contraindicated (e.g., inpediatrics, pregnancy, or serial assessment ofdisease progression). Inhalation of an HP
noble gas with MRI provides spatial
information about ventilation and perfusion

heterogeneity with higher resolution thanPET and nuclear imaging techniques, withunique capabilities for measuring
alveolar–capillary gas diffusion and lungmicrostructure.
In hyperpolarization, nuclear spinsof gas atoms are aligned in a small magnetic
ﬁeld outside of the scanner (118). Wheninhaled, spatial distribution of themagnetized exogenous gas can be tracked
using specialized receiver coils. The ﬁrst
report of 129Xe MRI in a mouse heart–lung
preparation (119) was followed by patientstudies using HP 3He. Hyperpolarization
enhances 129Xe and 3He MRI signal by
105. The subject inhales up to 1 L ofgas (for adults) from functional residual
capacity, and scanning occurs during abreath hold (5–15 s). The doses (mixed withnitrogen) typically contain no oxygen, as
it shortens the 1-hour “half-life” ofAnnalsATS Volume 20 Number 2 | February 2023

AMERICAN THORACIC SOCIETY DOCUMENTS

A

B

C

D

E

F

G

H

I

Figure 5. Composite of methods using computed tomography (CT) to visualize and assess lung structure and function. (A) Segmentation ofwhole lung, left and right lung, and individual lobes down to the sublobar segments, as described in Reference 369. (B) Airway segmentation
with automated naming down to the sublobar segment. (C) Segmentation of pulmonary vascular tree with lobar fissures. Fissure completeness
report is important for endobronchial lung volume reduction approaches in chronic obstructive pulmonary disease. (D) Voxels ,2856
Hounsfield units (HU) at residual volume (or functional residual capacity) have been designated as air trapped. Spheres showing regions of air
trapping concentration are color coded for lobar location. (E) Heated object map of pulmonary blood volume distribution derived using dualenergy CT (47-year-old female smoker). (F) Voxels below 2950 HU at total lung capacity (spheres) are designated as “emphysema-like.”(G) Subpleural view. First, the isosurface 2 mm beneath the pleural surface (i.e., lung mask boundary) is computed. A subpleural rendering
uses the posterior-facing portion of the lung isosurface to orthogonally project the density values and render the final topographic multiplanarreformatted (tMPR) image (patient with idiopathic pulmonary fibrosis). (H) Through matched inspiratory and expiratory images, the ventilation
defect probability map demonstrates regions of functional small airway disease (yellow), emphysema (purple), and normal lung (green) mapped
onto a tMPR image in which the airways and their associated parenchyma are projected onto a flat surface. (I) Adaptive multiple feature
method–defined texture map (370) of ground-glass opacities (green), ground-glass reticular (blue), honeycombing (yellow), emphysema(light blue), bronchovascular (pink), and normal (gray) (patient with interstitial pulmonary fibrosis). Courtesy of E.A. Hoffman.

hyperpolarization (120). Supplemental
oxygen is administered before and afterscanning as required.
Using inhaled HP 3He or 129Xe,
subvoxel resolution (300 μm)
measurements can be made (121–124) that
exploit the Brownian motion of these gases
and the restriction to gas movement by
American Thoracic Society Documents

alveolar walls and terminal airspaces (125).
MRI-derived apparent diffusion coefﬁcients(126) are highly sensitive to airspaceenlargement and alveolar recruitment (127),
may be abnormal in the absence of CT
and/or spirometric abnormalities (128), andcorrelate highly with histology (122–124,
129–131).

MRI has also been used to quantify lungcancer (132, 133). Conventional 1H MRI is
useful in differentiating inﬂammation fromﬁbrosis (134, 135). Alveolitis presents as highsignal intensity on T2-weighted sequencesand early enhancement on contrast-enhanced
magnetic resonance (MR) sequences, whereas
ﬁbrotic lesions present as low signal with late171

AMERICAN THORACIC SOCIETY DOCUMENTS
Table 4. Comparison of magnetic resonance imaging and computed tomography inthe assessment of diffuse lung disease (emphysema, interstitial lung disease, acuterespiratory distress syndrome)
Strengths
CT

MRI

Limitations

Rapid (,10 s) acquisition
High spatial resolution
Excellent contrast among emphysema,normal lung, and fibrotic lungCan quantify regional structural features
down to 1–2 mm
Sensitive to many aspects of function
Sensitive to alveolar geometry (in vivo
morphometry)
Estimates acinar airspace size andalveolar wall thickness
Can evaluate perfusion and gas
diffusion

Exposure to ionizing radiation
Most acquisitions provide structural
rather than functional information

May require specialized hardware
More complex image analysis
UTE sequence requires signal
averaging
Structural imaging generally lower
resolution than CT
Low proton density of aerated lungtissueAir–tissue interfaces degrade MR
signal properties and reduce SNRLong acquisition time increases
cardiorespiratory motion artifacts

Definition of abbreviations: CT = computed tomography; MR = magnetic resonance;
MRI = magnetic resonance imaging; SNR = signal-to-noise ratio; UTE = ultrashort–echo time.

enhancement (136). Lung deformation
evaluated by coregistration of paired
inspiratory/expiratory UTE MRI identiﬁes
regional ﬁbrosis with acceptablecorrespondence with CT (137). For assessing
ILD, UTE MRI protocols typically include
noncontrast breath hold, steady-state free
precession, and contrast-enhanced
examinations providing complementary(138) data to high-resolution thin-slice
CT, providing both functional andstructural information in one examination(134, 139).
129
Xe “dissolved phase” MRI exploits therelatively high tissue solubility of xenon that
results in its transmembrane diffusion throughthe alveolar wall into the bloodstream (140),
where it binds transiently to hemoglobin in amanner similar to oxygen (140, 141, 142).
Whole-lung spectroscopy (143, 144) has
shown distinct resonance frequencies of xenon
in gas, tissue, and blood compartments sothat alveolar tissue density maps may be
acquired to directly and regionally characterizeﬁbrosis (145).
Caveats: MRI, particularly HP gas MRI,is expensive; the latter requires specialized
hardware. Proton MRI using low-fieldscanners may reduce cost, and advanced
image reconstruction with motion compensation during free breathing is rapidly
evolving; reproducibility and validation
studies are currently limited.

172

Assessing Airway Structure
and Function
The human airway tree has .23 branching
generations and exhibits 3D hierarchicalheterogeneity in dimension, composition,and physiology; variations also exist withrespect to sex and body and lung sizes. The
ﬁrst six or seven airway generations arereadily visualized on inspiratory CT (thetrachea is usually generation 0). The
hierarchy must be considered when sampling
airways for analysis and inferring airway
structure and function. Selection of airways
on the basis of a structural property (e.g.,luminal diameter) or at random may result
in comparison of airways from different
hierarchical levels if the disease under study
differentially alters airway structure or loss
(e.g., COPD) (146). Table 5 shows some
objective approaches to selecting
hierarchically “similar” airways. No
approach is perfect, as anatomical variations
within and between individuals canintroduce errors in selection. Multiple
approaches may increase the robustness of
airway structure–function inferences.
Automatic labeling of the airway tree on thebasis of segmental nomenclature has beenused (147). Measurement of spatiallymatched airways has revealed that airway
walls are generally thinner rather thanthicker in COPD (146).

Common airway metrics include crosssectional luminal diameter and area, wall
thickness, wall area as a percentage of total
airway area, and an estimated perimeter of ahypothetical “normalized” airway with aninternal perimeter of, for example, 10 or
15 mm (148–152) derived from regression
equations between the square root of airway
wall area and its internal perimeter on thebasis of certain assumptions (Table 5).
Airway centerlines may be segmented and abranch-growing algorithm applied togenerate distal lobe-ﬁlling airways (153). The
resulting airway geometry, CT-derived tissue
density, lung volumes, and pulmonary
function test results may be combined in afull-scale airway network ﬂow model tosimulate whole-lung ventilation (154),
correlated with in vivo HP 129Xe MRI and99mtechnetium-labeled diethylenetriamine
pentaacetate (DTPA) aerosol SPECT
ventilation imaging in COPD (155).
The thinner airway walls in COPD
(146, 156, 157) are collapsible (158).
Expiratory central airway collapse may bemeasured on paired inspiratory–expiratory
scans from the minor-axis diameter or crosssectional area at selected anatomical levels.
There is normally a wide range of age- andsex-dependent expiratory tracheal collapse(159, 160). Airway segmental mucus plugs
have been visually counted, correlating withpulmonary function, oxygen saturation, MRIderived ventilation heterogeneity (161–163),
and locally reduced 3He MRI signal (164).
Caveats: Static imaging may underestimate tracheobronchial collapse and notpredict dynamic expiratory collapse(158–160, 165). CT-derived metrics forlung densitometry and airway structure
may be influenced by age, height, TLC,
gender, and smoking (82, 150, 166, 167).
Sufficient airways should be measured toensure an acceptable range of error (168).

