Radiol Phys Technol (2016) 9:139–153
DOI 10.1007/s12194-016-0361-6

Dynamic chest radiography: flat-panel detector (FPD) based
functional X-ray imaging
Rie Tanaka1

Received: 13 May 2016 / Revised: 16 May 2016 / Accepted: 18 May 2016 / Published online: 13 June 2016
Ó Japanese Society of Radiological Technology and Japan Society of Medical Physics 2016

Abstract Dynamic chest radiography is a flat-panel
detector (FPD)-based functional X-ray imaging, which is
performed as an additional examination in chest radiography. The large field of view (FOV) of FPDs permits realtime observation of the entire lungs and simultaneous rightand-left evaluation of diaphragm kinetics. Most importantly, dynamic chest radiography provides pulmonary
ventilation and circulation findings as slight changes in
pixel value even without the use of contrast media; theinterpretation is challenging and crucial for a better
understanding of pulmonary function. The basic concept
was proposed in the 1980s; however, it was not realized
until the 2010s because of technical limitations. Dynamic
FPDs and advanced digital image processing played a keyrole for clinical application of dynamic chest radiography.
Pulmonary ventilation and circulation can be quantified
and visualized for the diagnosis of pulmonary diseases.
Dynamic chest radiography can be deployed as a simple
and rapid means of functional imaging in both routine andemergency medicine. Here, we focus on the evaluation of
pulmonary ventilation and circulation. This review article
describes the basic mechanism of imaging findings
according to pulmonary/circulation physiology, followed
by imaging procedures, analysis method, and diagnostic
performance of dynamic chest radiography.

& Rie Tanaka
rie44@mhs.mp.kanazawa-u.ac.jp
1

Department of Radiological Technology, School of Health
Sciences, College of Medical, Pharmaceutical and Health
Sciences, Kanazawa University, 5-11-80 Kodatsuno,
Kanazawa 920-0942, Japan

Keywords Chest radiography  Functional imaging  Flatpanel detector (FPD)  Ventilation  Circulation  Dynamic
image analysis

1 Background
Over the last 50 years or so, many investigators havecontributed to the development of a pulmonary functional
imaging method based on X-ray technique. Their approaches progressed from X-ray densitometry to image intensifier (II) TV digital imaging, and now dynamic flat-panel
detector (FPD) imaging.
From the beginning, there has been concern about
analysis of the changing lung density during respiration.
Kourilsky and his associates [1] first described a fluoroscopic technique with special photoelectric cells for lungdensity analysis during respiration. They achieved thedetection of abnormalities as small variations in video
signals. Since then, variations of their procedure have beenused by others, confirming its usefulness [2–4]. Modifications of this technique known as fluorodensitometry andvideo densitometry were proposed by George et al. [5, 6]
and Siverman et al. [7–9], respectively. They differentiatedventilation abnormalities from normal regions. During thesame period, other groups were dedicated to the development of image-oriented technology. Rogers et al. [10] andToffolo et al. [11] evaluated the distribution of intravenousradioactive agents by scanning one whole lung, using
collimators with 1–2 cm diameter.
In the 1980s, II-TV systems were commonly used inclinical practice. Researchers attempted to visualize pulmonary ventilation and circulation based on the translucency variations on digital fluoroscopic images, for which
they used the subtraction technique. Leung et al. [12, 13]140

investigated regional pulmonary ventilation with a 57 cm
II-TV system and nonradioactive Xenon. Regional pulmonary and myocardial perfusion with digital subtraction
angiography (DSA) was proposed in this period [14–16].
The digital subtraction technique without any injection was
also proposed and proven to be useful for studying ventilation and/or perfusion on digital fluoroscopic images[17–19]; this was the precursor of dynamic chest
radiography.
Although there have been many reports demonstrating
the feasibility of pulmonary densitometry and digital fluoroscopic approaches, these techniques have not achieved
widespread use due to technical limitations, such as theneed for special instrumentation, poor image quality, and asmall field of view (FOV).
Dynamic chest radiography based on a dynamic FPD
combined with computer analysis has overcome the difficulties encountered in the above mentioned studies.
Dynamic FPDs were developed in the 2000s and are now
widely used in X-ray fluoroscopic examinations. With
dynamic FPDs, both lungs can be observed simultaneously
on dynamic images during respiration with a large FOV.
There is no image distortion, and high image quality can beachieved at low dose levels due to improved sensitivity of
X-ray detectors [20, 21].
Dynamic chest radiographs contain a wealth offunctional information, such as diaphragm movement,
cardiac motion, pulmonary ventilation, and circulation.
Most importantly, pulmonary ventilation and circulation
are reflected as slight changes in pixel value on dynamic
chest radiographs. However, their interpretation is challenging for radiologists; therefore, computerized methods have been developed for the evaluation of
pulmonary ventilation [22–25] and circulation [26–29]
on dynamic chest radiographs. With image subtraction
and color-mapping techniques, ventilatory and perfusion
impairments were detected as reduced changes in pixel
value without the use of contrast media (described laterin detail).
The first clinical report of this technique was publishedby the author’s group [22]. We demonstrated that in apatient with emphysema, trapped air was indicated as
reduced changes in pixel value despite diaphragm motion
in a dynamic way. In experimental studies focusing on
pulmonary perfusion, we also succeeded in visualizing
circulation-caused changes in pixel value as sequential mapof blood distribution, showing a normal pattern, which
diffuses from around the pulmonary arteries to theperipheral area [26, 28]. In the process of functional
analysis, many related technologies have been developedfor evaluating diaphragm motion [30, 31], measuring thevelocity field in the lungs [31], and determining the respiratory/cardiac phase [30–32].

