# Cerebral Blood Flow Measurement Using fMRI and PET: A Cross-Validation Study

Authors: Jean J. Chen; Marguerite Wieckowska; Ernst Meyer; G. Bruce Pike
Journal: International Journal of Biomedical Imaging
 (2008)

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## Abstract

An important aspect of functional magnetic resonance imaging (fMRI) is the study of brain hemodynamics, and MR arterial spin labeling (ASL) perfusion imaging has gained wide acceptance as a robust and noninvasive technique. However, the cerebral blood flow (CBF) measurements obtained with ASL fMRI have not been fully validated, particularly during global CBF modulations. We present a comparison of cerebral blood flow changes (ΔCBF) measured using a flow-sensitive alternating inversion recovery (FAIR) ASL perfusion method to those obtained using H2O15 PET, which is the current gold standard for in vivo imaging of CBF. To study regional and global CBF changes, a group of 10 healthy volunteers were imaged under identical experimental conditions during presentation of 5 levels of visual stimulation and one level of hypercapnia. The CBF changes were compared using 3 types of region-of-interest (ROI) masks. FAIR measurements of CBF changes were found to be slightly lower than those measured with PET (average ΔCBF of 21.5±8.2% for FAIR versus 28.2±12.8% for PET at maximum stimulation intensity). Nonetheless, there was a strong correlation between measurements of the two modalities. Finally, a t-test comparison of the slopes of the linear fits of PET versus ASL ΔCBF for all 3 ROI types indicated no significant difference from unity (P>.05).


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## Body

## 1. Introduction

Neuronal activity results in focal changes in hemodynamics, metabolism, and blood
oxygenation of associated brain areas. Functional maps of cerebral blood flow
(CBF) can be used to monitor hemodynamic changes in the healthy brain as well as
alterations associated with cerebrovascular disease. Positron emission
tomography (PET) is capable of providing in vivo quantitative measures of CBF and
has evolved to be considered the gold standard for studying cerebral
hemodynamics. However, PET imaging involves the injection of radioactive
tracers, which limits its repeatability and application in healthy volunteers.
Among other limitations are low temporal and spatial resolution, low
signal-to-noise ratio (SNR), as well as the requirement for a cyclotron. Thus,
magnetic resonance (MR) perfusion imaging, being widely available and having
relatively high spatial and temporal resolution, is increasingly seen as an
attractive alternative to PET.MR
perfusion imaging is performed using dynamic susceptibility contrast (DSC)
techniques or arterial spin labeling (ASL) [1, 2]. DSC imaging has not been widely
applied in human functional research due to the requirement of an exogenous
contrast agent and limited temporal resolution. ASL is based on the detection
of magnetically labeled arterial blood water spins and has therefore been used
with more success in functional MRI (fMRI) studies. Pulsed ASL methods such as
proximal inversion with control for off-resonance effects (PICORE),
flow-sensitive alternating inversion recovery (FAIR), quantitative imaging of
perfusion using a single subtraction (QUIPSS I/II), and QUIPSS II with
thin-slice TI1 and periodic saturation (Q2TIPS) have greatly facilitated perfusion-based
fMRI [2–8].The
validation of MR perfusion measurements using various invasive and noninvasive
methods has been a topic of considerable interest. Walsh et al. compared CBF measured
using continuous ASL and radioactive microspheres using a rat model and found ASL
to underestimate CBF under high flow [9]. On the other hand, based on radiotracer-enhanced
quantitative autoradiography flow measurements in rats, Ewing et al. concluded that CBF were overestimated by ASL under ischemia [10]. In healthy humans, Østergaard et
al. [11] found a highly linear
relationship between PET and DSC MR CBF measurements (using Gd-DTPA),
consistent with the values reported in the literature. Similar findings were
reported by Carroll et al. [12], Lin et
al.
[13], and
Grandin et al. [14] in healthy human subjects. Quantitative CBF values
of 68.1±9.5 and 26.7±5.0 mL/100 g/min were measured
for grey matter (GM) and white matter (WM), respectively [13]. In addition, Grandin et al.
reported a high correlation between DSC and PET CBF measurements under the
effect of vasodilative pharmacological agents, with PET results having higher
reproducibility [14]. However, Carroll and Grandin found that GM
CBF values were overestimated with DSC MR possibly due to sensitivity to the
presence of large blood vessels [12, 14].Ye et al. reported a comparison of resting CBF using steady-state ASL and PET and
measured GM CBF of 64.12 and 67.13 mL/100 g/min, respectively. The PET and ASL
measurements were not statistically different from one another and were both in
good agreement with literature values [15]. However, the WM ASL CBF (23.8 mL/100 g/min) was 30% lower compared to PET (33.7 mL/100 g/min), the
discrepancy being attributed to the arterial tagging time difference between GM
and WM, specific to the quantitative model employed in this study. In epilepsy
patients, Liu et al. studied
perfusion in the temporal lobe using the FAIR-prepared half-Fourier single-shot
turbo spin-echo (HASTE) technique [16] and also found a statistically
significant correlation between ASL and PET data. A functional comparison
involving PET and ASL was first performed by Zaini et al. using a simple
finger-tapping task
[17]. However, the matching of spatial
resolution and noise was not possible for PET and ASL data. In a more recent
study by Feng et al. [18], another comparison of PET
and FAIR fMRI measurements of CBF changes was reported, using a single level of
visual stimulation in healthy subjects. Once again, results obtained by the two
methods were very similar, with the PET CBF percent change being slightly
higher than that of FAIR (ΔCBF of 38.79% versus 36.95%).Notwithstanding
the contributions of the above studies, several factors limit the scope and
applicability of the existing studies. Firstly, an accurate comparison of PET
and fMRI perfusion is challenging due to methodological differences. In
particular, spatial resolution disparities lead to difficulties in accurate region-of-interest
(ROI) registration and partial-volume matching, which are critical for direct
comparisons. Secondly, both techniques are inherently sensitive to
physiological variations, which reduce measurement reproducibility. Carroll et al. measured interexam ASL CBF
variation in a single subject to be as high as 20 mL/100 g/min in GM and 15 mL/100 g/min in WM, while those observed with PET were 4-5 mL/100 g/min [12]. Grandin et al. observed
variations of up to 13% for PET and 16% for MR CBF measurements at rest in the
same individual [14]. As a result, high intersubject
and interexam variability between PET and MR are expected, particularly in
inexperienced volunteers scanned over several days. Thirdly, past comparisons
of CBF measurements were largely performed without functional stimulation, and
for those within the fMRI context, the impact of graded stimulus intensity [8] has not been explored. Finally,
previous ASL and PET CBF data were not always collected in similar
environments.We were interested
in evaluating the relative accuracy of the FAIR ASL method for ΔCBF measurements in comparison with PET. Previous
ASL research [19, 20] has shown
the accuracy of FAIR in determining CBF to be dependent on the transit delay
and label width, which can be variable across subjects and experimental
conditions. This has led to the introduction of techniques less sensitive to
transit delay and bolus width, such as QUIPSS II [4], and their adoption by our group [21–23] and others. However, the FAIR
technique [24] has been and continues to be
used extensively in the literature as a well-established method for the
investigation of functional hemodynamics [3, 6–8]. Notably, some widely adopted
and investigated biophysical models of the BOLD signal have been developed and
validated based on CBF data using FAIR [3, 18, 25–27]. Thus, a dedicated assessment of
the validity of FAIR for CBF measurement would be a highly valuable addition to
our knowledge.In this
study, we compare FAIR fMRI measurements of CBF with those made using PET during
graded levels of visual stimulation. In addition, we measured CBF
changes induced by hypercapnia, which has been employed to explore global,
activation-independent perfusion increases and applied to cross-subject
calibrations of the BOLD response [25]. Also, over the course of our
experiments, the conditions for ASL and PET data collection were also closely
matched and monitored.


