Visualization of small brain nuclei with a high-spatial resolution, clinically available whole-body PET scanner.

OBJECTIVE: To verify the visibility of physiological 18F-fluorodeoxyglucose (18F-FDG) uptake in nuclei in and around the brainstem by a whole-body (WB) silicon photomultiplier positron emission tomography (SiPM-PET) scanner with point-spread function (PSF) reconstruction using various iteration numbers. METHODS: Ten healthy subjects (5 men, 5 women; mean age, 56.0 ± 5.0 years) who underwent 18F-FDG PET/CT using a WB SiPM-PET scanner and magnetic resonance imaging (MRI) of the brain including a spin-echo three-dimensional sampling perfection with application-optimized contrasts using different flip angle evolutions fluid-attenuated inversion recovery (3D-FLAIR) and a 3D-T1 magnetization-prepared rapid gradient-echo (T1-MPRAGE) images were enrolled. Each acquired PET image was reconstructed using ordered-subset expectation maximization (OSEM) with iteration numbers of 4, 16, 64, and 256 (subset 5 fixed) + time-of-flight (TOF) + PSF. The reconstructed PET images and 3D-FLAIR images for each subject were registered to individual T1-MPRAGE volumes using normalized mutual information criteria. For each MR-coregistered individual PET image, the pattern of FDG uptake in the inferior olivary nuclei (ION), dentate nuclei (DN), midbrain raphe nuclei (MRN), inferior colliculi (IC), mammillary bodies (MB), red nuclei (RN), subthalamic nuclei (STN), lateral geniculate nuclei (LGN), medial geniculate nuclei (MGN), and superior colliculi (SC) was visually classified into the following three categories: good, clearly distinguishable FDG accumulation; fair, obscure contour of FDG accumulation; poor, FDG accumulation indistinguishable from surrounding uptake. RESULTS: Among individual 18F-FDG PET images with OSEM iterations of 4, 16, 64, and 256 + TOF + PSF, the iteration numbers that showed the best visibility in each structure were as follows: ION, MRN, LGN, MGN, and SC, iteration 64; DN, iteration 16; IC, iterations 16, 64, and 256; MB, iterations 64 and 256; and RN and STN, iterations 16 and 64, respectively. Of the four iterations, the 18F-FDG PET image of iteration 64 visualized FDG accumulation in small structures in and around the brainstem most clearly (good, 98 structures; fair, 2 structures). CONCLUSIONS: A clinically available WB SiPM-PET scanner is useful for visualizing physiological FDG uptake in small brain nuclei, using a sufficiently high number of iterations for OSEM with TOF and PSF reconstructions.

Non-contrast dual-energy CT using X-map for acute ischemic stroke: region-specific comparison with simulated 120-kVp CT and diffusion-weighted MR images.

PURPOSE: X-map is a non-contrast dual-energy CT (DECT) application to identify acute ischemic stroke (AIS). Our aim was to verify region-specific characteristics of early ischemic changes (EIC) on X-map compared with simulated 120-kVp mixed-CT image and DWI. METHODS: Fifty AIS patients who underwent DECT and DWI were enrolled (mean age, 76 years; 34 men, 16 women). All datasets including mixed-CT image, X-map, and DWI were transformed into a standard brain atlas with 11 × 2 ROIs based on the ASPECTS + W system. ROIs with EIC on DWI, mixed-CT image, and X-map were defined as DWI-positive, mixed-CT-positive, and X-map-positive, and those with normal finding were DWI-negative, mixed-CT-negative, and X-map-negative respectively, in visual assessment by two neuroradiologists in consensus. RESULTS: EIC on X-maps were visually relevant to those on the other images: of 221 ROIs with mixed-CT-positive and X-map-positive, 198 (89.6%) were DWI-positive. X-map revealed moderate diagnostic accuracy for AIS compared with DWI in ROC curve analysis (AUC = 0.732). X-map identified EIC in deep white matter more sensitively than mixed-CT image: of 15 ROIs with mixed-CT-negative and X-map-positive in W segments, 14 (93.3%) were DWI-positive. X-map often showed EIC in cortical regions that were not detected on the other images: of 67 ROIs with mixed-CT-negative and X-map-positive in I and M1-M6 segments, 47 (70.1%) were DWI-negative. CONCLUSIONS: X-map is useful to detect EIC, especially in deep white matter, and may also provide additional information in acute ischemic lesions where DWI cannot be detected.

