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Quantification of 18F-FDG PET images is useful for accurate diagnosis and evaluation of various brain diseases, including brain tumors, epilepsy, dementia, and Parkinson disease. However, accurate quantification of 18F-FDG PET images requires matched 3-dimensional T1 MRI scans of the same individuals to provide detailed information on brain anatomy. In this paper, we propose a transfer learning approach to adapt a pretrained deep neural network model from amyloid PET to spatially normalize 18F-FDG PET images without the need for 3-dimensional MRI. Methods: The proposed method is based on a deep learning model for automatic spatial normalization of 18F-FDG brain PET images, which was developed by fine-tuning a pretrained model for amyloid PET using only 103 18F-FDG PET and MR images. After training, the algorithm was tested on 65 internal and 78 external test sets. All T1 MR images with a 1-mm isotropic voxel size were processed with FreeSurfer software to provide cortical segmentation maps used to extract a ground-truth regional SUV ratio using cerebellar gray matter as a reference region. These values were compared with those from spatial normalization-based quantification methods using the proposed method and statistical parametric mapping software. Results: The proposed method showed superior spatial normalization compared with statistical parametric mapping, as evidenced by increased normalized mutual information and better size and shape matching in PET images. Quantitative evaluation revealed a consistently higher SUV ratio correlation and intraclass correlation coefficients for the proposed method across various brain regions in both internal and external datasets. The remarkably good correlation and intraclass correlation coefficient values of the proposed method for the external dataset are noteworthy, considering the dataset's different ethnic distribution and the use of different PET scanners and image reconstruction algorithms. Conclusion: This study successfully applied transfer learning to a deep neural network for 18F-FDG PET spatial normalization, demonstrating its resource efficiency and improved performance. This highlights the efficacy of transfer learning, which requires a smaller number of datasets than does the original network training, thus increasing the potential for broader use of deep learning-based brain PET spatial normalization techniques for various clinical and research radiotracers.

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Objectives: To develop the following three attenuation correction (AC) methods for brain 18F-fluorodeoxyglucose-positron emission tomography (PET), using deep learning, and to ascertain their precision levels: (i) indirect method; (ii) direct method; and (iii) direct and high-resolution correction (direct+HRC) method. Methods: We included 53 patients who underwent cranial magnetic resonance imaging (MRI) and computed tomography (CT) and 27 patients who underwent cranial MRI, CT, and PET. After fusion of the magnetic resonance, CT, and PET images, resampling was performed to standardize the field of view and matrix size and prepare the data set. In the indirect method, synthetic CT (SCT) images were generated, whereas in the direct and direct+HRC methods, a U-net structure was used to generate AC images. In the indirect method, attenuation correction was performed using SCT images generated from MRI findings using U-net instead of CT images. In the direct and direct+HRC methods, AC images were generated directly from non-AC images using U-net, followed by image evaluation. The precision levels of AC images generated using the indirect and direct methods were compared based on the normalized mean squared error (NMSE) and structural similarity (SSIM). Results: Visual inspection revealed no difference between the AC images prepared using CT-based attenuation correction and those prepared using the three methods. The NMSE increased in the order indirect, direct, and direct+HRC methods, with values of 0.281×10-3, 4.62×10-3, and 12.7×10-3, respectively. Moreover, the SSIM of the direct+HRC method was 0.975. Conclusion: The direct+HRC method enables accurate attenuation without CT exposure and high-resolution correction without dedicated correction programs.