## 2. Methods and Materials

### 2.1. Experimental Design

Visual
stimuli were generated using locally developed software (GLStim) based on the
OpenGL graphics library (Silicon Graphics, Mountain View, Calif, USA). The baseline condition
consisted of a uniform grey field, while the activation pattern was a
yellow-blue radial checkerboard with 30 spokes and 6.5 rings of equal radial
thickness, reversing contrast at 4 Hz. The checkerboard contains both color and
luminance contrast designed to produce robust local CBF increase in the primary
visual cortex (V1) [3, 8]. In an effort to maintain the subjects’
attention, a fixation task (a small arrow randomly changing directions) was
present at the centre of the field of view (FOV) throughout the scans. Subjects
were requested to continuously report the arrow direction by means of an
MR-compatible mouse.The
graded visual stimulation and hypercapnia schemes were matched to those
previously employed in calibrated fMRI studies of flow-metabolism coupling [3]. In addition to the uniform
grey-field reference condition, the subjects were presented with 4 graded
levels of visual stimulation, ranging from 25% to 100% intensity, while
inhaling atmospheric composition medical air supplied at 16 L/min. Furthermore,
mild hypercapnia (induced using air mixture of 5:21:74% CO2:O2:N2)
was used to study global CBF changes. Both PET and ASL scans included 6
sessions of 3 minutes, each consisting of one visual-respiratory condition
played out continuously. Each stimulation session was preceded and followed by
a baseline condition (Figures 1 and 2) of 1 and 2 minutes, respectively.Figure 1
(a)
The fMRI experimental protocol consisted of 6 randomly presented sessions
(baseline, hypercapnia, and 4 levels of visual stimulation). (b) The
interleaved BOLD-FAIR sequence was repeated 30 times during each run of 6
minutes, composed of a 1-minute baseline, 3-minute stimulation, and 2-minute
baseline period. Scans shaded in grey (1 minute post onset and cessation of
stimulation plus first scan in the run) were excluded in percent change
calculations to ensure that time-averaged data included only physiological
changes in steady state. (c) The basic structure of the BOLD-FAIR sequence
consists of 2 BOLD acquisitions (averaged to form 1 BOLD-contrast image)
interleaved with 1 slice selective and 1 nonselective ASL acquisition. (d)
These 4 acquisitions produce a flow-weighted FAIR image through subtraction and
a BOLD image through addition.Figure 2
(a)
The PET experimental protocol consisted of 6 randomly presented sessions
(baseline, hypercapnia, and 4 levels of visual stimulation). A tracer bolus
injection was given at the start of each block. (b) Each run has a 3-minute
acquisition period divided in 12 5-second, 6 10-second, and 3 20-second frames,
followed by 15 minutes of rest. (c) A volume of activity distribution was
acquired during each frame. (d) This series of distribution images and the
blood activity curve were fit into a kinetic model, resulting in a CBF map.A total
of 10 healthy human subjects (8 males, 2 females), aged23.9±3.3 years, were imaged under the
above six experimental conditions. Informed consent was obtained from every
subject prior to each PET and MRI scanning session, with the experimental
protocol being approved by the Research Ethics Board of the Montreal Neurological
Institute (MNI, Montreal, Canada). In order to achieve
maximal similarity between the PET and fMRI experimental conditions, the sizes
of the projected checkerboards were matched, as well as the lighting intensity
at the two imaging locations. During the scans, subjects were asked to breathe
through a nonrebreathing face mask, allowing control of the incoming air composition.


### 2.2. Magnetic Resonance Imaging

Subjects were immobilized with a foam
headrest and head restraints. A nasal cannula connected to a capnometer was
used to monitor end-tidal carbon dioxide (ETCO2) and the respiratory rate, whereas the
arterial oxygen saturation (O2Sat) and the pulse rate were measured
with a finger pulse oximeter. The
pulse and respiratory rates are indicators of blood CO2 tension. The
stimulus was presented by an adjustable back-projection mirror mounted on the
head coil.The MR
scans were performed on a 1.5 T Siemens Magnetom Vision system (Siemens, Erlangen, Germany).
To optimize the SNR of the functional data in the visual cortex, a
transmit-receive surface coil placed near the occipital lobe was used to
acquire all functional images. Thus, prior to the functional scans, a
surface-coil T1-weighted (T1W) anatomical scan, acquired at a resolution of1×1×2mm3, was used in slice selection and alignment of all
functional data. However, as the surface-coil anatomical data has highly nonuniform
intensity, registration to PET data using our local software was difficult.
Therefore, an additional high-resolution 1×1×1mm3 T1W anatomical
scan with a head coil
was also acquired to facilitate the registration of PET and surface-coil MR
data. We use the interleaved FAIR-BOLD echo-planar imaging sequence as
implemented by Hoge et al. [3] in order to directly evaluate
their measurements as well as to achieve simultaneous BOLD monitoring. Furthermore,
we selected imaging parameters to best enable replication of experimental
conditions in previous fMRI flow-metabolism studies [3, 28]. The FAIR inversion time and
echo time (TE) were 900 milliseconds and 20 milliseconds, respectively, while
the BOLD TE was 50 milliseconds. A 7-mm thick single oblique slice parallel to
the calcarine sulcus was acquired on a 64×64 matrix with a 5×5mm2 inplane voxel size. As seen in Figure 1, the repetition time of the sequence
was 12 seconds, allowing acquisition of a total of 60 frames per 6 minutes run,
30 FAIR and 30 BOLD frames. Of these 30 frames, 11 were excluded (1 minute
postonset and postcessation of stimulation plus the first scan in the run) to
ensure that only data corresponding to the physiological steady-state response was
examined. The chosen FAIR implementation minimizes errors related to the
tagging slab arrival time and width through the use of a single-slice
acquisition and a body coil inversion [8]. In order to obtain an accurate masking
location of V1 (primary visual cortex), BOLD-based retinotopic mapping was
performed in a separate session, using a visual stimulus composed of a thick
black-and-white expanding ring, also designed using GLStim [3, 28]. A total of 16 slices of 4 mm
parallel to the calcarine sulcus were acquired using a BOLD sequence during 6
randomly ordered runs of a 6-minute
visual stimulation.


### 2.3. Positron Emission Tomography

The protocol for
the PET experiments was adapted from previous PET studies aimed at reproducing
MRI results [29]. The subjects were
immobilized using a self-inflating foam headrest, which minimized motion during
scans. The stimulus was presented through an adjustable mirror mounted on the
patient table. As previously described, the O2Sat level and pulse
rate were monitored using a
finger pulse-oxymeter, while a capnometer connected via a nasal cannula
monitored the ETCO2 and the respiratory rate.
In addition, a short indwelling catheter was placed by an anesthetist into the
left radial artery for blood sampling and a more precise examination of blood
gases. A three-way stop cock allowed for simultaneous automatic (using a
locally developed sampling system for blood activity measurement) and manual
(for blood gases examination) blood sample withdrawal. Automatic blood sampling
was performed at 0.5-second intervals throughout the data collection period. A
fine needle catheter was placed into the antecubital vein of the right arm for
injection of the isotope.PET
images were acquired on an ECAT EXACT HR+ (CTI/Siemens, Knoxville, TN)
whole-body tomography system operating in three-dimensional (3D) mode. The
volumetric images were reconstructed on 128×128 matrices of 2×2mm2 pixels using
filtered back-projection with an 8-mm Hanning filter. For each of the 6 sessions, 10 mCi of H215O were injected. The H215O
isotopes were prepared in a Cyclone 18/9 cyclotron (IBA, Louvain-la-Neuve, Belgium)
adjacent to the scanner. The reconstructed images were automatically
corrected for random and scattered events, detector efficiency variation and
dead time [30, 31]. Also, a transmission scan was
collected for each subject before the experiments for estimating attenuation of
the 511 keV gamma rays as a function of tissue density [32]. A normalization scan was
acquired for eliminating effects due to ring geometry and crystal sensitivity [33]. The stimulation conditions were
presented in random order. As shown in Figure 2, during each 3-minute scan, the
subjects were presented with only one type of stimulation, which started 1
minute before the start of the scan. This delay was designed to enable
condition matching with fMRI. Arterial blood sampling and dynamic imaging
started at injection time, and each scan was followed by a 15-minute resting period,
allowing the radioisotope to decay before a new injection. Due to the short
half-life of H215O (2 minutes) and its kinetic behaviour,
the observed H215O activity changes are very fast
immediately after injection, requiring the acquisition of more frames at the
beginning of the scan. Thus, each 3-minute scan consists of 21 frames acquired
in 12 5-second intervals, followed by 6 frames at 10-second and 3 frames at
20-second intervals. Finally, due to the tracer kinetic model fitting required
for PET data, only one volumetric CBF image was obtained for each experimental
condition and no time evolution was measured.