R. Tanaka

The diagnostic performance improved as technology
advanced, and finally, a functional X-ray imaging method,
called ‘‘dynamic chest radiography’’, was realized. It is
expected to be a low dose and cost-effective functional
imaging method for evaluation of pulmonary function.

2 Physiology: what is reflected on dynamic chest
radiographs?
The most common and simple form of functional imaging
is inspiratory/expiratory chest radiography. In general,
additional imaging at the expiration level is performed forassessment of diaphragm movement and respiration-induced changes in X-ray translucency in the lungs, inaddition to conventional imaging at the inspiratory level.
Dynamic chest radiography also focuses on the lung density changes due to respiration or circulation. It is essential
to understand their mechanism in terms of pulmonary andcirculation physiology for utilizing dynamic chest radiography as a powerful and pertinent radiologic tool.
2.1 Pulmonary ventilation
The condition of the lungs is evaluated on the basis of
X-ray translucency in the lung area in conventional chest
radiographs. An increase in X-ray translucency indicates
localized air space that may represent anomalies like a lungcyst, emphysema, bulla, and pneumothorax. In contrast, adecrease in X-ray translucency indicates decreased pulmonary air or enhanced tissues, suggestive of conditions
like pulmonary inflammation, fibrosis, edema, or sclerosis[33]. In dynamic chest radiographs, pulmonary function
can be assessed based on the temporal changes in X-ray
translucency due to respiration or cardiac pumping. The
respiration-induced changes depend on relative increases
and decreases in the air and lung vessel volume per unit
volume [34, 35].
Figure 1 depicts the measured pixel value and electrocardiogram (ECG) findings in a normal control subject.
Low pixel values were related to dark areas in the images,
and these in turn, were related to high X-ray translucency
in this review article. The pixel value decreases (X-ray
translucency:) according to an increase in air volume in thelung during the inspiratory phase; in contrast, the pixel
value increases (X-ray translucency;) according to adecrease in air volume in the lung during the expiratory
phase. The slight change in synchrony with ECG findings
is the result of changes caused by cardiac pumping andpulmonary blood circulation. The impact of cardiac motion
is identified to be less than 10 % on the respiration-inducedchanges [36]. Thus, it is possible to evaluate the relative
pulmonary ventilation quantitatively from respiration-

Dynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging

141

Fig. 1 Mechanism of dynamic changes in X-ray translucency (= pixel value). Respiration-induced changes in pixel value are caused by changes
in air and lung vessel volume per unit volume, whereas circulation-induced ones are caused by changes in blood volume per unit volume

induced changes in pixel value on dynamic chest
radiographs.
For example, chronic obstructive pulmonary disease(COPD) patients take time to expire, and they also ventilate
insufficiently due to trapped air. Therefore, it is possible todetect abnormalities such as delayed and decreased changes in pixel values. Preliminary studies showed that asimulated ventilatory defect more than 10 mm in diameter
could be detected as decreased changes in pixel value. In
addition, respiratory changes in pixel value had a close
relationship with displacement of the diaphragm, and thechanges in the right and left lungs were almost the same
(R:L = 1:1) [37]. However, the following points should be
noted: the present method does not directly measure the gas
exchanges in the lung alveoli; rather, it provides relative
functional information related to pulmonary ventilation.

per unit volume is greatest near the bottom of the lung andbecomes progressively smaller toward the top, with asymmetrical right-and-left distribution. Therefore, in clinical situations, ventilatory impairment can be detectedbased on a deviation from the normal pattern of the ventilation distribution. In the same way, regional differencesin ventilation can be detected as regional differences in
respiratory changes in pixel value, which are greatest near
the bottom of the lung and become significantly smaller
toward the top (P \ 0.01) [37]. Therefore, ventilatory
impairment can be detected by comparison of respirationinduced changes in pixel value in the left and right lungs in
the craniocaudally same level [25]. These facts show thatdynamic chest radiography has the capability of measuring‘‘regional differences in ventilation’’ based on the respiration-induced pixel value.