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The differential diagnosis between atypical Parkinsonian syndromes may be challenging and critical. We aimed to proposed a radiomics-guided deep learning (DL) model to discover interpretable DL features and further verify the proposed model through the differential diagnosis of Parkinsonian syndromes. We recruited 1495 subjects for 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) scanning, including 220 healthy controls and 1275 patients diagnosed with idiopathic Parkinson's disease (IPD), multiple system atrophy (MSA), or progressive supranuclear palsy (PSP). Baseline radiomics and two DL models were developed and tested for the Parkinsonian diagnosis. The DL latent features were extracted from the last layer and subsequently guided by radiomics. The radiomics-guided DL model outperformed the baseline radiomics approach, suggesting the effectiveness of the DL approach. DenseNet showed the best diagnosis ability (sensitivity: 95.7%, 90.1%, and 91.2% for IPD, MSA, and PSP, respectively) using retained DL features in the test dataset. The retained DL latent features were significantly associated with radiomics features and could be interpreted through biological explanations of handcrafted radiomics features. The radiomics-guided DL model offers interpretable high-level abstract information for differential diagnosis of Parkinsonian disorders and holds considerable promise for personalized disease monitoring.

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Extranodal diffuse large B-cell lymphoma (DLBCL) is a heterogeneous disease process and an aggressive form of non-Hodgkin's lymphoma. We present a case of multiorgan involvement of DLBCL in a patient with documented risk factors, including [ 18 F] fluorodeoxyglucose positron emission tomography/magnetic resonance imaging findings highlighting striking perineural spread involving intracranial and extracranial segments of the bilateral trigeminal nerves.

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The collaboration of Yale, the University of California, Davis, and United Imaging Healthcare has successfully developed the NeuroEXPLORER, a dedicated human brain PET imager with high spatial resolution, high sensitivity, and a built-in 3-dimensional camera for markerless continuous motion tracking. It has high depth-of-interaction and time-of-flight resolutions, along with a 52.4-cm transverse field of view (FOV) and an extended axial FOV (49.5 cm) to enhance sensitivity. Here, we present the physical characterization, performance evaluation, and first human images of the NeuroEXPLORER. Methods: Measurements of spatial resolution, sensitivity, count rate performance, energy and timing resolution, and image quality were performed adhering to the National Electrical Manufacturers Association (NEMA) NU 2-2018 standard. The system's performance was demonstrated through imaging studies of the Hoffman 3-dimensional brain phantom and the mini-Derenzo phantom. Initial 18F-FDG images from a healthy volunteer are presented. Results: With filtered backprojection reconstruction, the radial and tangential spatial resolutions (full width at half maximum) averaged 1.64, 2.06, and 2.51 mm, with axial resolutions of 2.73, 2.89, and 2.93 mm for radial offsets of 1, 10, and 20 cm, respectively. The average time-of-flight resolution was 236 ps, and the energy resolution was 10.5%. NEMA sensitivities were 46.0 and 47.6 kcps/MBq at the center and 10-cm offset, respectively. A sensitivity of 11.8% was achieved at the FOV center. The peak noise-equivalent count rate was 1.31 Mcps at 58.0 kBq/mL, and the scatter fraction at 5.3 kBq/mL was 36.5%. The maximum count rate error at the peak noise-equivalent count rate was less than 5%. At 3 iterations, the NEMA image-quality contrast recovery coefficients varied from 74.5% (10-mm sphere) to 92.6% (37-mm sphere), and background variability ranged from 3.1% to 1.4% at a contrast of 4.0:1. An example human brain 18F-FDG image exhibited very high resolution, capturing intricate details in the cortex and subcortical structures. Conclusion: The NeuroEXPLORER offers high sensitivity and high spatial resolution. With its long axial length, it also enables high-quality spinal cord imaging and image-derived input functions from the carotid arteries. These performance enhancements will substantially broaden the range of human brain PET paradigms, protocols, and thereby clinical research applications.