### 2.4. Data Analysis

Flow-sensitive
MR perfusion images were obtained by subtraction of the slice-selective and
nonselective FAIR acquisitions. Subject motion, assessed by examining the
temporal standard deviation images, was deemed negligible. Quantitative
analysis of PET images was performed using the two-compartment weighted
integration method [34]. No motion correction was
performed given the longer acquisition time, due to which the effects of motion
are greatly reduced. Three different methods of selecting the ROI were
examined. The ROIs were defined on an individual basis due to intersubject
slice placement variability. In addition, for group analysis, PET (volume) and
fMRI data (single-slice) were resampled into the same reference frame,
accounting for PET’s lower image resolution.(i)V1-based ROI: The
first ROI selection criterion involved choosing only voxels within the
primary visual cortex (V1), since this region should contain the most
reliable activation for the stimulus used. V1 was defined using fMRI-based
retinotopic mapping with an eccentricity range of 5–10°, as
described previously [3, 8, 35], and resampled onto the
slice corresponding to the fMRI data for each subject. However,
retinotopic V1 regions meeting these criteria can be small, rendering the
masking process highly sensitive to misregistration between MRI and PET.
Also, a small ROI mask might produce variable results, with activation
data outside V1 ignored. Hence, other ROIs types were considered in the
analysis.(ii)t-map-based ROI: The
second type of ROI was obtained based on activation t-maps for both PET and fMRI CBF images. For fMRI, individual t-maps were calculated using fMRIstat [36].
PET t-maps were automatically
generated with the locally developed software used for PET analysis, DOT (version 1.8.0, S Milot, MNI) [37].
Both fMRI and PET t-maps were
thresholded at the 0.05 significance level to obtain the mask, accounting
for multiple comparisons for each subject. The overlap between fMRI and
PET masks was taken to be the ROI.(iii)GM-based ROI: Since
the CBF changes occur mostly in GM, a third set of ROIs, consisting of a
GM map in the fMRI occipital lobe slice, was defined for each subject. The
GM ROIs were obtained using Bayesian fuzzy classification [38] on
the high resolution anatomical MR images. This is well suited for
hypercapnia studies, which are best analyzed via the global demarcation of GM. The
occipital GM ROIs include activated visual cortical areas.In this
study, the raw FAIR fMRI images have a higher spatial resolution than PET images.
To maximize the degree of matching between the MR and PET data, the surface-coil
MR anatomical scans were first manually registered to the head-coil images
usingRegister (D MacDonald, MNI) [39], then transformed into Talairach
space. Prior to the subsequent resampling the surface-coil
fMRI data into the head-coil and stereotaxic coordinates, the images were
blurred using a 12×12mm2 FWHM (full-width-at-half-maximum) Gaussian
kernel, resulting in a resolution approximately equal to that of PET data. The postblurring
MR data was then resampled into a 1×1mm2 grid using trilinear
interpolation. Since FAIR data is single slice, PET data was transformed into
the FAIR slice space [17]. Individual PET scans were
registered to the first PET scan for each subject using an in-house
implementation of the variance-of-ratios algorithm [40]. Following this, an average PET
scan was calculated for registration of PET onto the MR anatomical space. DOT was then used to
transform the registered PET images into Talairach space. These images were subsequently
resampled onto the same slice from the fMRI data in Talairach space, and the
final sampling of the PET CBF slices corresponds to that of fMRI for each
subject. All ROI masks were also resampled into Tailarach space, with a
resolution of 1×1×1mm3.To
characterize the relationship between CBF changes measured using PET and FAIR,
a correlation analysis was performed. The initial analysis was at the level of
individual CBF changes for each subject, averaged across all voxels in the
subject’s ROIs, while the subsequent analysis was performed on the CBF data
averaged across all subjects in the individually defined ROIs.


## 2.1. Experimental Design

Visual
stimuli were generated using locally developed software (GLStim) based on the
OpenGL graphics library (Silicon Graphics, Mountain View, Calif, USA). The baseline condition
consisted of a uniform grey field, while the activation pattern was a
yellow-blue radial checkerboard with 30 spokes and 6.5 rings of equal radial
thickness, reversing contrast at 4 Hz. The checkerboard contains both color and
luminance contrast designed to produce robust local CBF increase in the primary
visual cortex (V1) [3, 8]. In an effort to maintain the subjects’
attention, a fixation task (a small arrow randomly changing directions) was
present at the centre of the field of view (FOV) throughout the scans. Subjects
were requested to continuously report the arrow direction by means of an
MR-compatible mouse.The
graded visual stimulation and hypercapnia schemes were matched to those
previously employed in calibrated fMRI studies of flow-metabolism coupling [3]. In addition to the uniform
grey-field reference condition, the subjects were presented with 4 graded
levels of visual stimulation, ranging from 25% to 100% intensity, while
inhaling atmospheric composition medical air supplied at 16 L/min. Furthermore,
mild hypercapnia (induced using air mixture of 5:21:74% CO2:O2:N2)
was used to study global CBF changes. Both PET and ASL scans included 6
sessions of 3 minutes, each consisting of one visual-respiratory condition
played out continuously. Each stimulation session was preceded and followed by
a baseline condition (Figures 1 and 2) of 1 and 2 minutes, respectively.Figure 1
(a)
The fMRI experimental protocol consisted of 6 randomly presented sessions
(baseline, hypercapnia, and 4 levels of visual stimulation). (b) The
interleaved BOLD-FAIR sequence was repeated 30 times during each run of 6
minutes, composed of a 1-minute baseline, 3-minute stimulation, and 2-minute
baseline period. Scans shaded in grey (1 minute post onset and cessation of
stimulation plus first scan in the run) were excluded in percent change
calculations to ensure that time-averaged data included only physiological
changes in steady state. (c) The basic structure of the BOLD-FAIR sequence
consists of 2 BOLD acquisitions (averaged to form 1 BOLD-contrast image)
interleaved with 1 slice selective and 1 nonselective ASL acquisition. (d)
These 4 acquisitions produce a flow-weighted FAIR image through subtraction and
a BOLD image through addition.Figure 2
(a)
The PET experimental protocol consisted of 6 randomly presented sessions
(baseline, hypercapnia, and 4 levels of visual stimulation). A tracer bolus
injection was given at the start of each block. (b) Each run has a 3-minute
acquisition period divided in 12 5-second, 6 10-second, and 3 20-second frames,
followed by 15 minutes of rest. (c) A volume of activity distribution was
acquired during each frame. (d) This series of distribution images and the
blood activity curve were fit into a kinetic model, resulting in a CBF map.A total
of 10 healthy human subjects (8 males, 2 females), aged23.9±3.3 years, were imaged under the
above six experimental conditions. Informed consent was obtained from every
subject prior to each PET and MRI scanning session, with the experimental
protocol being approved by the Research Ethics Board of the Montreal Neurological
Institute (MNI, Montreal, Canada). In order to achieve
maximal similarity between the PET and fMRI experimental conditions, the sizes
of the projected checkerboards were matched, as well as the lighting intensity
at the two imaging locations. During the scans, subjects were asked to breathe
through a nonrebreathing face mask, allowing control of the incoming air composition.