2.2 Regional differences in ventilation

2.3 Airway closure

It is well known that there are regional differences inventilation in respiratory physiology [34, 35]. Ventilation

At the end of expiration, the lower airway closes earlier
than the upper airway due to the differences in air pressure

142of the thoracic cavity, i.e., airway closure, and the ventilation in the upper area becomes greater than that in thelower area [35]. The lung volume when airway closure
occurs is defined as the ‘‘closing volume,’’ which is a very
effective index for diagnosing pulmonary diseases. For
example, in subjects with COPD and restrictive pulmonary
disease, airway closure appears in the early expiratory
phase, and as a result the closing volume becomes large.
Therefore, abnormalities can be detected by evaluation of
the timing of ‘‘airway closure’’. In the case of dynamic
chest radiography, airway closure is confirmed as moving
up of regions with the greatest inter-frame differences in
pixel value during expiration [37]. That is, dynamic chest
radiography has the capability of determining the ‘‘closing
volume’’ based on the regional and temporal measurementof the respiration-induced changes in pixel value.
2.4 Pulmonary blood flow
Circulation dynamics is reflected on chest radiographs, andabnormalities appear as shape changes or shifts in the
distribution of pulmonary vessels [35, 38, 39]. These are
effective indices for diagnosing specific cardiac diseases
and determination of an appropriate examination procedure: redistribution or cephalization of pulmonary blood
flow, indicating the presence of pulmonary venous hypertension [40]; a centralized pulmonary blood flow pattern,indicating pulmonary arterial hypertension [41]; or
widening of the vascular pedicle, indicating an increase in
the circulating blood volume [42]. Circulation dynamics is
also reflected on fluoroscopic images as changes in X-ray
translucency [8, 9, 26–28] (Fig. 1), which provide functional information. This is because the lungs contain aconstant volume of about 500 mL of blood, with 75 mL
distributed variably across the vasculature due to cardiac
pumping [36].
In dynamic FPD imaging; the pixel values change linearly with the blood volume in the lungs, and a perfusion
defect more than 10 mm in diameter can be detected as
decreased changes in pixel values [26]. There is a concern
about dilation and contraction of vessels; however, it would
not be considered to affect the measurement of pixel values
in projected images because the rate of change is reported
to be approximately ±10 % [25].
Figure 2 shows changes in pixel value measured inregions of interest (ROIs) on dynamic chest radiographs
under breath-holding in a normal subject. There is a strong
correlation between the cardiac cycle and changes in pixel
value, which are measured in the ventricles, atria, aortic
arch, and pulmonary arteries [25] (Fig. 2a, b). The changes
in pixel values measured in each ROI can be explained bynormal circulation dynamics, as indicated below: (1) at theend of the diastolic phase, the ventricles are at the

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maximum volume, as shown by large pixel values in theventricles. (2) In the early ventricular systolic phase, fromclosure of the atrioventricular (AV) valves to opening of
the aortic valve, the ventricular volume remains constant,
shown as the absence of a significant change in pixel values
during this period. (3) After opening of the aortic valve,blood is pumped from the ventricles into the aortic arch andpulmonary arteries. This is shown as decrease in pixel
values in the ventricles and an increase in pixel values in
the aortic arch and pulmonary arteries. (4) In the lateventricular systolic phase, increase in aortic blood flow isshown as a continuous increase in pixel values. (5) In theearly ventricular diastolic phase, from closure of the aortic
valve to opening of the AV valves, the ventricular volume
remains constant. (6) Blood rapidly moves from the atria to
the ventricles in response to opening of the AV valves; thisis why the pixel values in the ventricles increase, while
those in the atria and pulmonary veins decrease.
Figure 2c shows the average rate of change in pixel
value for seven normal subjects. The results measured ineach ROI decrease in the following order: left ventricle [ left atrium [ aortic arch [ right atrium [ rightventricle [ left pulmonary artery. These findings indicatethat the pulmonary blood circulation is reflected on
dynamic chest radiographs, and that the quantitative analysis of changes in pixel value has potential for evaluationof the local blood circulation.