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PURPOSE: A hypometabolic profile involving the limbic areas, brainstem and cerebellum has been identified in long COVID patients using [18F]fluorodeoxyglucose (FDG)-PET. This study was conducted to evaluate possible recovery of brain metabolism during the follow-up of patients with prolonged symptoms. METHODS: Fifty-six adults with long COVID who underwent two brain [18F]FDG-PET scans in our department between May 2020 and October 2022 were retrospectively analysed, and compared to 51 healthy subjects. On average, PET1 was performed 7 months (range 3-17) after acute COVID-19 infection, and PET2 was performed 16 months (range 8-32) after acute infection, because of persistent severe or disabling symptoms, without significant clinical recovery. Whole-brain voxel-based analysis compared PET1 and PET2 from long COVID patients to scans from healthy subjects (p-voxel < 0.001 uncorrected, p-cluster < 0.05 FWE-corrected) and PET1 to PET2 (with the same threshold, and secondarily with a less constrained threshold of p-voxel < 0.005 uncorrected, p-cluster < 0.05 uncorrected). Additionally, a region-of-interest (ROI) semiquantitative anatomical approach was performed for the same comparisons (p < 0.05, corrected). RESULTS: PET1 and PET2 revealed voxel-based hypometabolisms consistent with the previously reported profile in the literature. This between-group analysis comparing PET1 and PET2 showed minor improvements in the pons and cerebellum (8.4 and 5.2%, respectively, only significant under the less constrained uncorrected p-threshold); for the pons, this improvement was correlated with the PET1-PET2 interval (r = 0.21, p < 0.05). Of the 14,068 hypometabolic voxels identified on PET1, 6,503 were also hypometabolic on PET2 (46%). Of the 7,732 hypometabolic voxels identified on PET2, 6,094 were also hypometabolic on PET1 (78%). The anatomical ROI analysis confirmed the brain hypometabolism involving limbic region, the pons and cerebellum at PET1 and PET2, without significant changes between PET1 and PET2. CONCLUSION: Subjects with persistent symptoms of long COVID exhibit durable deficits in brain metabolism, without progressive worsening.

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Antiamyloid therapies for Alzheimer disease recently entered clinical practice, making imaging biomarkers for Alzheimer disease even more relevant to guiding patient management. Amyloid and tau PET are valuable tools that can provide objective evidence of Alzheimer pathophysiology in living patients and will increasingly be used to complement 18F-FDG PET in the diagnostic evaluation of cognitive impairment and dementia. Parkinsonian syndromes, also common causes of dementia, can likewise be evaluated with a PET imaging biomarker,18F-DOPA, allowing in vivo assessment of the presynaptic dopaminergic neurons. Understanding the role of these PET biomarkers will help the nuclear medicine physician contribute to the appropriate diagnosis and management of patients with cognitive impairment and dementia. To successfully evaluate brain PET examinations for neurodegenerative diseases, knowledge of the necessary protocol details for obtaining a reliable imaging study, inherent limitations for each PET radiopharmaceutical, and pitfalls in image interpretation is critical. This review will focus on underlying concepts for interpreting PET examinations, important procedural details, and guidance for avoiding potential interpretive pitfalls for amyloid, tau, and dopaminergic PET examinations.

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PET imaging of synaptic vesicle glycoprotein 2A allows for noninvasive quantification of synapses. This first-in-human study aimed to evaluate the kinetics, test-retest reproducibility, and extent of specific binding of a recently developed synaptic vesicle glycoprotein 2A PET ligand, (R)-4-(3-(18F-fluoro)phenyl)-1-((3-methylpyridin-4-yl)methyl)pyrrolidine-2-one (18F-SynVesT-2), with fast brain kinetics. Methods: Nine healthy volunteers participated in this study and were scanned on a High Resolution Research Tomograph scanner with 18F-SynVesT-2. Five volunteers were scanned twice on 2 different days. Five volunteers were rescanned with preinjected levetiracetam (20 mg/kg, intravenously). Arterial blood was collected to calculate the plasma free fraction and generate the arterial input function. Individual MR images were coregistered to a brain atlas to define regions of interest for generating time-activity curves, which were fitted with 1- and 2-tissue-compartment (1TC and 2TC) models to derive the regional distribution volume (V T). The regional nondisplaceable binding potential (BP ND) was calculated from 1TC V T, using the centrum semiovale (CS) as the reference region. Results: 18F-SynVesT-2 was synthesized with high molar activity (187 ± 69 MBq/nmol, n = 19). The parent fraction of 18F-SynVesT-2 in plasma was 28% ± 8% at 30 min after injection, and the plasma free fraction was high (0.29 ± 0.04). 18F-SynVesT-2 entered the brain quickly, with an SUVpeak of 8 within 10 min after injection. Regional time-activity curves fitted well with both the 1TC and the 2TC models; however, V T was estimated more reliably using the 1TC model. The 1TC V T ranged from 1.9 ± 0.2 mL/cm3 in CS to 7.6 ± 0.8 mL/cm3 in the putamen, with low absolute test-retest variability (6.0% ± 3.6%). Regional BP ND ranged from 1.76 ± 0.21 in the hippocampus to 3.06 ± 0.29 in the putamen. A 20-min scan was sufficient to provide reliable V T and BP ND Conclusion: 18F-SynVesT-2 has fast kinetics, high specific uptake, and low nonspecific uptake in the brain. Consistent with the nonhuman primate results, the kinetics of 18F-SynVesT-2 is faster than the kinetics of 11C-UCB-J and 18F-SynVesT-1 in the human brain and enables a shorter dynamic scan to derive physiologic information on cerebral blood flow and synapse density.