Assessing the Vasculature
The human pulmonary vasculaturecomprises .23 branching generations frommain pulmonary artery (PA) to alveolar
capillaries, with only the ﬁrst few generations
being routinely visible. Standard CT using
energy-integrating detectors (in-plane pixel
dimension 0.4 mm) limits the ability toaccurately evaluate spatial properties ofvessels that approach the voxel size(e.g., 1 mm) (169). A 0.2-mm in-plane

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Table 5. Common approaches in matching airways for comparative analysis
Selection Criteria
Anatomical property

Branching hierarchy

Distance from landmark
Airway dimension

Airways of a stipulated
inner perimeter (PiX)

Example of Metric

Considerations

Designation of “Terminal bronchiole”

Pathology may alter airway status(e.g., if the distalmost conducting airways are destroyed,
the airway labeled as terminal bronchiole
may appear to “migrate” centrally)
Designating a generation
There are anatomical variations in branching pattern;number for each airway
certain nomenclature systems do not cover the entire
airway tree
Path distance (e.g., 5 cm from carina or pleura) Body and lung size variations
Lumen size (e.g., 2-mm-diameter airways)
Risk of bias when pathology alters airway dimension(e.g., COPD may narrow airway lumens;
therefore, 2-mm airways may occur at a different
generation compared with subjects without COPD)
Wall thickness of airways
Regression assumptions should be confirmed inwith inner perimeter of 10 mm (Pi10)
the condition(s) under study; potential effects ofoutliers on regression equation should be assessed

Definition of abbreviations: COPD = chronic obstructive pulmonary disease; PiX = predicted wall thickness of airways with inner perimeter of X;
Pi10 = predicted wall thickness of airways with inner perimeter of 10 mm.

pixel dimension has been obtained using aphoton-counting scanner with ultrahighresolution matrix (170); even with this, mostof the vasculature is not directly imaged.
Micro-CT uses a small-bore compact
scanner to image only a small volume oftissue (typical ﬁeld of view 16 mm vs.
36 cm for conventional CT), yielding aspatial resolution of ,0.01 mm, commonlyused for ex vivo studies (42, 43, 171) and forin vivo studies in small animals (172).
Vessel Density, Tortuosity, andFractal Dimension

Extraction of pulmonary vascular data fromimaging data sets can be automated or
semiautomated (cf. Reference 173). Arteries
and veins may be automatically mapped,
peripherally matched, and classiﬁed (174).
Loss of pulmonary vascular density or distal
PA blood volume is associated with rightheart failure indices in smokers (175),
emphysema progression (176), and asthma
severity (177).
Besides density, vascular branchingpattern is an important determinant ofregional perfusion and function. Vessel
tortuosity can be determined by comparing
the linear distance between two points on avessel with the actual 3D path length. This
“distance metric” is increased in patientswith pulmonary arterial hypertension (PAH)(178) and is correlated with mean PA
pressure, pulmonary vascular resistance, andarterial and venous oxygen content andsaturation. The fractal dimension, an index
of the complexity of iteratively branching
American Thoracic Society Documents

space-ﬁlling structures, may be determinedby dividing the lung into cubes of equal size,
whereupon all cubes containing a vessel are
noted; the process is repeated using cubes ofvarying sizes down to a voxel. The numberof vessel-containing cubes is related to cube
size on a double logarithmic plot; fractal
dimension is the slope of the linear portionof the plot (178). Alternately, fractal
dimension can be estimated from CT or
MRI perfusion scans (179) by calculating therelative dispersion (standard deviation/mean) of the image in progressively smaller
blocks and evaluating the slope of therelationship between log relative dispersion
versus log number of voxels averaged.
See References (179 and 180) for details.
Pulmonary Artery (PA) Size andDistensibility

Central PA enlargement measured by
CT (181) is commonly associated with, but
not necessarily predictive of, PAH (182–184).
There are insufﬁcient data showing theclinical signiﬁcance of PA dilation orwhether PA size decreases with PAH
treatment or if a decrease improves outcome(184). MRI can image PAs at speciﬁc times
throughout the cardiac cycle; vessel crosssectional area will change because ofdifferential systole-to-diastole pressures;
this change yields a measure of mean PA
distensibility (mPAD), which can be
determined without direct pressure
measurements. mPAD can differentiate
PAH from normal (185) and therapeutic
responders from nonresponders (186) andcan predict mortality among individuals with
PAH (187).
Large-Vessel Blood Flow

Speciﬁc velocity-sensitive MRI sequences cancharacterize PA forward ﬂow, retrograde
ﬂow, average ﬂow velocity, and peak ﬂow
velocity. These metrics correlate withinvasive measures of arterial pressure andvascular resistance (188).
Caveats: Quantification may be limited
by ability to resolve distal lung structure(e.g., capillaries from larger vessels) (189).
The spatial resolution of MRI is less thanthat of CT; thus, imaging of vascular trees
is limited to visualized larger vessels.

Assessing the Pleura
Imaging metrics of pleural disease (190)include pleural thickness, percentage
circumferential pleural involvement, and thenumber of foci of rounded atelectasis at
anatomically deﬁned levels (191). The
presence and volume of pleural plaquesquantiﬁed by CT have not shown signiﬁcantassociation with indices of functional
impairment (192, 193). Ultrasound (194) andtransesophageal echocardiography (195)
have been used to semiquantitatively assess
pleural effusion volume (191).
Potential Caveats: It may be challenging
to delineate indistinct pleural boundaries
or reliably distinguish pleural fluid fromthickening or fibrosis.

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AMERICAN THORACIC SOCIETY DOCUMENTS
Assessing Regional
Lung Mechanics
General Considerations

Metrics of lung mechanics reﬂect gas or
blood transport into or through a structuralcompartment, calculated from the ratios of
two primary measurements: 1) elastance (orstiffness), the ratio of an applied pressure
to a resulting increase in volume, (DP/DV),and 2) resistance, the ratio of a pressure
difference to the gas or blood ﬂow between_ Elastance andtwo points (DP/DQ).
resistance are combined into a generalfunction of impedance when the lung can beapproximated as a linear dynamic system
and into multiterm descriptions when
linearity assumption is not applicable.
Whole-lung mechanics may be assessedfrom the pressure drop across the lung fromthe airway opening (trachea or mouth) to thepleural surface (often approximated byesophageal pressure). Inaccessibility of
individual lung compartments impedes thedetermination of regional lung mechanics,
but advanced imaging modalities canprovide estimates of regional gas volumes
and ﬂows during ventilation; if regionalpressures are also known, then local stiffness
and resistance can be calculated (although
not always possible to match rapid changes
in local parameters under dynamic
conditions). The difﬁculty of directly
measuring local gas or blood pressureshinders validation of imaging-derived
regional mechanics when these local pressuremust be inferred from mathematical models
on the basis of remote pressure
measurements (196).
Measurement Principles and Methods

Imaging coupled with dynamic pressure,ﬂow, and volume measurements enhances
information yield about regional lungelastance, resistance, impedance, linear strain,
and shear. Most approaches are based onrelationships between gas ﬂow and pressure
at an appropriate entry point (197). The
retrograde catheter technique (198) allows
measurement of the resistance of an airway
segment distal to a wedged catheter. An
equivalent approach, wedging the tip of abronchoscope into the lung, has been used inhuman subjects (199). Acinar-scale regionallung mechanics have been measured inanimals by applying ﬂow oscillations throughan alveolar capsule to determine input
impedance at the pleural surface (200).
Invasive tracking of 3D movements of174

implanted radiopaque markers in the lung as
inﬂation pressure changes has determinedvariations in regional lung compliance at
centimeter resolution (201). Nonrigid
coregistration of landmarks in paired CT
or MR images obtained at different lungvolumes allows quantitative mappingof regional parenchymal deformation(strain, shear) (202–204) (Figure 6).
Elastography quantiﬁes parenchymal stiffnessby shear-wave propagation through the lungand tracking landmarks within highresolution 3D images. This approach assumes
a uniform pressure ﬁeld; this limitation canbe partially avoided by measuring regionalpressures using miniature catheter-tip
pressure transducers. 1H MR elastographynoninvasively measures shear stiffness duringrespiration in human lung (205) and in ILD(206). Ultrasound surface elastography could
quantify subpleural shear stiffness as a marker
of ﬁbrosis and in staging ILD (207). Dynamic(four-dimensional) CT-based lungdeformation analysis and elastography havemapped lung elasticity distribution in patientswith COPD and lung cancer (208). Even
without regional pleural or elastic
recoil pressure, it is possible to estimate
local air pressures using an anatomicallyaccurate imaging-derived computationalmodel of the lung on the basis of the laws ofphysics. For example, one can model gas
pressures at any point in the airway tree
assuming a standard ﬂow regime along a
network of cylindrical conduits (209, 210)
or by applying computational ﬂuid
dynamics (211).
Caveats: Validation of imaging-based assessment of regional lung mechanics is challenging because the resolution of imaging
modalities far exceeds that of physical transducers for measuring local pressures, flow,
and volumes. Validating regional volumes
and flow determined by imaging methods
requires invasive measurement of thesevolumes and flow. Acinar-scale regionallung mechanics have been measured byapplying broadband flow oscillations through an alveolar capsule (200); measurements are limited to pleural surface, anddata from deep lung remain elusive.
Complicated image analysis algorithms
are used to derive structural and functional information. There is often
more than one useful way to extract information from an image, with limited
understanding of which algorithm is best

for a given application. The warping algorithms for transforming one image intoanother (e.g., at different lung volumes)(212) have been adapted from their application in other organs (e.g., brain) but
remain unvalidated for the much greater
deformation experienced by the breathinglung.
Without experimental gold standards formeasuring regional lung mechanics independent of imaging, validation relies on
predictions from computational models
grounded in physics (See section “Applying
Imaging Metrics in Computational Modeling to Predict Lung Function”); such models can be structurally accurate down to a
submillimeter scale (213). Fluid dynamics
enables pressure estimation throughout the
airway tree, while well-studied empiricalequations can estimate how parenchyma
structures affect pressures. Nevertheless,using models to validate imaging-derived
physiological conclusions is inferential andremains an open issue.