Oxygen extraction fraction is not uniform in human brain: a positron emission tomography study.

The regional differences in cerebral oxygen extraction fraction (OEF) in brain were investigated using positron emission tomography (PET) in detail with consideration of systemic errors in PET measurement estimated by simulation studies. The cerebral blood flow (CBF), cerebral blood volume (CBV), OEF, and cerebral metabolic rate of oxygen (CMRO2) were measured on healthy men by PET with 15O-labeled gases. The OEF values in the pons and the parahippocampal gyrus were significantly smaller than in the other brain regions. The OEF value in the lateral side of the occipital cortex was largest among the cerebral cortical regions. Simulation studies have revealed that errors in OEF caused by regional differences in the distribution volume of 15O-labeled water, as well as errors in OEF caused by a mixture of gray and white matter, must be negligible. The regional differences in OEF in brain must exist which might be related to physiological meanings.Article title: Kindly check and confirm the edit made in the article title.I have checked the article title and it is OK as is. Trial registration: The UMIN clinical trial number: UMIN000033382, https://www.umin.ac.jp/ctr/index.htm.

[Evaluation of Low-dose Whole-body FDG PET with SiPM-based PET/CT Scanner: Visual and Semi-quantitative Analyses Using Random Sampling from Full-dose Scan Data].

PURPOSE: Sensitivity and count rate performance of the latest PET/CT scanners with a silicon photomultiplier (SiPM) have been substantially improved compared to scanners with a photomultiplier tube (PMT), thereby promising a low-dose whole-body PET scan with maintaining image quality. However, it is ethically difficult to verify the low-dose protocol in actual clinical settings. In this study, we investigated the effect of dose reduction on reconstructed images by using a low-dose simulation technique, i.e., reducing the number of events from the acquired data. METHOD: For 21 subjects who underwent whole-body 18F-FDG PET examination with an SiPM-based PET/CT scanner, Biograph Vision (Siemens Healthineers, Erlangen, Germany), at a dosage of 3.5 MBq/kg and a continuous bed motion speed of 1.1 mm/sec (the standard protocol in our hospital), the number of events in acquired list data (100%; "full-dose") was reduced to 50%, 25%, 12.5%, and 6.25% ("low-dose"). The low-dose reconstructed images were evaluated visually and physically with reference to the full-dose images. The physical evaluation was performed by calculating differences in SUVmax at abnormal uptake (n=54) between the full-dose and low-dose images. RESULT: The 25% data images were visually acceptable, and the difference in SUVmax between the 100% and 25% data images was 9.8±13.5%. CONCLUSION: Our results suggest that Biograph Vision is a feasible method to reduce conventional dose with the potential use of 25% data images.

DeepPVC: prediction of a partial volume-corrected map for brain positron emission tomography studies via a deep convolutional neural network.

BACKGROUND: Partial volume correction with anatomical magnetic resonance (MR) images (MR-PVC) is useful for accurately quantifying tracer uptake on brain positron emission tomography (PET) images. However, MR segmentation processes for MR-PVC are time-consuming and prevent the widespread clinical use of MR-PVC. Here, we aimed to develop a deep learning model to directly predict PV-corrected maps from PET and MR images, ultimately improving the MR-PVC throughput. METHODS: We used MR T1-weighted and [11C]PiB PET images as input data from 192 participants from the Alzheimer's Disease Neuroimaging Initiative database. We calculated PV-corrected maps as the training target using the region-based voxel-wise PVC method. Two-dimensional U-Net model was trained and validated by sixfold cross-validation with the dataset from the 156 participants, and then tested using MR T1-weighted and [11C]PiB PET images from 36 participants acquired at sites other than the training dataset. We calculated the structural similarity index (SSIM) of the PV-corrected maps and intraclass correlation (ICC) of the PV-corrected standardized uptake value between the region-based voxel-wise (RBV) PVC and deepPVC as indicators for validation and testing. RESULTS: A high SSIM (0.884 ± 0.021) and ICC (0.921 ± 0.042) were observed in the validation and test data (SSIM, 0.876 ± 0.028; ICC, 0.894 ± 0.051). The computation time required to predict a PV-corrected map for a participant (48 s without a graphics processing unit) was much shorter than that for the RBV PVC and MR segmentation processes. CONCLUSION: These results suggest that the deepPVC model directly predicts PV-corrected maps from MR and PET images and improves the throughput of MR-PVC by skipping the MR segmentation processes.