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PURPOSE: Positron emission tomography/magnetic resonance imaging (PET/MRI) is a powerful tool for brain imaging, but the spatial resolution of the PET scanners currently used for brain imaging can be further improved to enhance the quantitative accuracy of brain PET imaging. The purpose of this study is to develop an MR-compatible brain PET scanner that can simultaneously achieve a uniform high spatial resolution and high sensitivity by using dual-ended readout depth encoding detectors. METHODS: The MR-compatible brain PET scanner, named SIAT bPET, consists of 224 dual-ended readout detectors. Each detector contains a 26 × 26 lutetium yttrium oxyorthosilicate (LYSO) crystal array of 1.4 × 1.4 × 20 mm3 crystal size read out by two 10 × 10 silicon photomultiplier (SiPM) arrays from both ends. The scanner has a detector ring diameter of 376.8 mm and an axial field of view (FOV) of 329 mm. The performance of the scanner including spatial resolution, sensitivity, count rate, scatter fraction, and image quality was measured. Imaging studies of phantoms and the brain of a volunteer were performed. The mutual interferences of the PET insert and the uMR790 3 T MRI scanner were measured, and simultaneous PET/MRI imaging of the brain of a volunteer was performed. RESULTS: A spatial resolution of better than 1.5 mm with an average of 1.2 mm within the whole FOV was obtained. A sensitivity of 11.0% was achieved at the center FOV for an energy window of 350-750 keV. Except for the dedicated RF coil, which caused a ~ 30% reduction of the sensitivity of the PET scanner, the MRI sequences running had a negligible effect on the performance of the PET scanner. The reduction of the SNR and homogeneity of the MRI images was less than 2% as the PET scanner was inserted to the MRI scanner and powered-on. High quality PET and MRI images of a human brain were obtained from simultaneous PET/MRI scans. CONCLUSION: The SIAT bPET scanner achieved a spatial resolution and sensitivity better than all MR-compatible brain PET scanners developed up to date. It can be used either as a standalone brain PET scanner or a PET insert placed inside a commercial whole-body MRI scanner to perform simultaneous PET/MRI imaging.

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We focus on reviewing state-of-the-art developments of dedicated PET scanners with irregular geometries and the potential of different aspects of multifunctional PET imaging. First, we discuss advances in non-conventional PET detector geometries. Then, we present innovative designs of organ-specific dedicated PET scanners for breast, brain, prostate, and cardiac imaging. We will also review challenges and possible artifacts by image reconstruction algorithms for PET scanners with irregular geometries, such as non-cylindrical and partial angular coverage geometries and how they can be addressed. Then, we attempt to address some open issues about cost/benefits analysis of dedicated PET scanners, how far are the theoretical conceptual designs from the market/clinic, and strategies to reduce fabrication cost without compromising performance.