3 Imaging procedures
Sequential chest radiographs during forced respiration are
obtained with use of a dynamic FPD system and X-ray
generator capable of pulsed irradiation. To be a reliablediagnostic tool, the system must have a high homogeneityand uniformity in X-ray pulses [43]. Except for breathingmanner, imaging is performed in the same way as theconventional chest examination, i.e., the standing position
and the PA (posteroanterior) direction. For an accurate
evaluation of cardiopulmonary function, it is crucial toinclude one respiratory cycle within a limited amount oftime with good reproducibility. Therefore, it is recommended to use an automatic voice system and conduct pretraining for patients. The total patient dose is adjustable bychanges in the imaging time, imaging rate, and source toimage distance (SID), and can be less than the dose limit
for two projections (PA ? LA) recommended by theInternational Atomic Energy Agency (IAEA) (1.9 mGy)
[44].
Cardiopulmonary function is evaluated separately ineach respiratory phase, i.e., inspiratory, expiratory, andbreath-holding phase. The imaging rate should be greaterthan 7.5 frames per second (fps) to permit an accurate

Dynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging

143

Fig. 2 (a) Relationship between cardiac cycle and changes in pixel
values, (b) the variations during a whole cycle, and (c) measurement
locations. Small squares show ROIs for measuring average pixel

value, and the horizontal line shows a profile for measuring left
ventricle motion. (SD: standard deviation, N.S.: not significant)evaluation of cardiac function [26]. If the focus is on theevaluation of respiratory function, a lower imaging rate
than 7.5 fps is acceptable by taking into account the patient
dose. However, an imaging time of more than 10 s isrequired to induce the maximum voluntary respiration.
Figure 3 shows an example of the imaging protocol. In thecase that the exposures are taken at 120 kV, 50 mA, 2.5
ms, 15 fps, and 2.0 m of SID, the entrance surface dose to
the detector is approximately 1.9 mGy in 14 s.

such as recognition of the lung area [45, 46], texture
analysis [47–51], measurement of heart size [52], andimage registration [53, 54], as well as recursive filter [55]and motion tracking [56–58] for digital fluoroscopy or
DSA. To extract functional information from dynamic
chest radiographs, we need to develop dynamic-oriented
analysis methods, while effectively utilizing the techniques described above. The computerized scheme fordynamic chest radiographs includes three basic components: determination of imaging phase, quantification,and visualization of functional information, as shown in
Fig. 4.

4 Dynamic image analysis
Dynamic chest radiographs are sequential chest radiographs obtained in an extremely short time interval. Thus,
we can utilize some of the previously developed image
processing techniques for conventional chest radiographs,

4.1 Image pre-processing
Dynamic chest radiographs consist of images during theinspiration, expiration, and breath-holding phases, which

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Fig. 3 An example of imaging protocol for dynamic chest radiography

4.1.1 Determination of respiratory phase
The lung area is determined by edge detection using thefirst derivative technique and an iterative contour-smoothing algorithm [45, 46]. The upper most points of the lungand diaphragm are determined in the first frame, and thentraced by the template-matching technique after the second
frame [22, 30, 31]. The respiratory phase is determinedbased on the distance from the lung apex to diaphragm.
The frames for the maximum and minimum distance aredetermined as the maximum inspiratory and expiratory
frames, respectively [32]. In this process, the excursion of
the diaphragm is also calculated by subtracting the distanceat the maximum inspiratory level from that at the expiratory level. Diaphragm kinetics provides effective functional information for the diagnosis of pulmonary diseases.
Abnormalities are detected by comparison with normal
excursion or side-by-side comparison in each individual.
Fig. 4 Overall scheme of image analysis for dynamic chest
radiographs

are classified into any of the cardiac phases, systole ordiastole. It is essential to specify the respiratory and cardiac
phases of each image because dynamic images containdifferent kinetic information in each respiratory and cardiac phase. For selecting proper image processing, therespiratory and cardiac phases should be determined prior
to dynamic image analysis.