Brain partial volume correction with point spreading function reconstruction in high-resolution digital PET: comparison with an MR-based method in FDG imaging.

OBJECTIVE: In quantitative positron emission tomography (PET) of the brain, partial volume effect due mainly to the finite spatial resolution of the PET scanner (> 3 mm full width at half maximum [FWHM]) is a primary source of error in the measurement of tracer uptake, especially in small structures such as the cerebral cortex (typically < 3 mm thickness). The aim of this study was to evaluate the partial volume correction (PVC) performance of point spread function-incorporated reconstruction (PSF reconstruction) in combination with the latest digital PET scanner. This evaluation was performed through direct comparisons with magnetic resonance imaging (MR)-based PVC (used as a reference method) in a human brain study. METHODS: Ten healthy subjects underwent brain 18F-FDG PET (30-min acquisition) on a digital PET/CT system (Siemens Biograph Vision, 3.5-mm FWHM scanner resolution at the center of the field of view) and anatomical T1-weighted MR imaging for MR-based PVC. PSF reconstruction was applied with a wide range of iterations (4 to 256; 5 subsets). FDG uptake in the cerebral cortex was evaluated using the standardized uptake value ratio (SUVR) and compared between PSF reconstruction and MR-based PVC. RESULTS: Cortical structures were visualized by PSF reconstruction with several tens of iterations and were anatomically well matched with the MR-derived cortical segments. Higher numbers of iterations resulted in higher cortical SUVRs, which approached those of MR-based PVC (1.76), although even with the maximum number of iterations they were still smaller by 16% (1.47), corresponding to approximately 1.5-mm FWHM of the effective spatial resolution. CONCLUSION: With the latest digital PET scanner, PSF reconstruction can be used as a PVC technique in brain PET, albeit with suboptimal resolution recovery. A relative advantage of PSF reconstruction is that it can be applied not only to cerebral cortical regions, but also to various small structures such as small brain nuclei that are hardly visualized on anatomical T1-weighted imaging, and thus hardly recovered by MR-based PVC.

A review on AI in PET imaging.

Artificial intelligence (AI) has been applied to various medical imaging tasks, such as computer-aided diagnosis. Specifically, deep learning techniques such as convolutional neural network (CNN) and generative adversarial network (GAN) have been extensively used for medical image generation. Image generation with deep learning has been investigated in studies using positron emission tomography (PET). This article reviews studies that applied deep learning techniques for image generation on PET. We categorized the studies for PET image generation with deep learning into three themes as follows: (1) recovering full PET data from noisy data by denoising with deep learning, (2) PET image reconstruction and attenuation correction with deep learning and (3) PET image translation and synthesis with deep learning. We introduce recent studies based on these three categories. Finally, we mention the limitations of applying deep learning techniques to PET image generation and future prospects for PET image generation.

Effect of hematocrit on cerebral blood flow measured by pseudo-continuous arterial spin labeling MRI: A comparative study with 15O-water positron emission tomography.

INTRODUCTION: In cerebral blood flow (CBF) quantification with pseudo-continuous arterial spin labeling (pCASL) MRI, arterial blood T1 (T1a) is usually fixed to a typical value (e.g., 1650 ms). However, individual T1a depends strongly on hematocrit (Hct) level. To investigate the utility of Hct-based T1a as an alternative to the fixed T1a method, we performed a comparative study with 15O-water positron emission tomography (PET). METHODS: For patients with unilateral occlusion or stenosis of major arteries, hemispheric CBF on the healthy side was measured using pCASL and 15O-water PET. The pCASL CBFs were calculated with both (a) fixed T1a (1650 ms) and (b) individual T1a estimated from blood-sampled Hct (Hct-based T1a). Correlation coefficients of Hct-CBF were calculated and compared between pCASL and PET. RESULTS: In pCASL, CBF with fixed T1a showed a strong negative correlation with Hct (r = -0.568), which was reduced with individual Hct-based T1a (r = -0.341 to -0.190), consistent with the Hct-CBF relation measured with PET (r = -0.349). DISCUSSION AND CONCLUSION: We demonstrated that Hct-based T1a resulted in smaller inter-individual variations in pCASL CBF and an inverse Hct-CBF relationship more similar to that of PET. Care must be taken in the interpretation of pCASL CBF imaging in relation to Hct level even in subjects without anemia. Further comparative studies are needed to investigate whether advanced techniques improve pCASL CBF quantification at the individual level.