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Dedicated brain PET scanners are optimized to provide high sensitivity and high spatial resolution compared with existing whole-body PET systems, and they can be much cheaper to produce and install in various clinical and research settings. Advancements in detector technology over the past few years have placed several standalone PET, PET/computed tomography, and PET/MR systems on or near the commercial market; the features and capabilities of these systems will be reviewed here.

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Objective.Time-of-flight (TOF) capability and high sensitivity are essential for brain-dedicated positron emission tomography (PET) imaging, as they improve the contrast and the signal-to-noise ratio (SNR) enabling a precise localization of functional mechanisms in the different brain regions.Approach.We present a new brain PET system with transverse and axial field-of-view (FOV) of 320 mm and 255 mm, respectively. The system head is an array of 6 × 6 detection elements, each consisting of a 3.9 × 3.9 × 20 mm3lutetium-yttrium oxyorthosilicate crystal coupled with a 3.93 × 3.93 mm2SiPM. The SiPMs analog signals are individually digitized using the multi-voltage threshold (MVT) technology, employing a 1:1:1 coupling configuration.Main results.The brain PET system exhibits a TOF resolution of 249 ps at 5.3 kBq ml-1, an average sensitivity of 22.1 cps kBq-1, and a noise equivalent count rate (NECR) peak of 150.9 kcps at 8.36 kBq ml-1. Furthermore, the mini-Derenzo phantom study demonstrated the system's ability to distinguish rods with a diameter of 2.0 mm. Moreover, incorporating the TOF reconstruction algorithm in an image quality phantom study optimizes the background variability, resulting in reductions ranging from 44% (37 mm) to 75% (10 mm) with comparable contrast. In the human brain imaging study, the SNR improved by a factor of 1.7 with the inclusion of TOF, increasing from 27.07 to 46.05. Time-dynamic human brain imaging was performed, showing the distinctive traits of cortex and thalamus uptake, as well as of the arterial and venous flow with 2 s per time frame.Significance.The system exhibited a good TOF capability, which is coupled with the high sensitivity and count rate performance based on the MVT digital sampling technique. The developed TOF-enabled brain PET system opens the possibility of precise kinetic brain PET imaging, towards new quantitative predictive brain diagnostics.

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PURPOSE: Challenges in PET/MRI quantitative accuracy for neurological uses arise from PET attenuation correction accuracy. We proposed and evaluated an automatic pipeline to assess the quantitative accuracy of four MRI-derived PET AC methods using analytically simulated PET brain lesions and ROIs as ground truth for PET activity. METHODS: Our proposed pipeline, integrating a synthetic lesion insertion tool and the FreeSurfer neuroimaging framework, inserts simulated spherical and brain ROIs into PET projection space, reconstructing them via four PET MRAC techniques. Utilizing an 11-patient brain PET dataset, we compared the quantitative accuracy of four MRACs (DIXON, DIXONbone, UTE AC, and DL-DIXON) against the gold standard PET CTAC, evaluating MRAC to CTAC activity bias in spherical lesions and brain ROIs with and without background activity against original (lesion free) PET reconstructed images. RESULTS: The proposed pipeline yielded accurate results for spherical lesions and brain ROIs, adhering to the MRAC to CTAC pattern of original brain PET images. Among the MRAC methods, DIXON AC exhibited the highest bias, followed by UTE, DIXONBone, and DL-DIXON showing the least. DIXON, DIXONbone, UTE, and DL-DIXON showed MRAC to CTAC biases of - 5.41%, - 1.85%, - 2.74%, and 0.08% respectively for ROIs inserted in background activity; - 7.02%, - 2.46%, - 3.56%, and - 0.05% for lesion ROIs without background; and - 6.82%, - 2.08%, - 2.29%, and 0.22% for the original brain PET images' 16 FreeSurfer brain ROIs. CONCLUSION: The proposed pipeline delivers accurate results for synthetic spherical lesions and brain ROIs, with and without background activity consideration, enabling the evaluation of new attenuation correction approaches without utilizing measured PET emission data. Additionally, it offers a consistent method to generate realistic lesion ROIs, potentially applicable in assessing further PET correction techniques.