4.1.2 Determination of cardiac phase
The cardiac phase is estimated on the basis of moving
directions of the left ventricular wall, which can be determined by the motion tracking technique. In this approach,cardiac function can be assessed based on the motion
rhythm and displacement of the left ventricle wall. The
cardiac phase is also stably determined based on the
averaged pixel values measured in a ROI over the edge ofthe left ventricular wall [28]. In that case, decrease in pixel
values (a low proportion of the cardiac area) is determinedDynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging

as the systolic phase and increase in pixel values (a highproportion of cardiac area) as the diastolic phase.
4.2 Quantification and visualization
Image subtraction is a powerful tool for identifying slightchanges in pixel value, even though it is difficult to evaluate the changes by observing images. There are two main
approaches to the quantification and visualization of pulmonary ventilation and circulation: (1) inter-frame subtraction, and (2) image subtraction with/among specificimages (e.g., an averaged image, MIP/MinIP, an image inthe maximum inspiration or expiration, an image in thesystolic or diastolic phase, etc.). The former provides
functional information relevant to ‘‘change rate’’ or ‘‘flow
velocity’’, whereas the latter provides functional information relevant to ‘‘distribution’’.
MIP
MinIP

maximum intensity projection
minimum intensity projection

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evaluate the regional changes in pixel value on the
resulting maps, showing a normal pattern determined inpulmonary physiology [34, 35].
Image subtraction between images at the maximum
inspiration and expiration creates another functional image,
showing a distribution map of respiration-caused changes
in pixel value, i.e., information related to the ventilation
distribution. We can also evaluate the pulmonary function
using difference maps created from image subtraction withany kind of specific image (e.g., an averaged image during
one breathing cycle, an image in expiratory phases) as abase image. A base image varies depending on the diagnostic purpose. In any approach, these functional images
are also created by superimpose of the difference values on
dynamic chest radiographs in the form of a color display.
The relative inspired volume compared to that at the
maximum expiratory level may be assessed in a distribution map, while the relative increase and decrease in ventilation compared to the base image in difference maps,
respectively.

4.2.1 Pulmonary ventilation

4.2.2 Pulmonary circulation

Pulmonary ventilation is assessed in images during respiration. It is highly likely that the same point on sequential
images does not represent the same anatomic point. Thus, anon-rigid image registration is required to match the corresponding points to each other. The image-warping technique developed for digital image subtraction of temporally
sequential chest images can be utilized for this purpose[53]. The displacement of lung structures at the imaging
rate of more than 7.5 fps is quite small, 0–3 pixels perframe. Therefore, a spatial smoothing may be used as analternative for inter-frame registration. The spatialsmoothing technique is effective in reducing respiratory
artifacts caused by slight mis-registration of lung structures. Smoothing of pixel values is also performed in thetime axis direction to reject noise due to circulation artifacts [22]. After the image registration and noise reduction,
two kinds of image subtraction described below are
performed.
Inter-frame differences in pixel value during respiration
create velocity maps of respiration-caused changes in pixel
value, i.e., information related to the change rate orvelocity of the air flow. Figure 5 shows the overall
scheme of the computer algorithm for visualizing respiration-caused changes. Inter-frame differences are calculated
throughout all images during respiration, and are thensuperimposed on the original images in the form of a color
display using a color table in which higher X-ray translucency (increased air) is shown in warm colors, and negativechanges, i.e., lower X-ray translucency (decreased air) are
shown in cool colors. As shown in Fig. 5, we can visually

Pulmonary circulation is assessed in images during breathholding, which do not contain respiration-induced changes
in pixel value. Spatial smoothing is used for reducing
artifacts caused by pulsating lung structures. The pulmonary circulation may be analyzed in images duringrespiration. In that case, pre-processing is required so that
respiratory changes in pixel value are rejected. A correction method developed for videodensitometry can be utilized for this purpose [9]. Signals measured over the edgeof the heart are used as reference and correlated with thosemeasured in various ROIs to produce signals with low
noise to reduce respiratory artifacts. These correlations are
performed as a function of time delay between the reference signal and the noisy densitometry signal.
Figure 6 shows the overall scheme of the computer
algorithm for visualizing the pulmonary circulation. Interframe differences in pixel value during breath-holding
create velocity maps of circulation-caused changes in pixel
value, i.e., information related to the change rate or flow
velocity of the circulation. Inter-frame differences arecalculated and then superimposed on the original images in
the form of a color display using a color table in which
lower X-ray translucency (increased blood volume) isshown in warm colors, and negative changes, i.e., higher
X-ray translucency (decreased blood volume) are shown incool colors. As shown in Fig. 6, we can observe circulation-caused changes on the resulting velocity maps,
showing a normal pattern of pulmonary and myocardial
perfusion, which diffuses from the pulmonary arteries tothe peripheral area.