Prediction of an oxygen extraction fraction map by convolutional neural network: validation of input data among MR and PET images.

PURPOSE: Oxygen extraction fraction (OEF) is a biomarker for the viability of brain tissue in ischemic stroke. However, acquisition of the OEF map using positron emission tomography (PET) with oxygen-15 gas is uncomfortable for patients because of the long fixation time, invasive arterial sampling, and radiation exposure. We aimed to predict the OEF map from magnetic resonance (MR) and PET images using a deep convolutional neural network (CNN) and to demonstrate which PET and MR images are optimal as inputs for the prediction of OEF maps. METHODS: Cerebral blood flow at rest (CBF) and during stress (sCBF), cerebral blood volume (CBV) maps acquired from oxygen-15 PET, and routine MR images (T1-, T2-, and T2*-weighted images) for 113 patients with steno-occlusive disease were learned with U-Net. MR and PET images acquired from the other 25 patients were used as test data. We compared the predicted OEF maps and intraclass correlation (ICC) with the real OEF values among combinations of MRI, CBF, CBV, and sCBF. RESULTS: Among the combinations of input images, OEF maps predicted by the model learned with MRI, CBF, CBV, and sCBF maps were the most similar to the real OEF maps (ICC: 0.597 ± 0.082). However, the contrast of predicted OEF maps was lower than that of real OEF maps. CONCLUSION: These results suggest that the deep CNN learned useful features from CBF, sCBF, CBV, and MR images and predict qualitatively realistic OEF maps. These findings suggest that the deep CNN model can shorten the fixation time for 15O PET by skipping 15O2 scans. Further training with a larger data set is required to predict accurate OEF maps quantitatively.

Integrated PET/MRI scanner with oxygen-15 labeled gases for quantification of cerebral blood flow, cerebral blood volume, cerebral oxygen extraction fraction and cerebral metabolic rate of oxygen.

OBJECTIVES: Measurement of cerebral blood flow (CBF), cerebral blood volume (CBV), cerebral oxygen extraction fraction (OEF) and cerebral metabolic rate of oxygen (CMRO2) by PET with oxygen-15 labeled gases is useful for diagnosis and treatment planning in cases of chronic occlusive cerebrovascular disease. In the present study, CBF, CBV, OEF and CMRO2 were measured using the integrated design of PET/MRI scanner system. This is a first attempt to measure cerebral perfusion and oxygen metabolism using PET/MRI with oxygen-15 labeled gases. METHODS: PET/MRI measurements with the steady-state method of oxygen-15 labeled gases, carbon monoxide (C15O), oxygen (15O2), and carbon dioxide (C15O2) were performed on nine healthy men. Two kinds of attenuation correction for PET were performed using MRI with Dixon sequence (DIXON) and Dixon sequence with model-based bone segmentation (DIXONbone). A real-time motion correction of PET images was also performed using simultaneously measured MR images to detect head motion. RESULTS: Mean and SD values of CBF, CBV, OEF, and CMRO2 in the cerebral cortices with attenuation correction by DIXON were 31 ± 4 mL/100 mL/min, 2.7 ± 0.2 mL/mL, 0.40 ± 0.07, and 2.5 ± 0.3 mL/100 mL/min without real-time motion correction, and 33 ± 4 mL/100 mL/min, 2.7 ± 0.2 mL/mL, 0.40 ± 0.07, and 2.6 ± 0.3 mL/100 mL/min with real-time motion correction, respectively. Values with of CBF, CBV, OEF, and CMRO2 with attenuation correction by DIXONbone were 35 ± 5 mL/100 mL/min, 2.8 ± 0.2 mL/mL, 0.40 ± 0.07, and 2.8 ± 0.3 mL/100 mL/min without real-time motion correction, and 38 ± 5 mL/100 mL/min, 2.8 ± 0.2 mL/mL, 0.40 ± 0.07, and 3.0 ± 0.4 mL/100 mL/min with real-time motion correction, respectively. CONCLUSIONS: Using PET/MRI with oxygen-15 labeled gases, CBF, CBV, OEF, and CMRO2 could be measured. Values of CBF, CBV, and CMRO2 measured with attenuation correction by DIXON were significantly lower than those measured with correction by DIXONbone. One of the reasons for this is that attenuation correction of DIXON does not take into consideration of the photon absorption by bone. OEF values, corresponding to ratios of CMRO2 to CBF, were not affected by attenuation correction methods. Values of CBF and CMRO2 with a real-time motion correction were significantly higher than those without correction. Using PET/MRI with adequate corrections, similar values of CBF, CBV, OEF, and CMRO2 as PET alone scanner system reported previously were obtained. TRAIL REGISTRATION: The UMIN clinical trial number: UMIN000033382.

