Masanobu Ibaraki

Positron emission tomography/computed tomography (PET/CT) scanners equipped with silicon photomultiplier detectors offer superior sensitivity and count-rate performance. The aim of this study was to evaluate the feasibility and impact of shortening the acquisition duration in brain tumor ¹¹C-methionine PET using a silicon photomultiplier PET/CT system, and to assess how point spread function (PSF) correction influences quantitative values. In the phantom study, a brain tumor phantom was scanned using the Biograph Vision silicon photomultiplier-based PET/CT system. Data were acquired for 10, 5, 3, and 1 min, and the images were reconstructed with and without PSF correction. In the clinical study, 20 patients who underwent ¹¹C-methionine PET were retrospectively analyzed. PET data were acquired over 10 min and subsequently reconstructed for 10, 5, and 3 min. We evaluated quantitative parameters including the maximum standardized uptake value (SUVmax), and their relative errors under shortened acquisition durations were analyzed. In the phantom study, the SUVmax increased with shorter acquisition durations; however, this increase was less pronounced with PSF correction. In the clinical study, relative errors of SUVmax for the 5 and 3 min acquisitions with PSF correction were 2.9 ± 3.8% and 5.2 ± 5.4%, respectively. They were smaller than those without PSF correction (5.5 ± 5.1% and 12.7 ± 8.5%), indicating superior quantitative stability with shortened acquisition duration. The combination of the Biograph Vision system and PSF correction enabled the acquisition of high-quality PET images with shortened scan times.

Positron emission tomography (PET) with 15O-gas for quantifying cerebral blood flow (CBF) and oxygen metabolism is the gold standard for assessing hemodynamics in ischemic cerebrovascular disease. However, conventional 15O-gas PET methods do not provide information on regional arrival timing, a hemodynamic parameter typically measured using computed tomography (CT) perfusion with contrast media. This study demonstrated that 15O-gas PET with a state-of-the-art clinical PET scanner and optimized analysis can generate arrival time maps. In this retrospective study of ten patients with unilateral stenosis or occlusion of the major arteries, we compared PET-derived arrival time maps with CT perfusion Tmax maps. In PET with short inhalation of [15O]-CO2 gases, dynamic images were reconstructed with 2-sec temporal resolution, followed by weighted least-squares fitting of one-tissue compartment models, with or without the contributions from vascular components. PET arrival time maps were visually comparable to CT perfusion Tmax maps regarding the spatial extent of delayed brain regions, with less noise and higher image quality when using the model without the vascular components. Region-of-interest analyses showed good correlations between the two modalities: correlation coefficients of 0.834 for absolute values and 0.718 for ipsilateral-to-contralateral differences, respectively, indicating that 15O-gas PET can quantitatively measure the arrival time with reasonable accuracy. The present method generates arrival-time maps with 15O-gas PET by applying optimized kinetic analysis to dynamic [15O]-CO2 images acquired using a state-of-the-art, high-sensitivity clinical PET scanner. Additional arrival time information for conventional PET parameters of CBF and oxygen metabolism may facilitate a more comprehensive understanding of the hemodynamic status in cerebrovascular steno-occlusive diseases.

Deep image prior (DIP) has been successfully applied to positron emission tomography (PET) image restoration, enabling represent implicit prior using only convolutional neural network architecture without training dataset, whereas the general supervised approach requires massive low- and high-quality PET image pairs. To answer the increased need for PET imaging with DIP, it is indispensable to improve the performance of the underlying DIP itself. Here, we propose a self-supervised pre-training model to improve the DIP-based PET image denoising performance. Our proposed pre-training model acquires transferable and generalizable visual representations from only unlabeled PET images by restoring various degraded PET images in a self-supervised approach. We evaluated the proposed method using clinical brain PET data with various radioactive tracers ($^{18}$F-florbetapir, $^{11}$C-Pittsburgh compound-B, $^{18}$F-fluoro-2-deoxy-D-glucose, and $^{15}$O-CO$_{2}$) acquired from different PET scanners. The proposed method using the self-supervised pre-training model achieved robust and state-of-the-art denoising performance while retaining spatial details and quantification accuracy compared to other unsupervised methods and pre-training model. These results highlight the potential that the proposed method is particularly effective against rare diseases and probes and helps reduce the scan time or the radiotracer dose without affecting the patients.

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.

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.

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.

In the bolus tracking technique with computed tomography (CT) or magnetic resonance imaging, cerebral blood flow (CBF) is computed from deconvolution analysis, but its accuracy is unclear. To evaluate the reliability of CT perfusion (CTP)-derived CBF, we examined 27 patients with symptomatic or asymptomatic unilateral cerebrovascular steno-occlusive disease. Results from three deconvolution algorithms, standard singular value decomposition (sSVD), delay-corrected SVD (dSVD), and block-circulant SVD (cSVD), were compared with ¹⁵O positron emission tomography (PET) as a reference standard. To investigate CBF errors associated with the deconvolution analysis, differences in lesion-to-normal CBF ratios between PET and CTP were correlated with prolongation of arterial-tissue delay (ATD) and mean transit time (MTT) in the lesion hemisphere. Computed tomography perfusion results strongly depended on the deconvolution algorithms used. Standard singular value decomposition showed ATD-dependent underestimation of CBF ratio, whereas cSVD showed overestimation of the CBF ratio when MTT was severely prolonged in the lesions. The computer simulations reproduced the trend observed in patients. Deconvolution by dSVD can provide lesion-to-normal CBF ratios less dependent on ATD and MTT, but requires accurate ATD maps in advance. A practical and accurate method for CTP is required to assess CBF in patients with MTT-prolonged regions.

Accurate and validated methods for estimating regional PET image noise are helpful for optimizing image processing. The bootstrap is a data-based simulation method for statistical inference, which can be used to estimate the PET image noise without repeated measurements. The aim of this study was to experimentally validate bootstrap-based methods as a tool for estimating PET image noise and demonstrate its usefulness for evaluating image reconstruction algorithms. Two bootstrap-based method, the list-mode data bootstrap (LMBS) and the sinogram bootstrap (SNBS), were implemented on a clinical PET scanner. A uniform cylindrical phantom filled with ¹⁸F solution was scanned using list-mode acquisition. A reference standard deviation (SD) map was calculated from 60 statistically independent measured list-mode data. Using one of the 60 list-mode data, 60 bootstrap replicates were generated and used to calculate bootstrap SD maps. Brain ¹⁸F-FDG data from a healthy volunteer were also processed as an example of the bootstrap application. Three reconstruction algorithms, FBP 2D and both 2D and 3D versions of dynamic row-action maximum likelihood algorithm (DRAMA), were assessed. For all the reconstruction algorithms used, the bootstrap SD maps agreed well with the reference SD map, confirming the validity of the bootstrap methods for assessing image noise. The two bootstrap methods were equivalent with respect to the performance of image noise estimation. The bootstrap analysis of the FDG data showed the better contrast–noise relation curve for DRAMA 3D compared to DRAMA 2D and FBP 2D. The bootstrap methods provide the estimates of image noise for various reconstruction algorithms with reasonable accuracy, require only a single measurement, not repeated measures, and are, therefore, applicable for a human PET study.