Assessing Ventilation andInhaled Gas Distribution
General Principles

Alveolar ventilation, V_ A, is the rate of freshgas delivery to the alveolar space (the dot
denotes a rate). The term “speciﬁc
ventilation” is used differently between
methodologies, with some including a timecomponent and some not. Thus, units ofmeasurement should be stated. In MRI andnitrogen washout studies, speciﬁc ventilation(SV) is deﬁned as the fraction of the original
regional lung gas volume at end-expiration(Vr) that is replaced by a volume of fresh air
or tracer gas (Vf) with each breath:
SV5

Vf
Vr

(1)

SV is used to describe overall ventilation
heterogeneity (214) in multiple-breath washout
studies; this principle is also used in someimaging techniques (215, 216). Alternatively,
fractional ventilation, a related volume
measure, is sometimes used to measure thefraction of end-inspiratory lung volume thatis made up of fresh air (or tracer gas):
Fractional ventilation5

Vf
SV
(2)
5
Vr 1Vf 11SV

V_ A is a metric of primary importancefor gas exchange. SV and fractional

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AMERICAN THORACIC SOCIETY DOCUMENTS
SUPINE

A

PRONE

PRE
L R

R

POST
A

L

R

L R

PRE
A

L

P

R

LR

POST

L

P

R

LR

P

L

A

P

A

B
y

x

y
z

x

x

y
z

z

FTV
0.30

FTV
0.30

0.15

0.15

0.00

0.00

C

Air volume (L·kg–1)

D

0.08

L cranial
L middle
L caudalR cranial
R middle
R caudalR infracardiac

0.008

0.12

0.006

0.09

0.004

0.06

0.02

0.002

0.03

0.00

0.000

0.06
0.04

E

Tissue volume (L·kg–1)

F

FTV

Cs, L·(cm H2O·L)–1

0.05
0.04
0.03
0.02

Supine Prone Supine Prone
PRE

G

PRE

SUPINEPOST
PRONE

SUPINE

x

PRONE

PRE

SUPINEPOST
PRONE

SUPINExz

Supine Prone Supine Prone

POST

I

StrainPREyzSupine Prone Supine Prone

POST

H

Displacement
PRE

0.00

0.00
Supine Prone Supine Prone

POST

0.01

PRE

POST

Maximum principal strain
1.0

PRONE

y

0.8
0.6

x

x

y

0.4

y

z

z

0.2
0.0
SupineProne

Supine

PRE

J

Prone

POST

Shear

0.6

0.4

0.2

0.0

–0.2
SupineDisplacement (mm)
0

20

40

60

Strain
0.0

0.2

0.4

Prone

PRE

Supine

Prone

POST

Figure 6. Computed tomography–derived metrics of regional parenchymal mechanics obtained during lung inflation in an adult canine model
illustrate the use of quantitative imaging to evaluate the effects of posture and compensatory lung growth and remodeling before (PRE) and4 months after (POST) right pneumonectomy (PNX, removing 58% of lung units). (A) Two-dimensional images. (B) Three-dimensional
rendering of lungs. Color scale shows lung tissue density (fractional tissue volume [FTV]). (C–E) Air volume (C), tissue volume (D), and FTV
measured at a transpulmonary pressure (Ptp) of 30 cm H2O (E). (F) Specific compliance (Cs) of individual lobes (seven PRE-PNX, three POSTPNX) calculated between Ptp values of 10 and 30 cm H2O. (G and H) Displacement (G) and linear strain (H) vector maps were derived via
coregistration of images obtained at the two values of Ptp, shown in three-dimensional renderings (upper) and two-dimensional (lower) images.

American Thoracic Society Documents

175

AMERICAN THORACIC SOCIETY DOCUMENTS
ventilation can be converted to V_ A using
respiratory rate and lung density to estimate Vr.
PETAssessment of regional ventilation by
PET uses the kinetics of nitrogen13–labeled nitrogen (13NN) (217, 218),
neon-19 (19Ne) (219), or gallium-68(68Ga)–labeled nanoparticles (“Galligas”)(220). Production and optimization ofradiolabels for ventilation and perfusion in
PET and CT are reviewed elsewhere (221).
Measurements are based on the time
constant of washout curves after intravenous
tracer injection or gas equilibration,_
providing regional speciﬁc ventilation, sV,
which combined with measurementof alveolar volume (resident gas) yields
absolute V_ A (222). Exploration of themultiexponential composition of washout
curves allows assessment of ventilation
heterogeneities below the voxel resolutionthat are key to quantifying ventilation
dysfunction during severe bronchoconstriction(223) and mechanical ventilation (224).
CT, PET–CT, and SPECT–CT havedocumented heterogeneous deposition of
inhaled aerosols and particulates (225–229),
validated experimentally against ﬂuorescent
microspheres (230). Imaging data may be
combined with theoretical modeling (231) tosimulate physiological conditions, examine
the factors causing variability, and predictin vivo response.
MRI

Distribution of ventilation may be assessedusing proton-based 1H MRI and direct
imaging of exogenous gases using
multinuclear MRI and spectroscopy (Table 6,
including comparison with PET).
Imaging regional lung expansion.

Using 1H MRI with UTE (echo times<0.2 ms) or fast gradient-echo techniques(echo times 1 ms), lung density can bequantiﬁed and normalized to nearby tissue
with similar T1 and/or T2 characteristics(e.g., muscle [232–234]), using reference

phantoms (235), very low ﬂip angles, orsome combination of these. This allows
measurement of changes in macroscopic
density during the respiratory cycle using
deformable image registration techniques.
Density changes provide a proxy measure of
regional ventilation, with modest
correlations to fractional ventilation
measured by HP gas inhalation (236, 237).
1
H MRI measurements are relatively simple
using standard hardware, without any needfor tracer gas and with potential for 3D
acquisitions.
HP gas techniques. Using inhaled HP
129
Xe or 3He MRI, gas distribution
abnormalities during static breath-holdimaging can be assessed by the ventilation
defect percentage (VDP). VDP is the total
volume of lung with signal intensity below a
threshold (typically a small percentage of
average signal intensity) normalized tothoracic cavity volume (238). VDP derived
from 3He or 129Xe is a safe and reproducible
measure correlating with clinical diseasemeasures (239–241). In COPD, 3He VDP
correlates with spirometry (242), symptoms,
exercise capacity (128), CT-based measuresof emphysema, and clinical exacerbations(243, 244). 129Xe MRI has also demonstrated
similar correlations with spirometry (245).
Ventilation defects are spatially related to gas
trapping and emphysema measured using
CT (246). Bias between VDP measured with
3
He and 129Xe (242, 247, 248) MRI are likely
due to the density and viscosity of each gas,
resulting in different ﬂow dynamics indifferent caliber airways (249), but bothdemonstrate higher sensitivity to mild
obstruction than spirometry (250, 251).
Oxygen-enhanced MRI, Fourier
decomposition, and fluorine imaging.

Oxygen-enhanced (OE) MRI and Fourier
decomposition (FD) MRI are performed
during tidal breathing. FD MRI (252)
depends on the periodic 1H MRI signal
intensity ﬂuctuations that accompany
changes in local lung parenchymal densitywith respiration and pulsatile blood ﬂow.