Iterative framework for image registration and partial volume correction in brain positron emission tomography.

Imprecise registration between positron emission tomography (PET) and anatomical magnetic resonance (MR) images is a critical source of error in MR imaging-guided partial volume correction (MR-PVC). Here, we propose a novel framework for image registration and partial volume correction, which we term PVC-optimized registration (PoR), to address imprecise registration. The PoR framework iterates PVC and registration between uncorrected PET and smoothed PV-corrected images to obtain precise registration. We applied PoR to the [11C]PiB PET data of 92 participants obtained from the Alzheimer's Disease Neuroimaging Initiative database and compared the registration results, PV-corrected standardized uptake value (SUV) and its ratio to the cerebellum (SUVR), and intra-region coefficient of variation (CoV) between PoR and conventional registration. Significant differences in registration of as much as 2.74 mm and 3.02° were observed between the two methods (effect size < - 0.8 or > 0.8), which resulted in considerable SUVR differences throughout the brain, reaching a maximal difference of 62.3% in the sensory motor cortex. Intra-region CoV was significantly reduced using the PoR throughout the brain. These results suggest that PoR reduces error as a result of imprecise registration in PVC and is a useful method for accurately quantifying the amyloid burden in PET.

Error propagation analysis of seven partial volume correction algorithms for [18F]THK-5351 brain PET imaging.

BACKGROUND: Novel partial volume correction (PVC) algorithms have been validated by assuming ideal conditions of image processing; however, in real clinical PET studies, the input datasets include error sources which cause error propagation to the corrected outcome. METHODS: We aimed to evaluate error propagations of seven PVCs algorithms for brain PET imaging with [18F]THK-5351 and to discuss the reliability of those algorithms for clinical applications. In order to mimic brain PET imaging of [18F]THK-5351, pseudo-observed SUVR images for one healthy adult and one adult with Alzheimer's disease were simulated from individual PET and MR images. The partial volume effect of pseudo-observed PET images were corrected by using Müller-Gärtner (MG), the geometric transfer matrix (GTM), Labbé (LABBE), regional voxel-based (RBV), iterative Yang (IY), structural functional synergy for resolution recovery (SFS-RR), and modified SFS-RR algorithms with incorporation of error sources in the datasets for PVC processing. Assumed error sources were mismatched FWHM, inaccurate image-registration, and incorrectly segmented anatomical volume. The degree of error propagations in ROI values was evaluated by percent differences (%diff) of PV-corrected SUVR against true SUVR. RESULTS: Uncorrected SUVRs were underestimated against true SUVRs (- 15.7 and - 53.7% in hippocampus for HC and AD conditions), and application of each PVC algorithm reduced the %diff. Larger FWHM mismatch led to larger %diff of PVC-SUVRs against true SUVRs for all algorithms. Inaccurate image registration showed systematic propagation for most algorithms except for SFS-RR and modified SFS-RR. Incorrect segmentation of the anatomical volume only resulted in error propagations in limited local regions. CONCLUSIONS: We demonstrated error propagation by numerical simulation of THK-PET imaging. Error propagations of 7 PVC algorithms for brain PET imaging with [18F]THK-5351 were significant. Robust algorithms for clinical applications must be carefully selected according to the study design of clinical PET data.