## 2.2. Magnetic Resonance Imaging

Subjects were immobilized with a foam
headrest and head restraints. A nasal cannula connected to a capnometer was
used to monitor end-tidal carbon dioxide (ETCO2) and the respiratory rate, whereas the
arterial oxygen saturation (O2Sat) and the pulse rate were measured
with a finger pulse oximeter. The
pulse and respiratory rates are indicators of blood CO2 tension. The
stimulus was presented by an adjustable back-projection mirror mounted on the
head coil.The MR
scans were performed on a 1.5 T Siemens Magnetom Vision system (Siemens, Erlangen, Germany).
To optimize the SNR of the functional data in the visual cortex, a
transmit-receive surface coil placed near the occipital lobe was used to
acquire all functional images. Thus, prior to the functional scans, a
surface-coil T1-weighted (T1W) anatomical scan, acquired at a resolution of1×1×2mm3, was used in slice selection and alignment of all
functional data. However, as the surface-coil anatomical data has highly nonuniform
intensity, registration to PET data using our local software was difficult.
Therefore, an additional high-resolution 1×1×1mm3 T1W anatomical
scan with a head coil
was also acquired to facilitate the registration of PET and surface-coil MR
data. We use the interleaved FAIR-BOLD echo-planar imaging sequence as
implemented by Hoge et al. [3] in order to directly evaluate
their measurements as well as to achieve simultaneous BOLD monitoring. Furthermore,
we selected imaging parameters to best enable replication of experimental
conditions in previous fMRI flow-metabolism studies [3, 28]. The FAIR inversion time and
echo time (TE) were 900 milliseconds and 20 milliseconds, respectively, while
the BOLD TE was 50 milliseconds. A 7-mm thick single oblique slice parallel to
the calcarine sulcus was acquired on a 64×64 matrix with a 5×5mm2 inplane voxel size. As seen in Figure 1, the repetition time of the sequence
was 12 seconds, allowing acquisition of a total of 60 frames per 6 minutes run,
30 FAIR and 30 BOLD frames. Of these 30 frames, 11 were excluded (1 minute
postonset and postcessation of stimulation plus the first scan in the run) to
ensure that only data corresponding to the physiological steady-state response was
examined. The chosen FAIR implementation minimizes errors related to the
tagging slab arrival time and width through the use of a single-slice
acquisition and a body coil inversion [8]. In order to obtain an accurate masking
location of V1 (primary visual cortex), BOLD-based retinotopic mapping was
performed in a separate session, using a visual stimulus composed of a thick
black-and-white expanding ring, also designed using GLStim [3, 28]. A total of 16 slices of 4 mm
parallel to the calcarine sulcus were acquired using a BOLD sequence during 6
randomly ordered runs of a 6-minute
visual stimulation.


## 2.3. Positron Emission Tomography

The protocol for
the PET experiments was adapted from previous PET studies aimed at reproducing
MRI results [29]. The subjects were
immobilized using a self-inflating foam headrest, which minimized motion during
scans. The stimulus was presented through an adjustable mirror mounted on the
patient table. As previously described, the O2Sat level and pulse
rate were monitored using a
finger pulse-oxymeter, while a capnometer connected via a nasal cannula
monitored the ETCO2 and the respiratory rate.
In addition, a short indwelling catheter was placed by an anesthetist into the
left radial artery for blood sampling and a more precise examination of blood
gases. A three-way stop cock allowed for simultaneous automatic (using a
locally developed sampling system for blood activity measurement) and manual
(for blood gases examination) blood sample withdrawal. Automatic blood sampling
was performed at 0.5-second intervals throughout the data collection period. A
fine needle catheter was placed into the antecubital vein of the right arm for
injection of the isotope.PET
images were acquired on an ECAT EXACT HR+ (CTI/Siemens, Knoxville, TN)
whole-body tomography system operating in three-dimensional (3D) mode. The
volumetric images were reconstructed on 128×128 matrices of 2×2mm2 pixels using
filtered back-projection with an 8-mm Hanning filter. For each of the 6 sessions, 10 mCi of H215O were injected. The H215O
isotopes were prepared in a Cyclone 18/9 cyclotron (IBA, Louvain-la-Neuve, Belgium)
adjacent to the scanner. The reconstructed images were automatically
corrected for random and scattered events, detector efficiency variation and
dead time [30, 31]. Also, a transmission scan was
collected for each subject before the experiments for estimating attenuation of
the 511 keV gamma rays as a function of tissue density [32]. A normalization scan was
acquired for eliminating effects due to ring geometry and crystal sensitivity [33]. The stimulation conditions were
presented in random order. As shown in Figure 2, during each 3-minute scan, the
subjects were presented with only one type of stimulation, which started 1
minute before the start of the scan. This delay was designed to enable
condition matching with fMRI. Arterial blood sampling and dynamic imaging
started at injection time, and each scan was followed by a 15-minute resting period,
allowing the radioisotope to decay before a new injection. Due to the short
half-life of H215O (2 minutes) and its kinetic behaviour,
the observed H215O activity changes are very fast
immediately after injection, requiring the acquisition of more frames at the
beginning of the scan. Thus, each 3-minute scan consists of 21 frames acquired
in 12 5-second intervals, followed by 6 frames at 10-second and 3 frames at
20-second intervals. Finally, due to the tracer kinetic model fitting required
for PET data, only one volumetric CBF image was obtained for each experimental
condition and no time evolution was measured.


## 2.4. Data Analysis

Flow-sensitive
MR perfusion images were obtained by subtraction of the slice-selective and
nonselective FAIR acquisitions. Subject motion, assessed by examining the
temporal standard deviation images, was deemed negligible. Quantitative
analysis of PET images was performed using the two-compartment weighted
integration method [34]. No motion correction was
performed given the longer acquisition time, due to which the effects of motion
are greatly reduced. Three different methods of selecting the ROI were
examined. The ROIs were defined on an individual basis due to intersubject
slice placement variability. In addition, for group analysis, PET (volume) and
fMRI data (single-slice) were resampled into the same reference frame,
accounting for PET’s lower image resolution.(i)V1-based ROI: The
first ROI selection criterion involved choosing only voxels within the
primary visual cortex (V1), since this region should contain the most
reliable activation for the stimulus used. V1 was defined using fMRI-based
retinotopic mapping with an eccentricity range of 5–10°, as
described previously [3, 8, 35], and resampled onto the
slice corresponding to the fMRI data for each subject. However,
retinotopic V1 regions meeting these criteria can be small, rendering the
masking process highly sensitive to misregistration between MRI and PET.
Also, a small ROI mask might produce variable results, with activation
data outside V1 ignored. Hence, other ROIs types were considered in the
analysis.(ii)t-map-based ROI: The
second type of ROI was obtained based on activation t-maps for both PET and fMRI CBF images. For fMRI, individual t-maps were calculated using fMRIstat [36].
PET t-maps were automatically
generated with the locally developed software used for PET analysis, DOT (version 1.8.0, S Milot, MNI) [37].
Both fMRI and PET t-maps were
thresholded at the 0.05 significance level to obtain the mask, accounting
for multiple comparisons for each subject. The overlap between fMRI and
PET masks was taken to be the ROI.(iii)GM-based ROI: Since
the CBF changes occur mostly in GM, a third set of ROIs, consisting of a
GM map in the fMRI occipital lobe slice, was defined for each subject. The
GM ROIs were obtained using Bayesian fuzzy classification [38] on
the high resolution anatomical MR images. This is well suited for
hypercapnia studies, which are best analyzed via the global demarcation of GM. The
occipital GM ROIs include activated visual cortical areas.In this
study, the raw FAIR fMRI images have a higher spatial resolution than PET images.
To maximize the degree of matching between the MR and PET data, the surface-coil
MR anatomical scans were first manually registered to the head-coil images
usingRegister (D MacDonald, MNI) [39], then transformed into Talairach
space. Prior to the subsequent resampling the surface-coil
fMRI data into the head-coil and stereotaxic coordinates, the images were
blurred using a 12×12mm2 FWHM (full-width-at-half-maximum) Gaussian
kernel, resulting in a resolution approximately equal to that of PET data. The postblurring
MR data was then resampled into a 1×1mm2 grid using trilinear
interpolation. Since FAIR data is single slice, PET data was transformed into
the FAIR slice space [17]. Individual PET scans were
registered to the first PET scan for each subject using an in-house
implementation of the variance-of-ratios algorithm [40]. Following this, an average PET
scan was calculated for registration of PET onto the MR anatomical space. DOT was then used to
transform the registered PET images into Talairach space. These images were subsequently
resampled onto the same slice from the fMRI data in Talairach space, and the
final sampling of the PET CBF slices corresponds to that of fMRI for each
subject. All ROI masks were also resampled into Tailarach space, with a
resolution of 1×1×1mm3.To
characterize the relationship between CBF changes measured using PET and FAIR,
a correlation analysis was performed. The initial analysis was at the level of
individual CBF changes for each subject, averaged across all voxels in the
subject’s ROIs, while the subsequent analysis was performed on the CBF data
averaged across all subjects in the individually defined ROIs.