OE MRI (253, 254) exploits the paramagnetic
effect of molecular oxygen: as O2 diffuses
into tissues and blood, local MR signals arealtered because local T1 (longitudinalrelaxation time) shortens as local dissolved
oxygen concentration rises (255). SV
imaging (SVI) (215, 256) is an OE methodthat uses the rate of change in T1, which
depends on the rate of wash-in/washout of
inhaled oxygen to a region to directly
measure regional SV. Recent advances in OE
MRI (257–260) have improved the spatial
resolution and repeatability of
semiquantitative estimates of VDP (258) andquantitative regional SV maps (215, 216),
supporting translation in a wide range oflung diseases. OE MRI and SVI metrics arestrongly correlated with pulmonary function
standards (e.g., multibreath nitrogen
washout [256]) and with HP 3He MRI(261, 262).
Alternative approaches using tracers(e.g., perﬂuorinated gases) allow direct
measurement of MR signal from multiple
19
F nuclei in each atom of tracer gas (263).
This approach can capture dynamic airspaceﬁlling during tidal breathing (263–265),
providing voxel-wise measures of SV(Equation 1). Relative to HP 3He MRI, 19F
imaging is inexpensive because the techniquedoes not use hyperpolarization.
Caveats: Signal-to-noise ratio and spatiotemporal resolution are limited. Most
methods depict the distribution of an
inhaled tracer gas that is proportional to
SV (acquired during a breath hold) or
reflect information averaged over multiple
respiratory cycles (e.g., steady-state tidal
breathing). In single-breath tracer inhalation techniques, a “ventilation defect” does
not necessarily represent absent ventilation,
as low-signal regions may reflect poorly
ventilated regions (slow time constants)that receive less inhaled gas but are nottrue ventilation defects. Imaging a tracer(oxygen or 19F) over multiple breaths
requires long scan times (10–20 min)and increased postprocessing complexity.

Figure 6. (Continued ). (I and J) Maximum principal strain (MPS) (I) and shear (in the sagittal plane) (J) of each lobe. Data are mean 6 standard
deviation. With respect to postural effects, at PRE-PNX baseline, prone posture is associated with higher FTV in dependent regions (B), lower
Cs, displacement, and strain magnitudes in most lobes, especially the caudal lobes (F–I), and lower interlobar heterogeneity in Cs (F), MPS (I),and shear (J) but selectively increased lung strain in a small region under the heart (H) compared with prone posture. With respect to PNX
effects, POST-PNX mediastinal shift and expansion of the remaining lobes (A and B) stimulated acinar tissue growth and doubled air and tissue
volumes (C and D) without altering FTV (E), increased parenchymal excursion (G), and altered intralobar regional strain distribution (H) during
lung inflation compared with the corresponding PRE-PNX baseline. Compensatory lung growth and concurrent parenchymal remodeling nearlynormalized average lobar Cs (F), MPS (I), and shear (J) and preserved the normal postural response patterns. Adapted by permission from
Reference 203. A = anterior; L = left; P = posterior; R = right.

176

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AMERICAN THORACIC SOCIETY DOCUMENTS
Table 6. Comparison of different techniques to measure ventilation and its heterogeneity
HP 3He MRI
Signal-to-noise ratio
Breath hold vs.
free breathing
Relative cost
Hardware requirement
IND exemption required
Scan length
Major metrics

Multicenter repeatability
established
Advantages

Disadvantages

HP 129Xe MRI

19

F MRI

HigherBreath holdHigherBreath hold

Lower
Both

Very highHigh
YesSeconds
Ventilated volume,
VDP

MediumHigh
Yes
Seconds
Ventilated volume,
VDP

MediumMediumYes
Seconds to minutes
VDP, fractional
ventilation

Yes

No

No

13

OE MRI
Lower
Free or controlled
breathing
Low
Low
No
10–20 min
Percentage signal
enhancement, VDP,
specific ventilation,
DPO2
No

NN PET

HigherFree breathing
High
High
Yes
Minutes
Ventilated volume, VDP,
specific ventilation

No

Regional measures of Regional measures of Regional measures of Can be performed
Regional measure of gas
emphysema/gas
using conventional
emphysema/gas
emphysema/gas
trapping
trapping and airway
coils without
trapping and airway
trapping and airway
Provides direct assessment
occlusion
additional gas
occlusion have highocclusion have highof regional alveolar
preparation/handlingsensitivity forsensitivity forventilation
hardware measures Validated in conjunction
longitudinal andlongitudinal andof heterogeneityinterventional
interventionalwith perfusion against
validated against
assessments
assessments
arterial blood gases
multiple breath
Ability to image
washout, andgaseous andagainst HP 3He MRI
dissolved phases
enables assessmentof gas exchange
Requires multinuclear, Requires multinuclear, Requires multinuclear, Typically long
Radiation exposure
dedicatedacquisition times
Requires several breaths to
dedicated
dedicatedtransmit/receive coil,estimate subvoxel
transmit/receive coiltransmit/receive coil,specialized
ventilation heterogeneity
and specialized
specialized3
sequences, and He
from 13NN-washout
sequences
sequences, and129
Xe hyperpolarizer
hyperpolarizer
curve

Definition of abbreviations: DPO2 = change in partial pressure of oxygen; HP = hyperpolarized; IND = investigational new device; MRI = magnetic
resonance imaging; OE = oxygen-enhanced; PET = positron emission tomography; VDP = ventilation defect percentage.

_ is the process of delivering
Perfusion (Q)blood to the capillary bed, which, coupledwith V_ A, determines pulmonary gas exchange.
Different techniques are shown in Table 7.

minimize potential errors in coregistering
paired images; this technique clearlyvisualizes regional defects (268).
Alternatively, DECT evaluates PBV after
iodinated contrast injection (269–271) using
two X-ray beams of different energies orspectral detectors and material
decomposition to visualize iodine
distribution that exhibits differential
attenuation properties at different energies(272, 273). DECT correlates well with ﬁrstpass kinetics (274) and detects regional
defects with higher sensitivity thanconventional angiography (68, 275). A lower
dose dynamic CT technique that measurespulmonary perfusion using only two serialscans has been validated against ﬂuorescent
microspheres in animals (276).

CT

PET

Intravenously injected iodine is used inconventional CT to measure regional PBV asa proxy for perfusion by subtracting
unenhanced precontrast images fromcorresponding postcontrast images(266, 267). Motion correction is applied to

PET assessment of regional perfusion isbased on the kinetics of tracers: 13NN,
oxygen-15–labeled water (15O-H2O),
or gallium-68 (68Ga)–labeled particles/
compounds. Because of its low blood–gas
partition coefﬁcient, intravenously injected

Techniques that rely on imaging of lungexpansion to infer ventilation are criticallydependent on accurate registration.
Regional lung expansion rather than true
ventilation is measured (e.g., gas-trapped
regions may expand with inspiration,
reducing local density even in the absenceof any gas influx, introducing error).

AssessingPulmonary Perfusion

American Thoracic Society Documents

13

NN reaching lung capillaries mostly
diffuses into alveoli, allowing assessmentof true capillary perfusion (222, 277).
Main 13NN-based approaches are as
follows:
1. Continuous 13NN infusion, where
perfusion is derived as
V_ A
_
Q5
_
_VA=Q

(3)

while ventilation is computed frominhalation of neon-19 gas (278), and2. 13NN-saline bolus: perfusion iscomputed from the distribution of an
injected bolus of 13NN-saline during abreath hold (218, 277), where theend–of–breath hold activity
characterizes capillary perfusion specificto aerated regions.
The 15O-H2O–based method measures
perfusion from local kinetics of injected 15OH2O, accounting for the input function into
pulmonary circulation and water extraction
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AMERICAN THORACIC SOCIETY DOCUMENTS
Table 7. Comparison of different techniques to measure perfusion and its heterogeneity
CT13

NN PET

SPECT

ASL MRI

Spatial resolution
Cost
Breath hold vs.
free breathing
Scan length
Major metrics

HighestRelatively low
Breath holdModerate
Relatively highBreath hold

Low
Low
Both

Seconds
Perfused blood
volumePerfusion defects

Several minutes
Relative perfusion,
perfusion defects

Validation

Validated against
microspheres inanimal model

Advantages

High spatial
resolution
Widely available
and in use

Few minutes
Regional shunt (for earlypeak in kinetics)
Perfusion (ml/min/ml)
Relative dispersion
Length-scale perfusion
heterogeneity
Noise-free estimate of
perfusion heterogeneity
Combined with ventilation to_ validated
give V_ A/Qagainst systemic
experimentally assessedarterial blood gases and
MIGETHighly sensitive
Allows quantification ofgases directly involved inpulmonary gas exchange
Coregistration with othertracers allows relevant
association of molecularand functional information
Short tracer half-life allows
sequential acquisition of
measurements
Enables quantification ofheterogeneity underlying
the imaging resolution

Disadvantages

Relatively highexposure toionizing radiation
Requires injection ofiodinated contrast
Relationship between
perfused blood
volume andperfusion notestablished
Conduit vessel
signal requires
removal or
separation toassess true
perfusion

Exposure to ionizing
radiation
May require a stablebreathing pattern, which
may not be easily
followed byspontaneously breathing
subjects
Requires cyclotron to be
immediately available
Requires kinetics analysis(for improved accuracy)and does not identify thelocation of heterogeneity
within the voxel
Low temporal resolution
may underestimate
perfusion
Quality of segmentation isimportant, as it cannot
uncouple shunt fromlarge blood vessels
Requires several breaths for
estimation of alveolar_
ventilation and V_ A/Q
ratios as derived fromwashout curve

Combined withventilation to give
_ validated
V_ A/Qagainst MIGET

Widely available,
used clinically

Exposure to ionizing
radiation
Semiquantitative as
typically
implemented

Bolus Contrast
Enhancement MRI

High
Relatively highFree or controlled
breathing
Minutes
Perfusion (ml/min/ml)
Perfusion (ml/min/g)
Relative dispersion, fractal
dimension
Gravitational gradientsHigh
Less than ASL
Breath hold