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BACKGROUND: Image harmonization has been proposed to minimize heterogeneity in brain PET scans acquired in multi-center studies. However, standard validated methods and software tools are lacking. Here, we assessed the performance of a framework for the harmonization of brain PET scans in a multi-center European clinical trial. METHOD: Hoffman 3D brain phantoms were acquired in 28 PET systems and reconstructed using site-specific settings. Full Width at Half Maximum (FWHM) of the Effective Image Resolution (EIR) and harmonization kernels were estimated for each scan. The target EIR was selected as the coarsest EIR in the imaging network. Using "Hoffman 3D brain Analysis tool," indicators of image quality were calculated before and after the harmonization: The Coefficient of Variance (COV%), Gray Matter Recovery Coefficient (GMRC), Contrast, Cold-Spot RC, and left-to-right GMRC ratio. A COV% ≤ 15% and Contrast ≥ 2.2 were set as acceptance criteria. The procedure was repeated to achieve a 6-mm target EIR in a subset of scans. The method's robustness against typical dose-calibrator-based errors was assessed. RESULTS: The EIR across systems ranged from 3.3 to 8.1 mm, and an EIR of 8 mm was selected as the target resolution. After harmonization, all scans met acceptable image quality criteria, while only 13 (39.4%) did before. The harmonization procedure resulted in lower inter-system variability indicators: Mean ± SD COV% (from 16.97 ± 6.03 to 7.86 ± 1.47%), GMRC Inter-Quartile Range (0.040-0.012), and Contrast SD (0.14-0.05). Similar results were obtained with a 6-mm FWHM target EIR. Errors of ± 10% in the DRO activity resulted in differences below 1 mm in the estimated EIR. CONCLUSION: Harmonizing the EIR of brain PET scans significantly reduced image quality variability while minimally affecting quantitative accuracy. This method can be used prospectively for harmonizing scans to target sharper resolutions and is robust against dose-calibrator errors. Comparable image quality is attainable in brain PET multi-center studies while maintaining quantitative accuracy.

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BACKGROUND AND PURPOSE: Chimeric antigen receptor (CAR) T-cell therapy is potentially associated with treatment-related toxicities mainly consisting of cytokine release syndrome (CRS) and immune-effector cell-associated neurotoxicity syndrome (ICANS). We evaluated brain metabolic correlates of CRS with and without ICANS in diffuse large B-cell lymphoma patients treated with CAR-T. METHODS: Twenty-one refractory DLCBLs underwent whole-body and brain [18 F]-fluorodeoxyglucose (FDG) PET before and 30 days after treatment with CAR-T. Five patients did not develop inflammatory-related side effects, 11 patients developed CRS, while in 5 patients CRS evolved in ICANS. Baseline and post-CAR-T brain FDG-PET were compared with a local controls dataset to identify hypometabolic patterns both at single-patient and group levels (p < .05 after correction for family-wise error [FWE). Metabolic tumor volume (MTV) and total lesion glycolysis (TLG) were computed on baseline FDG-PET and compared between patients' subgroups (t-test). RESULTS: ICANS showed an extended and bilateral hypometabolic pattern mainly involving the orbitofrontal cortex, frontal dorsolateral cortex, and anterior cingulate (p < .003 FWE-corrected). CRS without ICANS showed significant hypometabolism in less extended clusters mainly involving bilateral medial and lateral temporal lobes, posterior parietal lobes, anterior cingulate, and cerebellum (p < .002 FWE-corrected). When compared, ICANS showed a more prominent hypometabolism in the orbitofrontal and frontal dorsolateral cortex in both hemispheres than CRS (p < .002 FWE-corrected). Mean baseline MTV and TLG were significantly higher in ICANS than CRS (p < .02). CONCLUSIONS: Patients with ICANS are characterized by a frontolateral hypometabolic signature coherently with the hypothesis of ICANS as a predominant frontal syndrome and with the more prominent susceptibility of frontal lobes to cytokine-induced inflammation.