## 3. Results

### 3.1. Physiological Monitoring

Overall,
the pulse rate was not influenced by the air composition. It ranged from 55.6
to 58.3 beats/min (subject average) throughout the experiment. The subjects
also maintained a reasonably constant respiratory rate, between 15.7 and 17.9
breaths/min. While ETCO2 was stable at 40.2–42.1 mmHg during the
five normocapnia conditions, a small ETCO2 increase of about 4 to 6 mmHg was observed during hypercapnia, in agreement with rise in pressure
introduced by CO2. Finally, arterial saturation of O2 remained constant at 98-99% throughout
all the sessions.


### 3.2. Regions of Interest

The three
ROIs used for the ΔCBF comparisons were defined on a subject basis,
as described in Methods and Materials, and an example is shown in Figure3. For
all subjects, the V1 ROIs were the smallest, containing only between 0.101cc
and 0.187cc, due to the stringent retinotopic mapping criteria. V1 was
correctly identified by the ROIs, but due to the small mask size, the
corresponding ΔCBF measurements are prone to variations
introduced by misregistration and motion. On the other hand, the t-map ROIs contained between 0.202cc and
0.407cc. To obtain the t-map ROI’s at P=.05, the fMRI t-maps were thresholded at 5.42, and the PET maps at 4.45. A lower
standard deviation was seen in the responses detected in these ROIs, and the
FAIR t-maps contained no
statistically significant voxels for 2 of the 10 subjects. The largest were the
GM ROIs, covering 1.489-0.625 cc for the same group of subjects. Some
automatically classified GM voxels were excluded by the certainty-level
threshold, but the final GM ROIs were still significantly larger than the V1
ROIs. As previously mentioned, larger ROI masks may include nonactivated
voxels, leading to reduced levels of measured ΔCBF. Nonetheless, the GM masks provide an
effective means of comparing CBF measurement across modalities, especially for
the hypercapnia condition.Sample ROIs (shown in red) defined for one
subject. The V1 ROI was obtained by retinotopic mapping, thet-map ROI included common voxels from
thresholded and resampled FAIR and PET t-maps,
and the grey matter (GM) ROI was defined by Bayesian classification of the
anatomical structures.
(a)(b)(c)


### 3.3. Activation Time-Course

Time-series
CBF data were obtained only from fMRI data as PET measurements were not
available as separate frames. The BOLD and FAIR time courses were obtained by
averaging all voxels in the ROI and shown in Figure4 in the V1 ROI of one
subject (activation paradigm from left to right: baseline, checkerboards from
25% to 100% intensity and hypercapnia). We observed an increase in the level of
signal change with increasing checkerboard intensity for both BOLD and FAIR. As
expected, the activation SNR, defined as the ratio of the ROI mean over the
standard deviation during activation, was lower for FAIR than for BOLD.BOLD (a) and FAIR (b) time courses
of the CBF percent change in the V1 ROI averaged over 10 subjects are shown,
from left to right, corresponding to baseline, 25%, 50%, 75%, and 100%
intensity checkerboards and 5% hypercapnia. The horizontal bars represent the
3-minute periods during which the stimulus was on. An increase in signal change
can be observed with increasing checkerboard intensity for BOLD and FAIR
perfusion, ranging between 1.0%–1.9% and 11.4%–22.5%, respectively.
(a)(b)In the
averaged time courses, FAIR ΔCBF ranged between 11.4% and 22.5% for the
various stimulation intensities, and BOLD changes between 1.0% and 1.9%. These
are in the range of percent changes previously reported by Hoge et al. using the same stimulation and acquisition design (between 1.1%
and 2.2% for BOLD, and between 23% and 48% for FAIR) [3, 28]. The measured percent changes in
small ROIs such as the V1 were expected to decrease with the application of
image blurring. This was true for average FAIR ΔCBF in V1, where lower values (between 16.4% and
30.4%) were measured compared to the study by Hoge et al. (where no
blurring was applied). Comparatively lower signal changes were also measured
during hypercapnia (1.5% and 7.2% for BOLD and FAIR, resp., compared to 2.5%
and 21%, resp., in previous studies [3, 8]), despite similarity in observed
ETCO2 changes. However, our postblurring BOLD percent changes in the
V1 ROI (between 1.1% and 2.1%) were still similar to those obtained by Hoge et al.