Validated in phantoms
and against
microspheres
Combined with ventilation_ validated
to give V_ A/Qagainst MIGET
Noninvasive and no“dose”
Combined with density
imaging, provides
insight into effects oflung deformation
Temporal imaging
possibleSensitive to modest
physiologicalinterventions and lungdisease (e.g., PAH)
Uses a standard 1.5-Tclinical scanner without
hardware modification
Low proton density andshort
T2* make SNR an
issue
Currently single slice,
making whole-lung
imaging more time
consuming
Absolute calibration of
signal possible but
requires postprocessing
and reference
phantoms
Conduit vessel signal
requires removal or
separation
Requires cardiac gating
and reliable lungvolume control (orpostprocessing forregistration)
Translation to 3-T ischallenging (butpossible)
Data processing is
cumbersome

Limited validation in
cerebrum and inphantoms

Seconds
Time to peak contrast
enhancement (transittime), blood volume,
perfusion defects,
relative perfusion

Uses a standard 1.5-Tclinical scanner without
hardware modification
Widely available

Requires contrast
injection, not suitable
for some subjects (e.g.,
impaired renal function)
Indirect measure of
perfusion
Semiquantitative as
typically implemented
Accurate estimation
requires arterial input
function, which is rarely
available
Conduit vessel signal
requires removal or
separation to assesstrue perfusion

Definition of abbreviations: ASL = arterial spin labeling; CT = computed tomography; MIGET = multiple–inert gas elimination technique;
MRI = magnetic resonance imaging; PAH = pulmonary arterial hypertension; PET = positron emission tomography; SNR = signal-to-noise ratio;_ = ventilation-perfusion ratio.
SPECT = single-photon emission computed tomography; V_ A/Q

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AMERICAN THORACIC SOCIETY DOCUMENTS
6
5
4
3
2
1
0
Figure 7. Sagittal images showing an arterial spin labeling experiment in the lung to quantify
pulmonary perfusion in a normal subject. Images are shown in the orientation of acquisition tovisualize the large-scale effects of gravity on the lung. In the left-hand “tag” image, a nonselective
inversion pulse has been applied, and the intravascular signal is largely nulled. In the middle“control” image, a selective inversion pulse has resulted in intravascular signal appearing bright;
lobar fissures can be faintly seen because vessels do not cross lobar boundaries. Subtraction of
the tag and control images results in a map of delivery of one systolic ejection of blood after
quantification (right), showing greater perfusion in the gravitationally dependent lung. The color
scale is blood delivered (ml/min/cm3). The bright structures on the anterior chest wall arephantoms used in quantification. Courtesy of S.R. Hopkins.

by the lung (279–281). In addition,
pulmonary perfusion has been measured
with intravenously injected positronemitting 68Ga microspheres (282, 283) that
lodge in pulmonary capillaries in proportion
to local perfusion. 68Ga-chelated 1,4,7,10tetraazacyclododecane-1,4,7,10-tetraacetic
acid (DOTA)–measured perfusion agrees
reasonably with measurements using
ﬂuorescent microspheres (284).
Surrogate PET-based methods using
intravenous ﬂuorine-18–labeled
ﬂuorodeoxyglucose (18F-FDG) can assess
pulmonary perfusion in regions of interest
larger than a pixel. The fraction of bloodobtained from 18F-FDG kinetics approximates
15
O-H2O perfusion measurements in normal
and post–acute lung injury large animals(285). Relative perfusion is estimated byaccounting for delays in 18F-FDG transport
between plasma sampling site and lungregions of interest; this method correlates
highly with previously validated 13NN
perfusion measurements (286). These positive
results match previous good correlations
found between PBV measured using labeled
carbon monoxide and lung perfusion (287).
Single-photon Emission Computed
Tomography (SPECT)SPECT measures pulmonary perfusion using
injected 99mtechnetium- or 113mindiumlabeled macroaggregated albumin (MAA)(288–290) (See section “Metrics forAssessing Regional Rentilation-perfusion
Mismatch”).
American Thoracic Society Documents

MRI

Two major MRI methods are used to evaluate
lung perfusion. 1) A bolus of gadolinium, an
injectable contrast agent, is used to enhance
MR signal during its passage. Indicator
dilution techniques are applied (291) toderive perfusion from the time to peak signal
intensity (reﬂecting transit time) and the area
under the time–activity curve (reﬂecting
blood volume) (291, 292), using the central
volume principle (293). 2) The 2D arterial
spin labeling (ASL) ﬂow-sensitive alternating
inversion recovery sequence (FAIRER)(294–297) uses magnetic ﬁeld gradients andradiofrequency pulses to manipulate thesignal of blood delivered to an imaged slice indifferent ways: one image with delivered
blood generating a strong signal and one inwhich the signal is largely nulled (Figure 7).
Two electrocardiogram-gated images of eachslice are taken 5 seconds apart. The signal ina voxel of the subtracted image is
proportional to the amount of blooddelivered during the previous cardiac cycle;
multiple slices are acquired sequentially tocover the entire lung. These techniques
have been validated in animal models (298)and phantoms (299, 300) or against themultiple inert gas elimination technique(MIGET) (301).
Perfusion Metrics

Perfusion measurements may be expressed
as relative perfusion (i.e., a fraction of total
perfusion) or absolute perfusion (i.e., thevolume of blood delivered per unit volume

or mass per unit time); the metric usedshould be clearly stated. Perfusion
heterogeneity is affected by gravity, vascularbranching structure, active regulation(e.g., hypoxic pulmonary vasoconstriction),and disease (302). The simplest measure of
spatial heterogeneity is the relative dispersion(RD, or coefﬁcient of variation) of an image(standard deviation divided by average signal
intensity). RD is independent of absolutesignal quantiﬁcation, simple to calculate,and sensitive to physiological interventions(179, 303–306). Descriptors that account for
signal distribution (e.g., log normality) may
require absolute quantiﬁcation, adding
complexity (295). Additional metrics include
vertical perfusion gradients (12, 307, 308),
the noise-free estimate of perfusion
heterogeneity (309), and the contribution of
speciﬁc ranges of length scales to perfusion
heterogeneity (310, 311) and the fractal
dimension (179, 180). Studies using ASL
measures of perfusion or contrast
enhancement were used to show increased
perfusion heterogeneity in hypoxia, suggesting
uneven hypoxic pulmonary vasoconstriction(312, 313), in individuals with a history ofhigh-altitude pulmonary edema. RD is also
elevated in patients with Fontan circulation
and PAH (314). Another example is theﬁnding that the length scale of perfusion
heterogeneity differs between normal subjects
and patients with COPD (310) and between
patients with exercise-induced PAH andcontrol subjects at rest (308).
Pulmonary ASL is well suited forstudying temporal perfusion variations; thetechnique does not use injected contrast or
ionizing radiation allowing serial image
acquisition. Using a free-breathing scanning
protocol consisting of a series ofelectrocardiogram-gated scans (315), an ASL
series can be acquired and registered andtemporal perfusion ﬂuctuations calculated(316, 317). Results demonstrate active normal
control of pulmonary perfusion (316–318)
and marked alterations in PAH (319).
Caveats: Tissue deformation with gravity
creates an apparent gradient in alveolar–
capillary perfusion per unit volume evenwhen perfusion per alveolus is uniform (5).
Lung volume during image acquisition
affects quantification (11, 320, 321) andperfusion distribution. Depending on the
study, it is important to distinguish between
perfusion per unit lung volume and perfusion per alveolus. Density measure provides
a surrogate for local deformation, and179

Fraction of Total Pixels

AMERICAN THORACIC SOCIETY DOCUMENTS

Ventilation
SNR: 11.40
Mean: 0.52
Median: 0.53
SD: 0.22

0.06
0.04

Fraction of Total Pixels

IPF

Ref

RBC:
Barrier0.230

0.60±0.07

Inflation

2.35L

3.16±0.27

Defect

8%

3±2%

Low

25%

13±6%

High

10%

16±4%

Defect<1%<1±<1%

Low

<1%

13±7%

High

68%

<1±<1%

Defect

6%

3±1%

Low

20%

13±5%

High

9%

15±8%

0.02

0.0

0.5

1.0

Barrier
SNR: 16.20
Negative: <1%
Mean: 1.46
Median: 1.48
SD: 0.28

0.15
0.10
0.05

0.0
Fraction of Total Pixels

Subject

1.0

2.0

2.5

0.10
RBC
SNR: 3.99
Negative: 3%
Mean: 0.38
Median: 0.37
SD: 0.23

0.05

0.0

0.5

1.0

1.2

Intensity

Figure 8. Representative report showing the use of 129Xe magnetic resonance imaging to evaluate single-breath gas distribution and gas
exchange in a patient with idiopathic pulmonary fibrosis (IPF). The report provides histograms (left), color-coded maps (middle), andquantitative metrics (right) related to ventilation, interstitial barrier status, and gas transfer to capillary red blood cells (RBC). The quantitative
maps assign each voxel a color code depending on how its intensity deviates from the healthy reference population means (histograms on theleft). Red pixels indicate “defects” (pixel intensities more than 2 standard deviations [SDs] below the mean), while green voxels are those fallingwithin 1 SD, and blue represent intensities 2 SDs above the mean (for ventilation and RBC transfer maps). Uniquely, the barrier maps have attheir upper scale pink and purple to denote voxels with 129Xe uptake more than 2 SDs above healthy reference, which has been associated withinterstitial thickening. For each map, the percentage of the thoracic cavity falling within the defect, low, and high ranges is provided, together
with reference values from a young, healthy reference population (Ref, gray cells in the table, dotted curves in histograms). Courtesy of B.
Driehuys. SNR = signal-to-noise ratio.