Radiosynthesis of 18F-labeled d-allose.

Rare sugars are defined as monosaccharides that exist in nature but are only present in limited quantities. d-Allose is a rare sugar that has been reported to have some unique physiological effects. The present study describes suitable synthetic procedures for novel rare sugars of d-allose that are 18F-labeled at the C-3 and C-6 positions and the preparation of the appropriate labeling precursors. The goal is to facilitate in vivo, noninvasive positron emission tomography (PET) investigation of the behavior of rare sugar analogs of d-allose in organs. We found five precursors that were practical for labeling, three for 3-deoxy-3-[18F]fluoro-d-allose ([18F]3FDA) and two for 6-deoxy-6-[18F]fluoro-d-allose ([18F]6FDA). With manual operation synthesis, the highest radiochemical conversion rates were 75% for [18F]3FDA with a precursor of 1,2,4,6-tetra-O-acetyl-3-O-trifluoromethanesulfonyl-ß-d-glucopyranose and 69% for [18F]6FDA with a precursor of 1,2,3,4-tetra-O-acetyl-6-O-trifluoromethanesulfonyl-ß-d-allopyranose. Furthermore, the practical yields of [18F]3FDA and [18F]6FDA using an automated synthesizer were also investigated. Radiochemical yields of 67% and 49% were obtained for [18F]3FDA and [18F]6FDA, respectively, in an automated synthesizer. As basic assessment of stability for use in PET scanning, high performance liquid chromatography analysis showed no decomposition of [18F]3FDA and [18F]6FDA after up to 6 h in rabbit blood plasma.

Spatial coefficient of variation in pseudo-continuous arterial spin labeling cerebral blood flow images as a hemodynamic measure for cerebrovascular steno-occlusive disease: A comparative 15O positron emission tomography study.

Pseudo-continuous arterial spin labeling (pCASL) is a completely non-invasive method of cerebral perfusion measurement. However, cerebral blood flow (CBF) quantification is hampered by arterial transit artifacts characterized by bright vascular signals surrounded by decreased signals in tissue regions, which commonly appear in patients with reduced cerebral perfusion pressure. The spatial coefficient of variation (CoV) of pCASL CBF images has been proposed as an alternative region-of-interest (ROI)-based hemodynamic measure to predict prolonged arterial transit time (ATT). This retrospective study investigates the utility of spatial CoV by comparison with 15O positron emission tomography (PET). For patients with cerebrovascular steno-occlusive disease ( n = 17), spatial CoV was positively correlated with ATT independently measured by pulsed arterial spin labeling ( r = 0.597, p < 0.001), confirming its role as an ATT-like hemodynamic measure. Comparisons with 15O PET demonstrated that spatial CoV was positively correlated with vascular mean transit time ( r = 0.587, p < 0.001) and negatively correlated with both resting CBF ( r = -0.541, p = 0.001) and CBF response to hypercapnia ( r = -0.373, p = 0.030). ROI-based spatial CoV calculated from single time-point pCASL can potentially detect subtle perfusion abnormalities in clinical settings.

A comparison of five partial volume correction methods for Tau and Amyloid PET imaging with [18F]THK5351 and [11C]PIB.

PURPOSE: To suppress partial volume effect (PVE) in brain PET, there have been many algorithms proposed. However, each methodology has different property due to its assumption and algorithms. Our aim of this study was to investigate the difference among partial volume correction (PVC) method for tau and amyloid PET study. METHODS: We investigated two of the most commonly used PVC methods, Müller-Gärtner (MG) and geometric transfer matrix (GTM) and also other three methods for clinical tau and amyloid PET imaging. One healthy control (HC) and one Alzheimer's disease (AD) PET studies of both [18F]THK5351 and [11C]PIB were performed using a Eminence STARGATE scanner (Shimadzu Inc., Kyoto, Japan). All PET images were corrected for PVE by MG, GTM, Labbé (LABBE), Regional voxel-based (RBV), and Iterative Yang (IY) methods, with segmented or parcellated anatomical information processed by FreeSurfer, derived from individual MR images. PVC results of 5 algorithms were compared with the uncorrected data. RESULTS: In regions of high uptake of [18F]THK5351 and [11C]PIB, different PVCs demonstrated different SUVRs. The degree of difference between PVE uncorrected and corrected depends on not only PVC algorithm but also type of tracer and subject condition. CONCLUSION: Presented PVC methods are straight-forward to implement but the corrected images require careful interpretation as different methods result in different levels of recovery.