### 3.4. FAIR versus PET

Relative
PET CBF changes were calculated from absolute PET CBF values and compared with
relative FAIR ΔCBF changes. PET ΔCBF values in the V1 ROIs for all 10 subjects,
as well as averaged ΔCBF over all subjects in the 3 ROI types, are
presented in Table1. The results are in the range of published ΔCBF values [3]. However, the PET ΔCBF measurements have a high standard
deviation, largely attributable to the abnormally high ΔCBF measured in subject 8. As previously
mentioned, ΔCBF time courses could not be obtained for PET
data; instead, time-averaged ΔCBF in the PET ROIs were compared to individual
and group-averaged FAIR results. The PET ΔCBF maps were found to have significantly lower
SNR than FAIR (<1.0 for V1 and GM ROIs, and <2.27 for t-map ROIs) for all the experimental
conditions, as seen in Table 1, and to demonstrate considerable intersubject
variability under each condition. We further noted that ΔCBF for subject 10 in the V1 and GM ROIs were
negative for PET (possibly due to a relatively high PET CBF measured at
baseline) and only very slightly positive for fMRI (the subject having no
significant voxels in the t-maps
ROI). Data from this subject is likely to account for much of the standard
deviation seen in both techniques.Table 1
CBF measured with PET in 10 subjects under 6
experimental conditions (baseline, 4 levels of graded visual checkerboard (CHB)
stimulation as well as hypercapnia (HC)) in the V1 ROI, followed by the CBF
measurements averaged over the 10 subjects in all 3 ROIs—V1,t-map, and GM ROIs. The PET ΔCBF measurements have a high standard
deviation, largely attributable to the abnormally high CBF measured in subject
8.
SubjectBaseline25% CHB50% CHB75% CHB100% CHB5% HCmean CBF in V1 ROI for all 10 subjects (mL/100 g tissue/min)147.762.550.549.061.346.8234.654.453.443.152.438.1332.658.342.648.655.939.8442.450.455.345.152.060.2536.851.663.039.643.438.4639.546.474.759.662.650.7762.572.562.964.368.665.08114.3139.5137.8164.3148.7115.9966.180.253.666.6576.466.61050.036.645.437.131.835.1Average mean ± std CBF in VI ROI52.6±24.465.2±28.963.9±27.661.7±37.465.3±31.955.6±24.1Average mean  ±std CBF int-map ROI53.6±24.570.6±27.971.3±27.068.9±37.673.8±30.260.0±23.2Average mean ± std CBF in GM ROI47.0±22.352.8±23.251.7±20.450.3±26.852.8±23.949.8±21.0The ΔCBF values measured with FAIR and PET in all
the ROIs during baseline and 4 graded levels of visual stimulation are
presented in Figure5. PET data is shown as having much higher standard
deviation than FAIR measurements, even when the GM ROIs were used. The group-averaged
results seem to provide a better indication of the CBF changes. Table 2 summarizes
the FAIR and PET ΔCBF group averages. The FAIR baseline signal
value was obtained from the 6-minute baseline data, whereas in PET, since only
one baseline volume was obtained, the baseline percent change was fixed at 0%.
The regions of activation-induced ΔCBF in both the V1 and t-map ROIs were localized to the expected site of activation.
However, as seen in Figure 6, the ΔCBF in the GM ROIs are lower, since many less than
maximally activated voxels are likely to have been included in the mask. In
addition, a postblurring data resolution of 14×14mm2 implies that
both GM and WM contribution can be expected in the same voxel; the GM signal
intensity thus diminished in both PET and FAIR [3]. We further observed that for
all ROIs, increases of FAIR ΔCBF appear to correspond well with increases in
visual stimulation intensity, in agreement with previous observations [8]. This was not the case for PET
data. Finally, for the hypercapnic condition, ΔCBF in all three ROIs were similar for FAIR and
PET.Table 2
FAIR and PET CBF percent changes
averaged over all subjects (mean ±std), for all conditions (baseline, 4 levels of visual
checkboard (CHB) stimulation, and hypercapnia (HC)), in the V1,t-map, and GM ROIs. For PET, the
baseline percent change was fixed at 0%, since only one baseline volume was
acquired.
Baseline25% CHB50% CHB75% CHB100% CHB5% HCΔCBF (%)ROI: retinotopically defined VI (mean ± std)FAIR0.6±2.611.3±5.715.0±8.117.5±9.422.5±11.28.3±6.2PET0.0±0.027.6±27.927.5±35.316.3±25.026.9±30.08.1±19.6ROI: PET and FAIRt-map overlap (mean ± std)FAIR1.1±2.714.0±8.720.1±8.323.5±9.129.1±8.810.5±7.1PET0.0±0.035.2±18.238.4±25.527.7±22.141.6±18.315.7±18.0ROI: occipital lobe grey matter (mean ± std)FAIR0.9±3.06.1±4.48.6±4.09.5±4.912.8±5.17.9±6.2PET0.0±0.015.0±17.815.7±28.27.3±18.316.0±23.29.7±23.0FMRI (top) and PET (bottom) CBF percent
changes during visual stimulation in the V1,t-map, and GM ROIs. Error bars represent the standard deviation in
the data. Experimental conditions 1 to 5: baseline, 25%, 50%, 75%, and 100%
intensity checkerboard visual stimulation. The FAIR CBF maps have considerably
higher SNRs than PET maps, and there was a consistent monotonic increase in the
CBF percent change (ΔCBF) with stimulus intensity in the FAIR data
which could not be discerned distinctively in PET given the limited SNR.
(a)(b)(c)(d)(e)(f)Correlation plots of group-average fMRI CBF
percent changes with respect to PET CBF changes in the V1,t-map, and GM ROIs. The dots represent results from baseline and 4
levels of visual stimulation, and the triangles represent the results for the
5% hypercapnia condition. The equations resulted from the linear fitting of the
data, represented by the dotted lines. The hypercapnic ΔCBF measured using PET and MR are similar. In
all 3 cases, the χ2 values have probabilities well
within the range of acceptance, and t-test
results indicate the slope of the linear fit is not significantly different
from unity.
(a)(b)(c)


### 3.5. Correlation Analysis

In
Figure6, group-averaged ΔCBF values for baseline and the 4 visual
stimulation conditions are shown as dots, while the hypercapnic ΔCBF is represented by a triangle. The
hypercapnic ΔCBF values were quite consistent between PET
and MR measurements. Note that for all experimental conditions, higher
correlation was seen when comparing the PET and MR group averages (Figure 6) than
when comparing the two sets of individual subject values (0.76, 0.87, 0.73 versus
0.45, 0.29, 0.57 for the V1, t-map
and GM ROIs, resp.). The line-fitting algorithm used accounted for data
variance on both the PET and the ASL axis [3], and the resulting slopes in all
3 ROI types were similar. χ2 values had high probabilities (q  =  92%–98%), well below the threshold
for rejection of the fit (9.488 for 4 degrees of freedom at P=.05). Furthermore, the two-tailed t-test was used to assess whether the
slopes were significantly different from unity. The t-values obtained for all 3 ROIs (−0.10, −0.35, and −0.06 for the
V1, t-map, and GM ROIs, resp.) suggest
that the slopes of the MR-PET linear fits do not differ statistically from
unity.


## 3.1. Physiological Monitoring

Overall,
the pulse rate was not influenced by the air composition. It ranged from 55.6
to 58.3 beats/min (subject average) throughout the experiment. The subjects
also maintained a reasonably constant respiratory rate, between 15.7 and 17.9
breaths/min. While ETCO2 was stable at 40.2–42.1 mmHg during the
five normocapnia conditions, a small ETCO2 increase of about 4 to 6 mmHg was observed during hypercapnia, in agreement with rise in pressure
introduced by CO2. Finally, arterial saturation of O2 remained constant at 98-99% throughout
all the sessions.


## 3.2. Regions of Interest

The three
ROIs used for the ΔCBF comparisons were defined on a subject basis,
as described in Methods and Materials, and an example is shown in Figure3. For
all subjects, the V1 ROIs were the smallest, containing only between 0.101cc
and 0.187cc, due to the stringent retinotopic mapping criteria. V1 was
correctly identified by the ROIs, but due to the small mask size, the
corresponding ΔCBF measurements are prone to variations
introduced by misregistration and motion. On the other hand, the t-map ROIs contained between 0.202cc and
0.407cc. To obtain the t-map ROI’s at P=.05, the fMRI t-maps were thresholded at 5.42, and the PET maps at 4.45. A lower
standard deviation was seen in the responses detected in these ROIs, and the
FAIR t-maps contained no
statistically significant voxels for 2 of the 10 subjects. The largest were the
GM ROIs, covering 1.489-0.625 cc for the same group of subjects. Some
automatically classified GM voxels were excluded by the certainty-level
threshold, but the final GM ROIs were still significantly larger than the V1
ROIs. As previously mentioned, larger ROI masks may include nonactivated
voxels, leading to reduced levels of measured ΔCBF. Nonetheless, the GM masks provide an
effective means of comparing CBF measurement across modalities, especially for
the hypercapnia condition.Sample ROIs (shown in red) defined for one
subject. The V1 ROI was obtained by retinotopic mapping, thet-map ROI included common voxels from
thresholded and resampled FAIR and PET t-maps,
and the grey matter (GM) ROI was defined by Bayesian classification of the
anatomical structures.
(a)(b)(c)