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Fig. 5 Computer scheme for visualizing respiration-induced changes in pixel value

Fig. 6 Computer scheme for visualizing circulation -induced changes in pixel value (Japan patent JP4797173)

R. TanakaDynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging

Image subtraction between two specific images createsanother functional image, showing a distribution of circulation-caused changes in pixel value, i.e., information
related to the perfusion distribution. For example, aminimum intensity projection (MinIP) image is created in
one cardiac cycle, which composed of pixel values
showing the least blood during one cardiac cycle. Thus, if
image subtraction was performed between a MinIP image
and an image at the early diastolic phase, the resulting
distribution map would be created by the maximum differences in pixel value in the lungs during one cardiac
cycle. We can also evaluate increases and decreases incirculation using image subtraction between sequential
images with one specific image (e.g., an averaged image
during one cardiac cycle, an image at the end of systolicor diastolic phases). A base image varies depending on
the diagnostic purpose as well as analysis of the pulmonary ventilation. In any approach, distribution maps
are also created by superimpose of the difference values
on dynamic chest radiographs in the form of a color
display.

147

Pðm; nÞ
P% ðm; nÞ ¼ N1 M1
 100
P P
Pðm; nÞ

ð1Þ

n¼0 m¼0

where M and N are the numbers of blocks in the horizontal
and vertical directions, respectively. In addition, m andn represent the coordinates of the blocks in the horizontal
and vertical directions, respectively. Block unit analysis isuseful for realizing a quantitative and statistical analysis as
well as reducing the influence of movement, dilation, andcontraction of vessels. In addition, it facilitates side-by-side
and/or one-on-one comparison for computer-aided diagnosis (CAD) and V/Q study (described later). To facilitate
visual evaluation, P (m, n) may be mapped on the original
image in the same way as the velocity/distribution maps.
4.3 Detection of abnormalities
4.3.1 V/Q study

The above velocity and distribution maps are helpful for
visual interpretation of the pulmonary function; however,they lack quantitation. One of the solutions is the use ofanalysis in block units, where pixel values are averaged ineach block, tracking and deforming the ROI [25]. Figure 7
shows an example of block unit analysis. P (m, n) is the
maximum difference in each block during a wholebreathing or cardiac cycle. The percentage in differences ofthe pixel values to the summation of the results in all
blocks is then calculated as

The ventilation-perfusion (V/Q) ratio is a very importantdiagnostic index for the evaluation of V/Q ratio, leading to
the development of a treatment strategy in patients withpulmonary diseases. The V/Q ratio is usually calculated
from the radioactivity count in lung scintigraphy. Functional imaging with a dynamic FPD has the potential forevaluating the V/Q ratio [29]. A V/Q map based changes in
pixel value is provided by the use of resulting images of theventilation and circulation distribution, i.e., as the ratio ofventilation to circulation distribution.
Figure 8 shows the results of pixel value-based V/Q
study in a patient with a ventilation-perfusion mismatch(hypoxemia, 74-year-old man). In this case, the area ofdefective ventilation over the right lung was indicated as

Fig. 7 One frame of dynamic chest radiographs divided into blocks
that were slightly smaller than the intercostal space. (a) The blacklines show the lung area determined for the measurement of pixel
values in the lungs. The hilar regions are excluded from the lung area.

(b) The bold vertical line shows the center of symmetry. ‘‘Symmetricpositions’’ are pairs of blocks the same distance from the center ofsymmetry. The small black and white squares show the blocks withand without a pair for comparison, respectively

4.2.3 Quantitative analysis

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and cardiac physiology. Thus, there is a great concern
about the development of a CAD system for dynamic chest
radiography. This development has just started, and only
two trials have been reported. Tsuchiya et al. [59] developed a CAD system to detect lung nodules using dynamic
chest radiographs taken during respiration. Their techniquecould quantitatively evaluate the kinetic range of nodules
and was effective in detecting a nodule on dynamic images.
Another CAD system was to detect functional impairments in the ventilation and blood circulation in the lungs[25]. The results showed that unilateral abnormalities could
be detected as a deviation from the right and left symmetryof respiration-induced changes in pixel value. However, fordetecting bilateral abnormalities, further studies are
required for the development of a multilevel detection
method combined with several methods of pattern analysis.

