These glucose measurements, however, were based on a static FDG protocol together with population average-based values for the rate constants. The use of an arterial line blood sampler derived input function BSIF to acquire an input function is the gold standard for dynamic data analysis of cerebral studies, but it is an invasive procedure.
Therefore, use of an image-derived input function IDIF can be an interesting alternative [ 18 — 20 ], but its utility needs to be validated for each tracer, each scanner, and each acquisition and data analysis protocol separately. The main purpose of the present study was to derive human CBF and CMR glu values as measured using a current state-of-the-art high-resolution scanner.
In addition, CBF and K i values for automatically delineated regions were compared with manually drawn regions of interest and literature values. Based on earlier work on IDIFs [ 18 , 20 , 21 ] and given the high resolution of the HRRT, an additional objective was to assess whether a carotid artery-based IDIF could be used as a noninvasive alternative for arterial sampling in the case of both CBF and CMR glu measurements, thereby increasing clinical applicability of this methodology in humans.
Cerebral Blood Flow and Metabolism Measurement
Thirteen healthy men age The study consisted of a screening visit and two visits for magnetic resonance imaging MRI and PET scan acquisition, respectively. All subjects were free of medical and psychiatric illness based on medical history, physical examination, and blood analysis. All subjects provided written informed consent prior to inclusion.
At the day of the PET study, catheters were placed in the antecubital vein for tracer injection and in the radial artery for blood sampling. Next, subjects were positioned on the HRRT scanner bed such that the head was in the center of the field of view. Velcro tapes were used to minimize patient movement during the entire imaging procedure. Lights were dimmed, noise was minimized, and subjects were asked to close their eyes and stay awake during data acquisition.
Prior to or immediately after the 15 O]H 2 O scan, a 6-min singles-based transmission scan with a fan-collimated Cs moving point source was acquired. A min dynamic emission scan was started 10 s prior to a bolus injection of approximately MBq 15 O]H 2 O. The administration protocol was identical to the one used for the 15 O]H 2 O scan. During both emission scans, arterial blood concentrations were monitored continuously using a dedicated on-line blood sampler Veenstra Instruments, Joure, Netherlands [ 22 ].
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In addition, three manual blood samples were taken at 5, 7. These samples were taken from the same arterial line by briefly interrupting continuous withdrawal. After each sample, the arterial line was flushed with heparinized saline to prevent clotting. Manual samples were used to measure whole blood radioactivity concentrations. During this scan, manual blood samples were taken 15, 35, and 55 min post-injection. These samples were used to measure both whole blood and plasma radioactivity concentrations, as well as arterial plasma glucose levels.
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Sinograms were normalized, corrected for randoms, dead time, and decay, and based on the transmission scan corrected for scatter and attenuation. Corrected sinograms were reconstructed using the iterative 3D ordinary Poisson ordered subset expectation maximization algorithm [ 23 , 24 ] using eight iterations and 16 subsets. Automatic delineation of regions of interest ROIs was performed using PVElab [ 26 ] resulting in a total of 17 gray matter regions, subdivided into their left and right constituents cerebellar cortex, orbital frontal cortex, inferior medial frontal cortex, anterior cingulate cortex, thalamus, insula, caudate nucleus, putamen, superior temporal cortex, parietal cortex, inferior medial temporal cortex, superior frontal cortex, occipital cortex, sensory motor cortex, posterior cingulate cortex, enthorinal cortex, and hippocampus , a global white matter WM region, and a total gray matter GM region.
To compare our data to literature values, two additional manually drawn ROIs were analyzed: a gray matter region insular gray in four successive transversal planes and a white matter region centrum semiovale in two successive transversal planes , using Amide [ 27 ]. Sampler data were corrected for flushes and calibrated using the plasma concentrations derived from the three manual samples per subject to generate an arterial plasma input function.
First, using NLR, TACs were fitted to the standard irreversible two-tissue compartment model, providing the three rate constants K 1 , k 2 , and k 3 as well as the blood volume fraction V B. Second, the validity of the Patlak linearization [ 28 ] was investigated by comparing regional values of the net influx rate constant K i with those obtained using NLR. Third, the Patlak method was used without smoothing on a voxel-by-voxel basis, and for each ROI, mean values extracted from parametric K i images were compared with those obtained from regional Patlak analyses.
Additionally, parametric images were smoothed with a 6-mm Gaussian filter typical resolution of the current-generation whole-body PET scanners prior to analysis.