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Purpose: PET/MRI quantitative accuracy for neurological applications is challenging due to accuracy of the PET attenuation correction. In this work, we proposed and evaluated an automatic pipeline for assessing the quantitative accuracy of four different MRI = based attenuation correction (PET MRAC) approaches. Methods: The proposed pipeline consists of a synthetic lesion insertion tool and the FreeSurfer neuroimaging analysis framework. The synthetic lesion insertion tool is used to insert simulated spherical, and brain regions of interest (ROI) into the PET projection space and reconstructed with four different PET MRAC techniques, while FreeSurfer is used to generate brain ROIs from T1 weighted MRI image. Using a cohort of 11 patients' brain PET dataset, the quantitative accuracy of four MRAC(s), which are: DIXON AC, DIXONbone AC, UTE AC, and Deep learning trained with DIXON AC, named DL-DIXON AC, were compared to the PET-based CT attenuation correction (PET CTAC). MRAC to CTAC activity bias in spherical lesions and brain ROIs were reconstructed with and without background activity and compared to the original PET images. Results: The proposed pipeline provides accurate and consistent results for inserted spherical lesions and brain ROIs inserted with and without considering the background activity and following the same MRAC to CTAC pattern as the original brain PET images. As expected, the DIXON AC showed the highest bias; the second was for the UTE, then the DIXONBone, and the DL-DIXON with the lowest bias. For simulated ROIs inserted in the background activity, DIXON showed a -4.65% MRAC to CTAC bias, 0.06% for the DIXONbone, -1.70% for the UTE, and - 0.23% for the DL-DIXON. For lesion ROIs inserted without background activity, DIXON showed a -5.21%, -1% for the DIXONbone, -2.55% for the UTE, and - 0.52 for the DL-DIXON. For MRAC to CTAC bias calculated using the same 16 FreeSurfer brain ROIs in the original brain PET reconstructed images, a 6.87% was observed for the DIXON, -1.83% for DIXON bone, -3.01% for the UTE, and - 0.17% for the DL-DIXON. Conclusion: The proposed pipeline provides accurate and consistent results for synthetic spherical lesions and brain ROIs inserted with and without considering the background activity; hence a new attenuation correction approach can be evaluated without using measured PET emission data.

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Amyotrophic lateral sclerosis (ALS) is a fatal and progressive neurodegenerative disorder involving both upper and lower motor neurons. Interestingly, 15 to 41% of patients with ALS have concomitant frontotemporal dementia (FTD). Approximately, 50% of patients with ALS can copresent with a broader set of neuropsychological pathologies that do not meet FTD diagnostic criteria. This association resulted in revised and expanded criteria establishing the ALS-frontotemporal spectrum disorder (FTSD). In this case report, we review background information, epidemiology, pathophysiology, and structural and molecular imaging features of ALS-FTSD.

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Recently, retinoid actions on the central nervous system (CNS) have attracted considerable attention from the perspectives of brain disease diagnosis and drug development. Firstly, we successfully synthesized [11C]peretinoin esters (methyl, ethyl, and benzyl) using a Pd(0)-mediated rapid C-[11C]methylation of the corresponding stannyl precursors without geometrical isomerization in 82%, 66%, and 57% radiochemical yields (RCYs). Subsequent hydrolysis of the 11C-labeled ester produced [11C]peretinoin in 13 ± 8% RCY (n = 3). After pharmaceutical formulation, the resulting [11C]benzyl ester and [11C]peretinoin had high radiochemical purity (>99% each) and molar activities of 144 and 118 ± 49 GBq µmol-1 at total synthesis times of 31 min and 40 ± 3 min, respectively. Rat brain PET imaging for the [11C]ester revealed a unique time-radioactivity curve, suggesting the participation of the acid [11C]peretinoin for the brain permeability. However, the curve of the [11C]peretinoin rose steadily after a shorter time lag to reach 1.4 standardized uptake value (SUV) at 60 min. These various phenomena between the ester and acid became more pronounced in the monkey brain (SUV of > 3.0 at 90 min). With the opportunity to identify high brain uptake of [11C]peretinoin, we discovered CNS activities of a drug candidate called peretinoin, such as the induction of a stem-cell to neuronal cell differentiation and the suppression of neuronal damages.