## 3.3. Activation Time-Course

Time-series
CBF data were obtained only from fMRI data as PET measurements were not
available as separate frames. The BOLD and FAIR time courses were obtained by
averaging all voxels in the ROI and shown in Figure4 in the V1 ROI of one
subject (activation paradigm from left to right: baseline, checkerboards from
25% to 100% intensity and hypercapnia). We observed an increase in the level of
signal change with increasing checkerboard intensity for both BOLD and FAIR. As
expected, the activation SNR, defined as the ratio of the ROI mean over the
standard deviation during activation, was lower for FAIR than for BOLD.BOLD (a) and FAIR (b) time courses
of the CBF percent change in the V1 ROI averaged over 10 subjects are shown,
from left to right, corresponding to baseline, 25%, 50%, 75%, and 100%
intensity checkerboards and 5% hypercapnia. The horizontal bars represent the
3-minute periods during which the stimulus was on. An increase in signal change
can be observed with increasing checkerboard intensity for BOLD and FAIR
perfusion, ranging between 1.0%–1.9% and 11.4%–22.5%, respectively.
(a)(b)In the
averaged time courses, FAIR ΔCBF ranged between 11.4% and 22.5% for the
various stimulation intensities, and BOLD changes between 1.0% and 1.9%. These
are in the range of percent changes previously reported by Hoge et al. using the same stimulation and acquisition design (between 1.1%
and 2.2% for BOLD, and between 23% and 48% for FAIR) [3, 28]. The measured percent changes in
small ROIs such as the V1 were expected to decrease with the application of
image blurring. This was true for average FAIR ΔCBF in V1, where lower values (between 16.4% and
30.4%) were measured compared to the study by Hoge et al. (where no
blurring was applied). Comparatively lower signal changes were also measured
during hypercapnia (1.5% and 7.2% for BOLD and FAIR, resp., compared to 2.5%
and 21%, resp., in previous studies [3, 8]), despite similarity in observed
ETCO2 changes. However, our postblurring BOLD percent changes in the
V1 ROI (between 1.1% and 2.1%) were still similar to those obtained by Hoge et al.


## 3.4. FAIR versus PET

Relative
PET CBF changes were calculated from absolute PET CBF values and compared with
relative FAIR ΔCBF changes. PET ΔCBF values in the V1 ROIs for all 10 subjects,
as well as averaged ΔCBF over all subjects in the 3 ROI types, are
presented in Table1. The results are in the range of published ΔCBF values [3]. However, the PET ΔCBF measurements have a high standard
deviation, largely attributable to the abnormally high ΔCBF measured in subject 8. As previously
mentioned, ΔCBF time courses could not be obtained for PET
data; instead, time-averaged ΔCBF in the PET ROIs were compared to individual
and group-averaged FAIR results. The PET ΔCBF maps were found to have significantly lower
SNR than FAIR (<1.0 for V1 and GM ROIs, and <2.27 for t-map ROIs) for all the experimental
conditions, as seen in Table 1, and to demonstrate considerable intersubject
variability under each condition. We further noted that ΔCBF for subject 10 in the V1 and GM ROIs were
negative for PET (possibly due to a relatively high PET CBF measured at
baseline) and only very slightly positive for fMRI (the subject having no
significant voxels in the t-maps
ROI). Data from this subject is likely to account for much of the standard
deviation seen in both techniques.Table 1
CBF measured with PET in 10 subjects under 6
experimental conditions (baseline, 4 levels of graded visual checkerboard (CHB)
stimulation as well as hypercapnia (HC)) in the V1 ROI, followed by the CBF
measurements averaged over the 10 subjects in all 3 ROIs—V1,t-map, and GM ROIs. The PET ΔCBF measurements have a high standard
deviation, largely attributable to the abnormally high CBF measured in subject
8.
SubjectBaseline25% CHB50% CHB75% CHB100% CHB5% HCmean CBF in V1 ROI for all 10 subjects (mL/100 g tissue/min)147.762.550.549.061.346.8234.654.453.443.152.438.1332.658.342.648.655.939.8442.450.455.345.152.060.2536.851.663.039.643.438.4639.546.474.759.662.650.7762.572.562.964.368.665.08114.3139.5137.8164.3148.7115.9966.180.253.666.6576.466.61050.036.645.437.131.835.1Average mean ± std CBF in VI ROI52.6±24.465.2±28.963.9±27.661.7±37.465.3±31.955.6±24.1Average mean  ±std CBF int-map ROI53.6±24.570.6±27.971.3±27.068.9±37.673.8±30.260.0±23.2Average mean ± std CBF in GM ROI47.0±22.352.8±23.251.7±20.450.3±26.852.8±23.949.8±21.0The ΔCBF values measured with FAIR and PET in all
the ROIs during baseline and 4 graded levels of visual stimulation are
presented in Figure5. PET data is shown as having much higher standard
deviation than FAIR measurements, even when the GM ROIs were used. The group-averaged
results seem to provide a better indication of the CBF changes. Table 2 summarizes
the FAIR and PET ΔCBF group averages. The FAIR baseline signal
value was obtained from the 6-minute baseline data, whereas in PET, since only
one baseline volume was obtained, the baseline percent change was fixed at 0%.
The regions of activation-induced ΔCBF in both the V1 and t-map ROIs were localized to the expected site of activation.
However, as seen in Figure 6, the ΔCBF in the GM ROIs are lower, since many less than
maximally activated voxels are likely to have been included in the mask. In
addition, a postblurring data resolution of 14×14mm2 implies that
both GM and WM contribution can be expected in the same voxel; the GM signal
intensity thus diminished in both PET and FAIR [3]. We further observed that for
all ROIs, increases of FAIR ΔCBF appear to correspond well with increases in
visual stimulation intensity, in agreement with previous observations [8]. This was not the case for PET
data. Finally, for the hypercapnic condition, ΔCBF in all three ROIs were similar for FAIR and
PET.Table 2
FAIR and PET CBF percent changes
averaged over all subjects (mean ±std), for all conditions (baseline, 4 levels of visual
checkboard (CHB) stimulation, and hypercapnia (HC)), in the V1,t-map, and GM ROIs. For PET, the
baseline percent change was fixed at 0%, since only one baseline volume was
acquired.
Baseline25% CHB50% CHB75% CHB100% CHB5% HCΔCBF (%)ROI: retinotopically defined VI (mean ± std)FAIR0.6±2.611.3±5.715.0±8.117.5±9.422.5±11.28.3±6.2PET0.0±0.027.6±27.927.5±35.316.3±25.026.9±30.08.1±19.6ROI: PET and FAIRt-map overlap (mean ± std)FAIR1.1±2.714.0±8.720.1±8.323.5±9.129.1±8.810.5±7.1PET0.0±0.035.2±18.238.4±25.527.7±22.141.6±18.315.7±18.0ROI: occipital lobe grey matter (mean ± std)FAIR0.9±3.06.1±4.48.6±4.09.5±4.912.8±5.17.9±6.2PET0.0±0.015.0±17.815.7±28.27.3±18.316.0±23.29.7±23.0FMRI (top) and PET (bottom) CBF percent
changes during visual stimulation in the V1,t-map, and GM ROIs. Error bars represent the standard deviation in
the data. Experimental conditions 1 to 5: baseline, 25%, 50%, 75%, and 100%
intensity checkerboard visual stimulation. The FAIR CBF maps have considerably
higher SNRs than PET maps, and there was a consistent monotonic increase in the
CBF percent change (ΔCBF) with stimulus intensity in the FAIR data
which could not be discerned distinctively in PET given the limited SNR.
(a)(b)(c)(d)(e)(f)Correlation plots of group-average fMRI CBF
percent changes with respect to PET CBF changes in the V1,t-map, and GM ROIs. The dots represent results from baseline and 4
levels of visual stimulation, and the triangles represent the results for the
5% hypercapnia condition. The equations resulted from the linear fitting of the
data, represented by the dotted lines. The hypercapnic ΔCBF measured using PET and MR are similar. In
all 3 cases, the χ2 values have probabilities well
within the range of acceptance, and t-test
results indicate the slope of the linear fit is not significantly different
from unity.
(a)(b)(c)


## 3.5. Correlation Analysis

In
Figure6, group-averaged ΔCBF values for baseline and the 4 visual
stimulation conditions are shown as dots, while the hypercapnic ΔCBF is represented by a triangle. The
hypercapnic ΔCBF values were quite consistent between PET
and MR measurements. Note that for all experimental conditions, higher
correlation was seen when comparing the PET and MR group averages (Figure 6) than
when comparing the two sets of individual subject values (0.76, 0.87, 0.73 versus
0.45, 0.29, 0.57 for the V1, t-map
and GM ROIs, resp.). The line-fitting algorithm used accounted for data
variance on both the PET and the ASL axis [3], and the resulting slopes in all
3 ROI types were similar. χ2 values had high probabilities (q  =  92%–98%), well below the threshold
for rejection of the fit (9.488 for 4 degrees of freedom at P=.05). Furthermore, the two-tailed t-test was used to assess whether the
slopes were significantly different from unity. The t-values obtained for all 3 ROIs (−0.10, −0.35, and −0.06 for the
V1, t-map, and GM ROIs, resp.) suggest
that the slopes of the MR-PET linear fits do not differ statistically from
unity.