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CMR glu was calculated as K i times arterial plasma glucose divided by a lumped constant of 0. Whole blood input functions as well as regional TACs were derived as described above. First, TACs were fitted to the standard single-tissue compartment model, fixing delay and dispersion to the values obtained from a fit to the whole brain TAC [ 12 ], providing CBF as well as V T , distribution volume. Second, after smoothing with a 6-mm Gaussian filter, this analysis was repeated, and in addition, parametric CBF images were generated using a basis function method BFM implementation of the blood flow model [ 30 ].
For FDG, arterial activity was clearly seen after smoothing an early frame approximately 20 to 30 s post-injection with a 6-mm Gaussian filter.
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Starting three planes below the circle of Willis to avoid contamination with activity from the brain, ten successive planes were combined into an ROI representing a carotid artery [ 18 , 19 ]. The four hottest pixels per plane were identified and combined in a carotid artery ROI. The average time-activity curve obtained from the two carotid artery ROIs, scaled to the manual samples, was taken as the whole blood IDIF.
For 15 O]H 2 O, an identical procedure was followed. No significant differences between left and right CMR glu values were observed for any of the regions delineated automatically. Average left and right CMR glu values are listed in Table 1. Total gray matter CMR glu was 0. Excluding the entorhinal cortex, an average coefficient of variation CoV of The high CoV in the entorhinal cortex is probably due to the small volume of this region 3. A typical blood volume fraction, V B , of 0. In Table 2 , mean values of the separate rate constants for these manual ROIs are listed as well as a comparison with literature data in which similar types of ROIs were used; no recent papers were available for comparison of these separate parameters.
Figure 1 shows the linear relationship between Patlak- and NLR-derived K i values for the 16 automatic combined gray matter regions and a white matter region. Linear regression provided a slope of 0. Data of all 16 gray matter regions and a white matter brain region of nine healthy subjects are presented. Data points for each individual subject are shown with a separate symbol. The solid line indicates the line of identity. Based on the good correlation between Patlak and NLR results, parametric Patlak images were generated without smoothing.
Figure 2 shows the relationship between average parametric and ROI-derived K i values, both before and after smoothing of the parametric images. Without smoothing, a slope of 1. After smoothing, these values were 0. Correlation of average K i values derived using parametric and regional Patlak analyses. Data of all 16 total gray matter regions and a white matter brain region are presented for nine subjects. Parametric values represent the mean of all voxels within an ROI. Results for both images without smoothing black dots and those smoothed with a 6-mm Gaussian filter white dots are shown.
The correlation between average regional NLR and parametric values had a slope of 1. A representative parametric image is shown in Figure 3.
Representative parametric images of a single subject. The parametric CBF image was generated after smoothing with a 6-mm Gaussian filter. Total gray and white matter CBFs were 0. Linear regression provided a slope of 1. In Figure 3 , a representative parametric image is presented. Data of 16 gray matter regions and a white matter region are shown for 11 subjects.
In two out of nine subjects, a slope of 0. For the other seven patients, slope and r 2 were 1. The correlation is for 16 gray matter regions and a white matter region. Figure 6 shows the results of a similar comparison for Patlak-derived K i values. In this case, no apparent outliers, either in single fits or complete subjects, were apparent.
Slope and r 2 were 0. In the present study, average CMR glu values of 0. Automatically delineated regions yielded values of 0. However, especially the white matter estimate based on the latter method was contaminated with some gray matter spill in.
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Therefore, it seems that a static scanning protocol, together with fixed rate constants, is a valid approximation of full kinetic modeling in the case of healthy volunteers. Nevertheless, it should be noted that the assumption of fixed rate constants may not be valid in certain clinically relevant patient populations, such as those having diabetes [ 31 ], characterized by a. Furthermore, normal fixed rate constants are not automatically applicable to flow-limited states [ 34 ]. The good correlation between Patlak and NLR results slope 0. Parametric K i images without smoothing showed good correlation with regional K i values r 2 of 0.
Average CBF values of 0. These values are in line with the recent data acquired on an HRRT by Walker and co-workers [ 17 ], who found values of 0. The use of a noninvasive input function, derived from the carotid arteries, allowed for quantitatively correct estimates of regional CMR glu when Patlak linearization was used. In the case of NLR, however, the more stringent requirements placed on the input function i. Scaling to the manual samples yielded a similar factor as for CMR glu scans, but an underestimation of the peak was observed by a factor of 3.
Metabolic processes within the brain capillary endothelial cells are important to blood-brain function. Most neurotransmitters present in the blood do not enter the brain because of their low lipid solubility and lack of specific transport carriers in the luminal membrane of the capillary endothelial cell see Figure Therefore, it enters the brain more easily from the blood than would be predicted based on its lipid solubility.