5 Case report

Fig. 8 The process of creating the V/Q map on the basis of changes in
pixel value. V/Q map is created based on P %(m,n) calculated from Eq. 1

remarkably reduced changes in pixel value in the ventilation mapping image. In the result, the right lung showed
lower V/Q ratio than that in the left lung. This finding
indicates severe pulmonary impairment due to unbalanced
V/Q, which was supported by the findings of the V/Q ratio
calculated from lung scintigraphy [29].
4.3.2 Computer-aided diagnosis (CAD)
An accurate interpretation of dynamic chest radiographs
requires a great deal of knowledge regarding respiratory

Preliminary clinical trials have demonstrated that pulmonary impairment is successfully detected as decreased
changes in pixel value in patients with pulmonary diseases,
such as emphysema, asthma, and lung fibrosis [22–25], andthat quantified diaphragm kinetics is also useful for theassessment of pulmonary function in patients with lung
lobectomy. In this section, the results in a patient withpulmonary disease will be described in comparison withthe typical findings found in normal controls.
Normally, ventilation color mapping images show a leftright symmetric distribution, with the color intensity
increasing from the lung apex to the base of the lung, asshown in Fig. 5. The findings are reflection of normal
pulmonary ventilation, which is greatest near the bottom of
the lung and becomes progressively smaller toward the top
with a symmetric right-and-left distribution [34, 35]. Circulation color mapping images also show a left-rightsymmetric distribution, with the color intensity decreasingfrom the pulmonary arteries to the peripheral area [60],reflecting normal perfusion, as shown in Fig. 6. In contrast,
abnormal cases show nonuniform/asymmetric color distributions, which are different from the normal pattern. Figures 9, 10, 11 show the results in a patient with pleuraladhesions in the left lung, resulting in a middle restrictedventilatory abnormality (74-year-old man). There are several findings of pleural adhesions in CT images (Fig. 12b,e–g). Lung scintigraphy shows marked reduction of ventilation as well as a reduced circulation in the right lung incomparison with the left lung (Fig. 12c). The right lungshows reduced changes in pixel value as well as a reduced
intensity in the ventilation color mapping images (Fig. 9).
The differences in respiratory changes in pixel value
between the right and left lungs are also confirmed by ROIDynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging

149

Fig. 9 Results of analysis of pulmonary ventilation

Fig. 10 Results of analysis of pulmonary circulation

Fig. 11 Measurement of
averaged pixel value in ROIs
located in the right and left
lungs. The white squares
indicate ROIs for themeasurement

measurement, which is similar to lung densitometry as
proposed by Siverman et al. [7–9] (Fig. 11). Furthermore,this patient had a reduced blood flow area in the rightwhole lung, as shown in Fig. 12d. The area appeared as adecrease in intensity in color mapping images during thebreath-holding phase (Fig. 10).

6 Future potential of dynamic chest radiography
It is likely that dynamic chest radiography will be performed as an additional examination in the chest radiography. To meet a high demand for a portable use in clinicalpractice, particularly in emergency medicine, a150

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Fig. 12 Image findings of the patient with pleural adhesions (74year-old man). (a) Chest radiograph, (b) CT image (coronal view),(c)-(d) ventilation and perfusion scintigrams. The right lung shows

marked reduced ventilation (solid line) as well as reduced circulation(broken line). (e)-(g) CT images (axial views). There are several
findings of pleural adhesions

portable dynamic chest radiography system will emerge in
the near future. Dynamic chest radiography is certainly not
the perfect functional imaging; lack of 3D information, no
referring actual gas exchange nor perfusion (only relative
functional information). These issues may be solved by theuse of contrast media and advanced technologies, such as
tomosynthesis and cone-beam CT. However, for dynamic
chest radiography to be widely used in clinical situations, it
is important to use simple procedures as much as possible
so that clinicians have ready access to functional information just as if they used a stethoscope to assess pulmonary and/or cardiac abnormalities.
Radiologists and thoracic physicians may utilize
dynamic chest radiography in two different ways. One is asa screening tool in health checkups. Patients are examined
not only for detection of anatomic abnormalities, but also
for the assessment of cardiopulmonary function. The total
patient dose is about double that of conventional chest
radiography; however, this is acceptable because of theincreased yield of information.
Another use is as a follow-up tool for patients withpulmonary diseases. Dynamic chest radiography allows
radiologists and thoracic physicians to evaluate the treatment effects and perioperative changes. One of the mostimportant advantages of this functional imaging is that theimaging is performed in a manner similar to conventional
chest radiography. Low-cost, small space and high-throughput functional X-ray imaging would lead to a
reduction of medical costs, which is a potential advantageof dynamic chest radiography.
With dynamic chest radiography, image interpretation
has transited from conventional estimation based on anatomic findings to evaluation based on dynamic changes. In
this situation, computerized methods for quantifying cardiopulmonary function are essential for the implementationof dynamic chest radiography.
Some basic computer schemes have been demonstrated
in this review paper. Specifically, image subtraction andcolor mapping techniques are useful for quantifying andvisualizing slight changes in pixel value caused by respiration and circulation.
For further assistance to radiologists, it is urgent todevelop CAD schemes designed for dynamic chest
radiography. Another concern is an adverse effect of rib
shadows. The presence of the ribs overlying or underlying soft tissues affects the quantitative analysis ofpixel value in the lungs. In addition, the rib shadow
prevents accurate inter-frame registration targeted forsoft tissues because they move in a different manner
during respiration. One of the solutions is the utilizationof an advanced form of image processing for bone
suppression, providing images that appear similar todual-energy soft tissue images [61, 62]. Improved
accuracy of motion tracking, texture analysis, andDynamic chest radiography: flat-panel detector (FPD) based functional X-ray imaging