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PURPOSE: Ketogenic diet (KD) is recommended to avoid intense [18F]FDG myocardial physiologic uptake in PET imaging. Neuroprotective and anti-seizure effects of KD have been suggested, but their mechanisms remain to be elucidated. This [18F]FDG PET study aims to evaluate the effect of KD on glucose brain metabolism. METHOD: Subjects who underwent KD prior to whole-body and brain [18F]FDG PET between January 2019 and December 2020 in our department for suspected endocarditis were retrospectively included. Myocardial glucose suppression (MGS) on whole-body PET was analyzed. Patients with brain abnormalities were excluded. Thirty-four subjects with MGS (mean age: 61.8 ± 17.2 years) were included in the KD population, and 14 subjects without MGS were considered for a partial KD group (mean age: 62.3 ± 15.1 years). Brain SUVmax was first compared between these two KD groups to determine possible global uptake difference. Semiquantitative voxel-based intergroup analyses were secondarily performed to determine possible inter-regional differences by comparing KD groups with and without MGS, separately, to 27 healthy subjects fasting for at least 6 h (mean age of 62.4 ± 10.9 years), and KD groups between them (p-voxel < 0.001, and p-cluster < 0.05, FWE-corrected). RESULTS: A 20% lower brain SUVmax was found in subjects under KD with MGS in comparison to those without MGS (Student's t-test, p = 0.02). Whole-brain voxel-based intergroup analysis revealed that patients under KD with and without MGS had relative hypermetabolism of limbic regions including medial temporal cortices and cerebellum lobes and relative hypometabolism of bilateral posterior regions (occipital), without significant difference between them. CONCLUSION: KD globally reduces brain glucose metabolism but with regional differences, requiring special attention to clinical interpretation. On a pathophysiological perspective, these findings could help understand underlying neurological effects of KD through possible decrease of oxidative stress in posterior regions and functional compensation in the limbic regions.

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Background: In medical imaging, images are usually treated as deterministic, while their uncertainties are largely underexplored. Purpose: This work aims at using deep learning to efficiently estimate posterior distributions of imaging parameters, which in turn can be used to derive the most probable parameters as well as their uncertainties. Methods: Our deep learning-based approaches are based on a variational Bayesian inference framework, which is implemented using two different deep neural networks based on conditional variational auto-encoder (CVAE), CVAE-dual-encoder and CVAE-dual-decoder. The conventional CVAE framework, i.e., CVAE-vanilla, can be regarded as a simplified case of these two neural networks. We applied these approaches to a simulation study of dynamic brain PET imaging using a reference region-based kinetic model. Results: In the simulation study, we estimated posterior distributions of PET kinetic parameters given a measurement of time-activity curve. Our proposed CVAE-dual-encoder and CVAE-dual-decoder yield results that are in good agreement with the asymptotically unbiased posterior distributions sampled by Markov Chain Monte Carlo (MCMC). The CVAE-vanilla can also be used for estimating posterior distributions, although it has an inferior performance to both CVAE-dual-encoder and CVAE-dual-decoder. Conclusions: We have evaluated the performance of our deep learning approaches for estimating posterior distributions in dynamic brain PET. Our deep learning approaches yield posterior distributions, which are in good agreement with unbiased distributions estimated by MCMC. All these neural networks have different characteristics and can be chosen by the user for specific applications. The proposed methods are general and can be adapted to other problems.