Airspace

Healthy

CTEPH

IPF
pre-TX

COPD
Alpha-1

COPD
emphysema

COPD
bronchitis
highBarrier

low
high

RBC

low
high

low
Figure 9. Representative 129Xe ventilation and gas exchange magnetic resonance imaging maps from a range of disease conditions. Note thatthe healthy volunteer is characterized by maps falling in the green range of the healthy reference cohort values. The patient with CTEPH exhibits
primarily defects in red blood cell (RBC) transfer. The subject with IPF exhibits high barrier uptake (interpreted as a thickened diffusion barrier),with basilar and peripheral loss of RBC transfer. Patients with COPD all exhibit significant ventilation defects, while two exhibit low barrier uptake(interpreted as emphysema), and all exhibit low RBC transfer. Courtesy of B. Driehuys. COPD = chronic obstructive pulmonary disease;
CTEPH = chronic thromboembolic pulmonary hypertension; IPF = idiopathic pulmonary fibrosis; TX = treatment.

180

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AMERICAN THORACIC SOCIETY DOCUMENTS

Many so-called perfusion scans actuallyreflect blood volume, particularly in larger
vessels; the relationship between blood
volume and flow is not fully established,particularly in disease. DECT or subtraction CT perfusion measures yield onlya snapshot of iodine enhancement; perfusion quantification requires rapid
sequential four-dimensional CT to assess
temporal changes (56, 321, 322), producing high radiation exposure and lower
image resolution.
Although 13NN PET and SPECT provide signal specific to blood in pulmonary capillaries,
other techniques (ASL MRI, CT) comprisesignal from both capillaries and conduit vessels carrying blood destined for elsewhere.
Removal of conduit vessel signals (323–325)
is essential to quantify capillary perfusion
participating in gas exchange.
Perfusion heterogeneity can occur at a large
spatial scale (e.g., gravity dependent) (5, 179,
180, 326) and smaller scales. The metrics are
scale dependent; scale information should bestated when reporting results (180).
Additional metrics are evolving for specificsituations and direct between-techniquecomparisons of the magnitude and spatial
distribution of perfusion. Hybrid cameras(e.g., PET–CT and PET–MRI) advanceaccurate quantification and comparisons,as they incorporate inherent image coregistration. Because of distinct physical principles of measurement, different techniques
are not equivalent; specific applications foreach method remain to be determined.

Apnea

13N Nitrogen Activity

As the lung normally receives the entire cardiac output, it is more important to assess
regional distribution of perfusion than total
flow. For evaluation of gas exchange, theperfusion signal should come from sourcesthat represent capillary perfusion in thevoxel of interest.

interstitial tissues and plasma and transfer topulmonary capillary RBCs. This capability
provides unique windows into obstructive
conditions such as asthma (327, 328), cysticﬁbrosis (329), and COPD (330), while theability to probe air–tissue–blood diffusion issuited for studying interstitial (145) andpulmonary vascular (331) disease.
HP 129Xe MRI can image acinar
diffusion, driven by xenon’s solubility anddiffusion across alveolar septal tissue andplasma and into capillary RBCs (332). Eachof these compartments is associated withunique 129Xe resonant frequencies (333),
which combined with novel MR acquisition
strategies allow 129Xe distribution in each to

be 3D encoded during a breath-hold (334);
signal is constrained to arise primarily fromthe alveolar–capillary gas exchange interface,and not larger vessels (142). 129Xe thus
reports on structure and function at muchsmaller length scales (micrometers) than the
resolution of the images (3–5 mm isotropic).
Additional 129Xe spectroscopic sampling of
the alveolar–capillary unit every 15 ms
provides metrics of blood oxygenation andhemodynamics (335).
HP 129Xe MRI is fast and well tolerated(239, 336), allowing visualization of gas
distribution and yielding information on
ventilation, alveolar barrier integrity, andcapillary RBC transfer. Its noninvasive nature

Washout

Slope = –1/sV̇v Q̇

Area v Q̇ /sV̇

Slope 1 = –1/sV̇1
Area1 v Q̇ 1/sV̇1
13N Nitrogen Activity

correction for regional density provides auseful first-order means to convert between
the two measurements (5).

v Q̇ 1

Slope 2 = –1/sV̇2v Q̇ 2

Area2 v Q̇ 2 /sV̇2
Time
13

N Nitrogen
Infusion

HP 129Xe MRI permits breath-hold imaging
of inhaled gas distribution in airspaces(discussed previously) and its uptake in

Figure 10. 13N-nitrogen tracer kinetics showing the derivation of quantitative measures of specific_ Top: Washout of 13N-nitrogen plotted on a log scaleventilation (sV_ in PET) and perfusion (Q).
showing activity versus time in a voxel exhibiting uniform behavior modeled as a single
compartment. 13N-nitrogen is injected during apnea and delivered to the alveolus in proportion toregional blood flow; thus, the plateau in activity at the time of the first appearance is proportional toregional perfusion in a given voxel. Then, as the subject begins breathing, the tracer in the alveolus_ and the slope of washout is equal to 21/sV.
_ The area
will wash out proportional to regional sV,
13__under the curve (light blue) is proportional to the Q/sV ratio. Bottom: N-nitrogen washout plottedon a log scale showing activity versus time in a voxel exhibiting two-compartment behavior, with
_ During
compartment 1 having high sV_ (rapidly clearing) and compartment 2 having low sV.
washout, compartment 1 clears tracer rapidly, with a steep initial slope (slope 1) of activity versus_ 11Q_ 2) is again reflected in the plateau but apportioned
time. Total blood flow in the voxel (Qbetween the two compartments on the basis of the back-extrapolated point to the onset of washout_ V_ for compartment 1 is shown in light blue and for compartment 2 isin compartment 2. The Q/sshown in gray. Reproduced with permission from Reference 371.

American Thoracic Society Documents

181

Assessing Ventilation andAcinar Air–Tissue–Blood
Diffusion

AMERICAN THORACIC SOCIETY DOCUMENTS
permits repeated scanning. Ongoing
methodological innovation (e.g., novel coils
and polarizer technology) seeks to accelerate
image acquisition (337) and identify newcontrast mechanisms (26).
Metrics
129

Figure 8 illustrates a typical HP Xe MRI
study (338) reporting on gas distribution in
airspaces, barrier tissues and plasma andtransfer to RBCs, and ability to characterizeabnormalities. Figure 9 shows images frompatients with lung diseases. Dynamic 129Xe
spectroscopic sampling of alveolar–capillary
unit is used to characterize RBC frequency
shift and its cardiogenic oscillations, which
are believed to reﬂect capillary oxygenationand capillary blood volume, which may be
used to assess pulmonary hypertension (331).
Caveats: HP 129Xe is a drug/device combination regulated by federal guidelines. Implementation remains expensive,
requiring an MRI scanner with multinuclear capabilities, specific pulse sequences(339), and custom software (340). The field
is just coming to agreement on the mostuseful types of image acquisition or analysis (332, 341). HP 129Xe signal is transient;(218, 279). Because 13NN diffuses only
into aerated lung units, local 13NN
kinetics allow the estimation of not only
_ ratio but also right-to-leftregional V_ A/Q
shunt (222, 345, 346).

imaging is time sensitive and with some
exceptions (342) is limited to single-breathscans. These factors demand excellent
patient coaching and gas delivery practices
to ensure well-defined lung volumes (343),
adequate inhaled 129Xe, and maintenanceof breath hold during imaging. Reproducibility and validation studies are limited.