analysis of rib kinematics has been confirmed indynamic imaging combined with the bone suppression
technique [63–65]. The computation time should beshort for real-time processing by utilizing images preceding and following a present image.
Although the diagnostic performance of dynamic chest
radiography has been addressed, the number of patients forwhom it has been applied to so far is insufficient for thedevelopment of diagnostic criteria. Furthermore, it is likelythat there is still a large amount of unused information that
could be of real importance to clinicians and researchers.
Dynamic chest radiography has been available technicallyand will be widely available in the near future. Further
clinical studies are required which address the potential of
dynamic chest radiography, along with the establishment of
diagnostic logic. Successful application of dynamic chest
radiography requires close collaboration of investigatorsfamiliar with technical matters and those familiar with
anatomy, physiology, and clinical practice, for which thistechnique is being attempted to be applied.
There is an important issue related to the use of dynamic
chest radiography both in clinical and research situations.
The present method does not directly measure the gas
exchanges in lung alveoli, or the actual perfusion; rather, it
provides relative functional information related to pulmonary ventilation and circulation. Therefore, dynamic
chest radiography should be used for the evaluation of
intra-individual changes (e.g., follow-up examinations and/or side-by-side comparisons) rather than inter-individual
differences, knowing the system capability as well as basic
respiratory/circulation physiology.
Furthermore, it is not surprising if there are some differences between findings in dynamic chest radiography
and those in lung scintigraphy. This is because the two aretotally different in their imaging target, imaging mechanism, and imaging posture. In fact, such a difference has
been reported in some clinical cases [24, 27]. The fact
would rather be a great motivator for exploring respiratory
and circulation physiology projected on dynamic chest
radiographs. There are still many questions regarding thediagnostic performance of dynamic chest radiography
which merit further research.

7 Conclusion
Dynamic chest radiography is a FPD based functional
X-ray imaging, performed as an additional examination inchest radiography. Some of the most important advantages
are: (1) imaging is performed in a manner similar to conventional chest radiography, (2) the total patient dose canbe less than double the conventional one, and (3) pulmonary function can be assessed even without the use of151

contrast media. It is likely that there is still a large amountof unused information on dynamic chest radiographs during respiration. For utilizing it as a powerful and pertinent
radiologic tool, further clinical studies are required which
address the potential of dynamic chest radiography incollaboration with clinicians, physicists, and engineers.
Acknowledgments The author is grateful to the staff in the department of Radiology, Respiratory Medicine, and Respiratory Surgery,
Clinical Laboratory, Kanazawa University Hospital, and staff from
Canon Inc., for their assistance with clinical data acquisitions, and to
Shigeru Sanada, PhD, for his frequent support over the course of the
project, and Nobuo Okazaki, MD, for his intellectual debate on respiratory physiology. This work was supported in part by The Ministryof Education, Culture, Sports, Science and Technology, MEXT
KAKENHI Grant Number 16K10271, 24601007, 19790860; JSPS
Grant-in-Aid for Scientific Research on Innovative Areas (Multidisciplinary Computational Anatomy) JSPS KAKENHI Grant Number
15H01113; The Tateisi Science and Technology Foundation, Nakashima Foundation, Konica Minolta Science and Technology Foundation, Suzuken Memorial Foundation, Japan Cardiovascular
Research Foundation, Nakatani Foundation, The Mitani Foundation
for Research and Development, and The Mitsubishi Foundation.
Compliance with ethical standards
Conflict of interest The authors declare that they have no conflict ofinterest.

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