## 4. Discussion

Arterial spin
labeling (ASL)-based perfusion fMRI techniques are fast, noninvasive, have high
resolution (temporal and spatial), and are widely accessible. Furthermore, ASL
can easily be used in conjunction with other MR techniques to provide
information on a variety of physiological parameters in a single session. The
goal of this study was to compare PET and FAIR ASL, under conditions
extensively used in numerous brain flow-metabolism studies [3, 25, 26, 41]. The
FAIR and PET data were fit to a straight line taking into account the
variations in both modalities. The resulting slopes were not significantly
different from unity, though large standard deviations were associated with the
fit. Furthermore, our results showed a consistent monotonic increase in the CBF
percent change (ΔCBF) with stimulus intensity in the FAIR CBF
data which could not be observed consistently with PET due to its low SNR. On
average, FAIR ΔCBF values were slightly lower than those
measured with PET under the same conditions. Also, a lower SNR was observed in
the FAIR group-average time course, likely due to intersubject variability and
group outliers. Despite these differences, the regional ΔCBF values measured with FAIR were highly
correlated with PET measurements.When
comparing PET and fMRI data, a key step was the transformation of both datasets
into the same frame of reference and to have the same spatial resolution, since
the numerous registration steps could potentially introduce errors. First of
all, PET data had to be aligned between runs and also to the surface-coil fMRI
data, which was in turn manually registered to the head-coil MR anatomical
images through a process susceptible to intersubject variations. Subsequently,
PET and MR scans were transformed into Talairach space along with the chosen
ROI masks, allowing for additional image degradation. Moreover, since the fMRI
data consisted of a single slice, no blurring could be performed in the slice
direction, and hence the slice resolution of the FAIR images did not match that
of the PET volume data. In some cases where the fMRI slice may have been
positioned above the activated area, PET data would reflect activation while
the FAIR data may not, resulting in FAIR ΔCBF values being lower than those of PET. This
may be one of the sources of the systematic ΔCBF underestimation using this single-slice
FAIR implementation. Furthermore, since the scan sessions were usually on
different days, measurements are prone to various intrasubject variations. In
fact, intrasubject variability as high as 16% has been reported in the literature
for trained subjects [12, 14], and simple factors such as
caffeine intake can alter the CBF response. Other sources of errors include
subject motion and respiration. PET data were acquired with lower temporal
resolution and over longer scan periods, thus the effects of motion would be
reduced through data averaging. In addition, as all PET scans were aligned to
the first run, the effect of potential movement between runs was greatly
reduced. Although this process does not account for motion within runs, the
need for in-plane motion correction was reduced due to the use of image blurring.
On the other hand, each fMRI image was acquired in <100 milliseconds, and
motion could have potentially shifted the location of the activated region out
of the ROI mask, resulting in an underestimation of the activation as well as
diminished SNR. Complete 3D retrospective motion correction was not possible
for the MR data, since the surrounding regions were not scanned, but visual
examination suggested minimal displacement between runs or between frames in
one run. Nonetheless, for both PET and MR, intersession positioning differences
could result in a slight data misalignment and therefore comparison of slightly
shifted brain regions.An
additional source of potential error could originate from the experimental
setup. Although the PET experiments were designed to reproduce the conditions
found in the MR environment as closely as possible, some elements in the
experimental setup were difficult to reproduce. These include factors such as
head and mirror orientations, sensory stimulation induced by MR scanner noise
and vibration, the presence of arterial and venous lines during the PET
experiment, and the duration of the study. Changes in mirror orientation might
occur between subjects and scans. While this should not have a large impact on
the activation if the subject maintains fixation on the centre, the quality of
the subjects’ attention could be influenced by the degree of ease with which
the stimulus was viewed. Sensory stimulation from the high background noise and
vibration in the MR scanner, absent in the PET environment, could have added an
additional variability. This factor was, however, previously reported to have
little impact on the analysis [3]. On the other hand, in the PET
experiments, the stress associated with having an arterial line and multiple
injections could also have affected the subject’s response. Moreover, the scan
durations for PET and fMRI were different; subject motion generally increases
during longer scans. In the fMRI experiments, nearly 36 minutes of continuous
stimulation was used with no breaks between runs, whereas in PET, the scan
sessions were shorter (4 minutes), and the subjects were given breaks,
relieving strain on subject attention.As we
mentioned, the dependence of FAIR CBF estimates on transit delay as well as
label width has been previously discussed [3, 19]. The transit delay is influenced
by the label gap size, and an underestimation of the delay leads to CBF
underestimation. The quantification and
correction of the underestimation requires ASL acquisitions at a range of
different inversion times for each flow condition. However, such measurements could not be
included in this study given the number of graded flow increases being
investigated. Instead,
in the FAIR implementation used in this study, we made an effort to minimize the
influence of transit delays by using a substantially smaller gap size (3 mm)
than typically reported in the literature [3, 20]. In addition, we used body coil
transmission to achieve a very large label width, thereby minimizing CBF
estimation errors due to labeling slab size.Finally,
it should be noted that while the single-slice FAIR sequence we used did not
allow the measurement of absolute CBF, the literature has recently described various
quantitative and multislice ASL sequences such as Q2TIPS [5] and QUIPSS II with BASSI pulse
tagging [21, 42], which would better suit future
fMRI studies. Multislice perfusion sequences provide a definite advantage when
studying large ROIs, reducing the dependence on slice placement, facilitating
visualization of global CBF changes, and benefiting the clinical utility of ASL
perfusion imaging. In addition, multichannel acquisitions and higher field
strengths can provide higher global SNR while abolishing the need for
surface-coil functional scans. This would reduce the number of steps needed for
registration of fMRI and PET data by eliminating the subject-dependent manual
registration step between head and surface coil anatomical scans. Improved data
SNR and reduced postprocessing variability would permit further exploration of ΔCBF variation with stimulation intensity.In
recently published studies involving CBF measurements by our group [22, 23], the QUIPSS II technique was
employed [21, 42]. However, as stated earlier, the
current study was motivated by the need to assess the accuracy and validity of the
FAIR method with its known technical limitations, particularly given the importance
attached to results published in recent years based on FAIR measurements [3, 18, 24, 25, 27]. In
addition, we wanted to address the intense interest in a comprehensive
evaluation of the relative quality of perfusion imaging using MRI and PET, the
latter being the de facto
golden standard technique for perfusion imaging. The chief finding of the
present study is that there was no significant difference between measurements
of CBF change using PET and FAIR under matched levels of graded visual
stimulation and hypercapnia. These findings directly support the argument that
FAIR, while leaving room for improvement, provides a valid measure of CBF
changes under our experimental conditions, characterized by an accuracy well within
the range measured using H215O PET. The other important
observation is that CBF measurements made using PET have a much lower SNR than
those made using ASL-fMRI, stresses the immense importance in validating the
latter for a wide array of applications.



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