SPECT

Assessing Regional
Ventilation–Perfusion Mismatch_ a primary
Matching regional V_ A/Q,
determinant of pulmonary gas exchange
efﬁciency, may be assessed using MRI andnuclear medicine techniques.
PET_ distributions using
Estimates of V_ A/Q
PET are based mainly on intravenously
administered 13NN tracer kinetics (222, 277)
(See section “Assessing Pulmonary
Perfusion”) (Figure 10). Continuous infusion
measurements require estimates of mixedvenous 13NN content and alveolar gas (217,344). In the bolus technique during apnea,_ is computed from the area
regional V_ A/Q
under the activity–time curve as steadystate ventilation begins after a breath hold

Quantification of ventilation-perfusion matching in the human lung with proton MRI
0.5
0.4

V̇A = fresp * SV * (1-Density)

0.3

5

0.2

4

0.1

3

0

4

2

Density(g/cm3)

1

3

0
0.5
0.4

V̇A
(ml/min/ml)

2

1

0.3
0.2

5

0.1

4

0

3

SV
(ml/cm3)

0

MRI

The most developed technique to quantify_ matching combines three
regional V_ A/Q
proton MRI sequences (Figure 11): 1) SVI(See section “Assessing Ventilation andInhaled Gas Distribution” above), 2)
perfusionmeasured using ASL (See section“Assessing Pulmonary Perfusion” above),and 3) proton density. Proton density is used
to assess the air compartment of the voxel as(1 2 density). When multiplied by local SV,
this gives the volume of fresh gas/breath andis used to calculate regional V_ A (301, 348).
Alternately, regional partial pressure ofoxygen (PO2) can be quantiﬁed from HP 3He
MRI by acquiring a back-to-back image
series (349–351) and evaluating regionalchanges in signal intensity with time (352);_ ratio is estimated from theregional V_ A/Q
measured PO2 using mass balance equations
for respiratory gases (353).

V̇A /Q̇ ratio

Metrics2
1
0Q̇(ml/min/cm3)

Figure 11. Analysis pathway for the quantification of regional alveolar ventilation/perfusion_ ratio from proton MRI proton density, specific ventilation (SV), and Q_ (arterial spin
(V_ A/Q)labeling) images. Quantified density images are used as a measure of regional gas content
and combined with SV images (representing delivery of fresh gas/resident gas) to provide
an estimate of regional V_ A. The smoothed ventilation image is then divided by the quantified_ image to give a map of regional V_ A/Q_ ratio. The dark tubular structuresand smoothed Q_ and V_ A/Q_ ratio images represent voxels that have signal characteristics
in the ventilation, Q,
consistent with large conduit vessels, which are masked out for calculations because they do_ (301, 348). Courtesy of Rui Carlos Sa . fresp = respiratory frequency;
not represent local Q
MRI = magnetic resonance imaging.

182

SPECT measures ventilation or perfusion
and reconstructs a 3D image (288, 289) after
injecting or inhaling a g-emitting
compound; the photons emitted during
decay are imaged. Pulmonary perfusion
measurements with SPECT typically involve
injection of 99mTc-MAA, 133mIn-MAA(288–290) particles distributed in proportion
to local blood ﬂow and lodged in small
pulmonary arterioles and capillaries.
Ventilation is measured by inhaledaerosolized particles (133xenon or
99m
technetium-labeled diethylenetriamine
pentaacetate) or 99mtechnetium-labeled
nanoparticles (347).

_ heterogeneity can be
Regional V_ A/Q
described with simple functional models
corresponding to a few compartments and a_ distribution (354).
narrow unimodal V_ A/Q
Small-length scale heterogeneity develops indisease (222, 223, 355) and under mechanicalventilation (224), resulting in heterogeneous_ distributions (356, 357) (Figure 12).
V_ A/Q_ matching include
Metrics of V_ A/Q
global heterogeneity, the second moments_ and(log scale) of ventilation (LogSDV)_ ratio_ versus V_ A/Q
perfusion (LogSDQ)
distributions, calculated in a 50-compartment

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AMERICAN THORACIC SOCIETY DOCUMENTS

End of washout

Perfusion
MBq/mL
0.7

1

0.08

0.5

Shunt = 0.000

0.4

0.06

10–1

0.3

0.04

10–2

0.2

–3

MBq/mL
0.70

0.08

0.6

0.06
0.4

0.04

0.2

0.02

0

0
MBq/mL

MBq/mL
0.10

0.8

0.08
0.6
0.06
0.4

0.04

0.2

0.02

0

0
MBq/mL

0.7
0.6
0.5
Broncho0.4
constriction 0.3
0.2
0.1
0

Mean activity within voxel (MBq/mL)

0.10

1
Shunt = 0.006

10–1
0.35
10–2

V̇A or Q̇ (L/min)

MBq/mL
0.8

0

10

0

0

Lung
lavage

0.4

0.02

0.1

Pulmonaryembolism

PET V̇A /Q̇ distributions
0.8

0.10

0.6

NormalTracer kinetics

MBq/mL

10–3

–1

10

0

0.30
Shunt = 0.606

0.15
–2

10

0
MBq/mL
0.10

1

0.5
0.4

Shunt = 0.107

0.3

0.05

0.2
0.1
0

10

–1

0
0

60

120

180

(Time, s)

240

10–2

10

0

102

V̇A /Q̇_ distributions for
Figure 12. Regional perfusion and end-of-washout lung images, tracer kinetics of whole-lung field, and PET–derived V_ A/Q
single examples of a normal sheep and a sheep after pulmonary embolism, saline lung lavage, and bronchoconstriction. Images are
tomographic sections viewed in craniocaudal direction from top to bottom. Animals were prone for normal, bronchoconstriction, and pulmonary
embolism studies and supine for lung lavage study. In supine position, left side in image corresponds to left side in animal. Note different scales
for images. Regions of unperfused lung are seen after embolism. After lung lavage, there is redistribution of perfusion and increase in residual
tracer at end of washout. Early peak and fast drop to plateau in lung lavage tracer kinetics indicate the presence of intrapulmonary shunt. Thereis significant retention of tracer in large areas after bronchoconstriction. Reproduced by permission from Reference 222. PET = positron emission_ = perfusion; V_ A = alveolar ventilation.
tomography; Q

model (301, 348, 354) (from a cumulative_ vs. V_ A/Q_ ratio) binned inplot of V_ or Q
50 equally spaced (log scale) compartments.
_ ratios ,0.005 (shunt) and .100 (deadV_ A/Q
space) are separately calculated. Relative
dispersion and gravitational gradients (348)
may also be assessed.
Validation Approaches

Arterial O2 and CO2 tensions calculated_
from 13NN PET–measured V_ A/Q
distributions are highly correlated witharterial blood gases (222) and are consistent
with data from the MIGET technique innormal, bronchoconstricted, pulmonary
embolism, and pulmonary edema
American Thoracic Society Documents

conditions (222, 223). MRI and SPECT
measurements have been validated against
MIGET (288, 301).
Caveats: Because estimates of V_ A/Q_
ratio in the 13NN-saline bolus technique
are specific to aerated and perfused
regions, estimates of ventilation could beinaccurate in regions of alveolar deadspace (e.g., pulmonary embolism). Thiscan be addressed either by implementing a 13NN-gas inhalation technique(358–360) or introducing additionalterms to the model of 13NN-saline bolus
kinetics. Absolute ventilation at the voxel
level can be computed from the productof regional (ventilation 3 gas volume)assessed by transmission scan or equilibration of inhaled 13NN (222, 359). As
SPECT quantifies relative V_ A and relative perfusion, total V_ A and perfusionmust be separately measured for fullquantification (288).
1
H MRI–derived regional V_ A/Q_ ratio istime consuming, requiring complex postprocessing. The relationship between alveolar PO2 and V_ A/Q_ ratio from HP 3He
MRI is insensitive at both high and low
V_ A/Q_ ratios: large changes in local V_ A/Q_
ratio result in minimal changes in PO2 inthese regions, thereby limiting utility.

183

AMERICAN THORACIC SOCIETY DOCUMENTS

Figure 13. Illustration of computational models of lung structure and function. The right lung shows an imaging-based model of the human
airway tree, reconstructed from volumetric computed tomography and a branch-filling tree generation method, placed within a finite element
mesh. The left lung model is overlaid with a Jacobian (vector function) map showing regional parenchymal deformation calculated bycoregistering paired images obtained at two lung volumes. Colors (from blue to green to red) in the left lung indicate increasing magnitudes ofdeformation from apex to the costophrenic sulcus. Courtesy of M. Tawhai.

Applying Imaging Metrics in
Computational Modeling to
Predict Lung Function
Modeling supports the acquisition, analysis,
and interpretation of images while providing
data to aid in the development andreﬁnement of physiological models.
Although imaging modalities provide
information on regional ventilation, blood_ matching, andvolumes, perfusion, V_ A/Q
blood gas uptake, no method captures all theinformation simultaneously. The goal is tointegrate data from different modalities,
together with physiological measurements, tocomprehensively characterize how structure
determines function. Quantitative linking ofstructure and function in an intricate organsuch as the lung can be done only through184

computational modeling on the basis of thelaws of physics.
Respiratory diseases are complex andmultifactorial, involving remodeling oftissue, airway, and vessel. This complicatesinterpretation of imaging-based metrics andlimits the clinical utility of imaging
biomarkers. Structure-based models(Figure 13) that include interactions among
tissue, airway, and circulation can facilitate
analysis and interpretation of how
intersubject differences, imaging protocols,
and normal or abnormal physiology
contribute to imaging-based metrics.
Carefully constructed models can be used to
examine contributions of different factors(361) and provide mechanistic
understanding far beyond that derived using
statistical methods alone.