Changes in effective diffusivity for oxygen during neural activation and deactivation estimated from capillary diameter measured by two-photon laser microscope.

The relation between cerebral blood flow (CBF) and cerebral oxygen extraction fraction (OEF) can be expressed using the effective diffusivity for oxygen in the capillary bed (D) as OEF = 1 - exp(-D/CBF). The D value is proportional to the microvessel blood volume. In this study, changes in D during neural activation and deactivation were estimated from changes in capillary and arteriole diameter measured by two-photon microscopy in awake mice. Capillary and arteriole vessel diameter in the somatosensory cortex and cerebellum were measured under neural activation (sensory stimulation) and neural deactivation [crossed cerebellar diaschisis (CCD)], respectively. Percentage changes in D during sensory stimulation and CCD were 10.3 ± 7.3 and -17.5 ± 5.3 % for capillary diameter of <6 µm, respectively. These values were closest to the percentage changes in D calculated from previously reported human positron emission tomography data. This may indicate that thinner capillaries might play the greatest role in oxygen transport from blood to brain tissue.

Impact of spillover from white matter by partial volume effect on quantification of amyloid deposition with [11C]PiB PET.

High non-specific uptake of [11C]Pittsburgh compound B ([11C]PiB) in white matter and signal spillover from white matter, due to partial volume effects, confound radioactivity measured in positron emission tomography (PET) with [11C]PiB. We aimed to reveal the partial volume effect in absolute values of kinetic parameters for [11C]PiB, in terms of spillover from white matter. Dynamic data acquired in [11C]PiB PET scans with five healthy volunteers and eight patients with Alzheimer's disease were corrected with region-based and voxel-based partial volume corrections. Binding potential (BPND) was estimated using the two-tissue compartment model analysis with a plasma input function. Partial volume corrections significantly decreased cortical BPND values. The degree of decrease in healthy volunteers (-52.7±5.8%) was larger than that in Alzheimer's disease patients (-11.9±4.2%). The simulation demonstrated that white matter spillover signals due to the partial volume effect resulted in an overestimation of cortical BPND, with a greater degree of overestimation for lower BPND values. Thus, an overestimation due to partial volume effects is more severe in healthy volunteers than in Alzheimer's disease patients. Partial volume corrections may be useful for accurately quantifying Aß deposition in cortical regions.

Validation of a simplified scatter correction method for 3D brain PET with 15O.

OBJECTIVE: Positron emission tomography (PET) enables quantitative measurements of various biological functions. Accuracy in data acquisition and processing schemes is a prerequisite for this. The correction of scatter is especially important when a 3D PET scanner is used. The aim of this study was to validate the use of a simplified calculation-based scatter correction method for 15O studies in the brain. METHODS: We applied two scatter correction methods to the same 15O PET data acquired from patients with cerebrovascular disease (n = 10): a hybrid dual-energy-window scatter correction (reference method), and a deconvolution scatter correction (simplified method). The PET study included three sequential scans for 15O-CO, 15O-O2, and 15O-H2O, from which the following quantitative parameters were calculated, cerebral blood flow, cerebral blood volume, cerebral metabolic rate of oxygen, and oxygen extraction fraction. RESULTS: Both scatter correction methods provided similar reconstruction images with almost identical image noise, although there were slightly greater differences in white-matter regions compared with gray matter regions. These differences were also greater for 15O-CO than for 15O-H2O and 15O-O2. Region of interest analysis of the quantitative parameters demonstrated that the differences were less than 10 % (except for cerebral blood volume in white-matter regions), and the agreement between the methods was excellent, with intraclass correlation coefficients above 0.95 for all the parameters. CONCLUSIONS: The deconvolution scatter correction despite its simplified implementation provided similar results to the hybrid dual-energy-window scatter correction. We consider it suitable for application in a clinical 15O brain study using a 3D PET scanner.