General Principles

Biophysical computational models canbe used for forward simulations offunction or inverse identiﬁcation ofsystem structure and parameters. Forward
models are useful for sensitivity analyses(e.g., evaluating the contribution of normal
subject variability to imaging metrics todeﬁne a threshold for abnormal) (362).
Forward models can also predict emergent
behavior in response to a parameterchange at the cellular/tissue level (363).
Inverse models are useful for deriving
information that cannot be measured
directly (e.g., distributions of airway or
vascular obstruction that give rise to_ patterns) (364). Bothspeciﬁc V_ A/Q
forward and inverse models can be used
to analyze, interpret, and predict,AnnalsATS Volume 20 Number 2 | February 2023

AMERICAN THORACIC SOCIETY DOCUMENTS
constrained by physical laws andphysiological variables (e.g., cardiac output
or respiratory rate).
The intimate structure–function
relationships among tissue, airway, andcirculation means that applications ofmodeling to imaging require models that
include at least two of these components
and their interactions. Models can bemade more speciﬁc by using imaging andphysiological data from individual
patients. However, there is a limit towhich the exact topology of airway andvasculature can be determined, andphysiological parameters such as elasticityof the lung tissue or airway wall can only
be estimated. It is critically important tounderstand the appropriate model to use
for speciﬁc imaging analyses and theirlimitations (362).
Applications of Modeling to
Imaging-Based Metrics

Conventional approaches to unifying datafrom different modalities use image
registration to provide one-to-one voxel
mapping and calculation of spatial
correlation. Registration can be challenging
when images are acquired in different
anatomical planes, lung volumes, resolutions,or slice thicknesses. Statistical methods used
to associate imaging with physiologicaland/or anthropometric data are limited in
the mechanistic understanding they provide.
Data can also be integrated by using a single
unifying biophysical model to relate
physiological measurements to imaging, asshown in studies that combine forward andinverse approaches to predict oscillatory lungmechanics in an airway tree model that ismatched to images of ventilation defects(364–366). A realistic model of pulmonary
perfusion has been used to duplicate an ASL
MRI experiment for acquisition of
pulmonary perfusion data to guide selectionof thresholds for removing large-vessel
signals that do not represent capillary
perfusion (323).
Imaging-based metrics rely on
assumptions underlying each imaging
method, analysis, and interpretation. For
example, 1H MR–based SVI uses O2 as acontrast agent, assuming that local lungtissue is in equilibrium with the surrounding
alveolar PO2. The potential contribution of
signal from O2 carried in pulmonary vessels
to SVI may be assessed (367) using a modelthat includes tissue mechanics, ventilation,
perfusion, and gas exchange, to simulate theAmerican Thoracic Society Documents

distribution and exchange of O2 during SVI.
By controlling the presence of signal fromvessels, its impact on heterogeneity andperfusion gradient could be assessed. Thismodel provides a simulated “ground truth”
where the exact O2 signal distribution in thecirculation and tissue are both known. The
model-generated image can be “voxelized”
and postprocessed as virtual image data.
Assumptions in imaging or analysis methods
can be tested by comparing derived metrics
with the model ground truth.
Imaging-based biomarkers based on
statistical relationships between imaging
features and clinical outcomes are insufﬁcientto understand the complexity of interactions
that underlie measurable or imageable
function. For example, several imaging-based
vascular metrics (ratio of PA to ascending
aorta diameter, diameter and volume ofproximal arteries, volume of small arteries,
and microvascular blood ﬂow andheterogeneity) are emerging as potential
biomarkers for disease severity. Althoughthese metrics are interrelated, it is difﬁcult to
examine their physiological interrelationships
from imaging alone; this represents a primeexample in which image-based model
analysis plays a central role. Biophysicalmodels can be time consuming to construct
and execute, but when well-designed
and appropriately used, they provide a
sophisticated tool to complement imaging.

This official document was developed by an
ad hoc subcommittee of the ATS and the
Fleischner Society.
Members of the subcommittee are as follows:
CONNIE C. W. HSIA, M.D. (Co-Chair)1
SUSAN R. HOPKINS, M.D., PH.D. (Co-Chair)2
JASON H. T. BATES, PH.D.3*
BASTIAAN DRIEHUYS, PH.D.4*
SEAN B. FAIN, PH.D.5*
JONATHAN G. GOLDIN, M.D.6
ERIC A. HOFFMAN, PH.D.5*
JAMES C. HOGG, M.D., PH.D.7
DAVID L. LEVIN, M.D., PH.D8*
DAVID A. LYNCH, M.D.9*
MATTHIAS OCHS, M.D.10*
GRACE PARRAGA, PH.D.11*
G. KIM PRISK, PH.D.2*
BENJAMIN M. SMITH, M.D.12,13*
MERRYN TAWHAI, PH.D.14*
MARCOS F. VIDAL MELO, M.D., PH.D.13*
JASON C. WOODS, PH.D.15*
1
University of Texas Southwestern Medical
Center, Dallas, Texas; 2University of California,
San Diego, San Diego, California; 3University ofVermont, Burlington, Vermont; 4School of
Medicine, Duke University, Durham, North
Carolina; 5University of Iowa, Iowa City, Iowa;
6
University of California, Los Angeles,
Los Angeles, California; 7University of British
Columbia, Vancouver, British Columbia,
Canada; 8Mayo Clinic, Rochester, Minnesota;
9
National Jewish Health, Denver, Colorado;10 – Universita€tsmedizin Berlin, Berlin,
ChariteGermany; 11Western University, London,
Ontario, Canada; 12McGill University, Montreal,
Quebec, Canada; 13Columbia University,
New York, New York; 14University of Auckland,
Auckland, New Zealand; and 15Cincinnati
Children’s Medical Center, Cincinnati, Ohio

*Writing committee.

Conclusions
QI continues to evolve with advances in
hardware and software capabilities. The
major imaging modalities described above
offer complementary structure–function
data. Despite high-precision measurements,
effective use of QI to enhance understandingof respiratory (patho)physiology criticallydepends on following robust study design
and unbiased sampling. This ensuresaccurate representation of the structures andselection of validated meaningful metrics,
facilitating understanding of the capabilities
and limitations of each modality andanalytical approach and aiding appropriate
interpretation of the results in correlation
with anatomical and clinicophysiologicalbiomarkers. As in vivo validation of imagingderived metrics remains empirical,
computational modeling plays an importantrole in elucidating the mechanisms
underlying the imaged perturbations. 䊏Author Disclosures: C.C.W.H. served as aconsultant for Ono Pharmaceutical; served asa consultant (spouse/partner) for Allena
Pharmaceuticals, Alnylam, Applied
Therapeutics, Dicerna, Tricida; holds
patent #10912791 and patent #11491115;
received research support from ATyr
Pharmaceutical, Kinevant Sciences,
Mallinckrodt Pharmaceuticals, NIH-NHLBI,
and Department of Defense. J.H.T.B. servedon advisory committee and has financial
stakes in Oscillavent; served as a consultant
for Oscillavent, Healthy Design, Johnson &Johnson, Respiratory Sciences, Chiesi;
employed by Healthy Design; received
research support from NHLBI. B.D. holds
unsold intellectual property, royalties, stock/stock options, leadership or fiduciary role, andserved as a consultant for Polarean Imaging;received research support from Astra Zeneca,
Boehringer Ingelheim, Genentech, NHLBI,
United Therapeutics. S.B.F served onadvisory committee for GE Healthcare; served
as consultant for COPD Gene, Polarean,
Sanofi/Regneron; served as co-chair for QIBA
RSNA; received research support from GE
Healthcare, Siemens; was a speaker for
Genentech, Polarean, Sanofi/Regeneron.

185

AMERICAN THORACIC SOCIETY DOCUMENTS
J.G.G. served as a consultant for Boehringer
Ingelheim; has financial stake in and isfounder of MedQIA, LLC. E.A.H. served on a
photon counting advisory committee for
Siemens Healthineers; founder andshareholder of VIDA Diagnostics; received
research support from NIH. D.L.L. served onadvisory committee for Imagen Technologies,
Inc. D.A.L. served on advisory committee, as
speaker, and as consultant for Boehringer
Ingelheim; served on advisory committee for
Bristol Myers Squibb and Polarean; served
as a consultant for Acceleron, Astra Zeneca,

Calyx Imaging, Daiichi Sankyo, Parexel,
Siemens, and Veracyte; holds patent#10706533, System and method forautomatic detection and quantification ofpathology using dynamic featureclassification; received research support
from NHLBI. G.K.P. is associate editor with
American Physiological Society; holds patent#9750427; received research support from
NIH. B.M.S. received research support from
Canadian Institutes of Health Research,
Canadian Lung Association, McGill HealthCentre Foundation, NIH, and Quebec Health

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Research Fund. M.T. received researchsupport from NIH. M.F.V.M. received
research support from NIH-NHLBI. J.C.W.
served as a consultant for Polarean; served
as a consultant and received researchsupport from Vertex. S.R.H. is employee(spouse/partner) of Thermofisher; hasownership or investment interests in General
Electric; holds patent #9750427; received
research support from NIH-NHLBI. J.C.H.,
M.O., G.P. reported no commercial or
relevant non-commercial interests fromineligible companies.

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