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Objective: To verify the visibility of physiological 18 F-fluorodeoxyglucose ( 18 F-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 18 F-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 18 F-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 18 F-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., (© 2023. The Author(s).)

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., (© 2023. The Author(s).)

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 18 F-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 SUV max 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 SUV max 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.

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 (CMRO 2 ) were measured on healthy men by PET with 15 O-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 15 O-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., (© 2023. The Physiological Society of Japan.)

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 18 F-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., (© 2022. The Author(s).)

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 [ 11 C]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 [ 11 C]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., (© 2022. The Author(s).)

As COVID-19 continues to spread globally, monitoring the disease at different scales is critical to support public health decision making. Surveillance for SARS-CoV-2 RNA in wastewater can supplement surveillance based on diagnostic testing. In this paper, we report the results of wastewater-based COVID-19 surveillance on Emory University campus that included routine sampling of sewage from a hospital building, an isolation/quarantine building, and 21 student residence halls between July 13th, 2020 and March 14th, 2021. We examined the sensitivity of wastewater surveillance for detecting COVID-19 cases at building level and the relation between Ct values from RT-qPCR results of wastewater samples and the number of COVID-19 patients residing in the building. Our results show that weekly wastewater surveillance using Moore swab samples was not sensitive enough (6 of 63 times) to reliably detect one or two sporadic cases in a residence building. The Ct values of the wastewater samples over time from the same sampling location reflected the temporal trend in the number of COVID-19 patients in the isolation/quarantine building and hospital (Pearson's r < -0.8), but there is too much uncertainty to directly estimate the number of COVID-19 cases using Ct values. After students returned for the spring 2021 semester, SARS-CoV-2 RNA was detected in the wastewater samples from most of the student residence hall monitoring sites one to two weeks before COVID-19 cases surged on campus. This finding suggests that wastewater-based surveillance can be used to provide early warning of COVID-19 outbreaks at institutions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

SARS-CoV-2 is a respiratory virus, but it is also detected in a significant proportion of fecal samples from COVID-19 cases. Recent studies have shown that wastewater surveillance can be a low-cost tool compared to massive diagnostic testing for tracking COVID-19 outbreaks in communities, but most studies have focused on sampling from wastewater treatment plants. Institutional level wastewater surveillance may serve well for early warning purposes because specific geographic areas/populations with emerging cases can be tracked and immediate action can be executed in the event of a positive wastewater signal. In this study, a novel Moore swab method was developed and used for wastewater surveillance of COVID-19 at an institutional level. Of the 442 swab samples tested, 148 (33.5%) swabs collected from the three campuses and two buildings were positive for SARS-CoV-2 RNA. Further study of the quarantine building with a known number of cases indicated that this method was sensitive enough to detect few cases in the building. In addition, comparison between grab samples and Moore swab samples from the hospital sewage line indicated that Moore swabs were more sensitive than grab samples and offer a simple, inexpensive method for obtaining a composite sample of virus in wastewater over a 24-48 h period. These results suggest that collection and analyses of Moore swabs for SARS-CoV-2 RNA detection is a sensitive, low-cost, and easy to use tool for COVID-19 surveillance that is useful for institutional settings and could be deployed in low-resource settings to identify emerging COVID-19 clusters in communities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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., (© 2021. The Author(s) under exclusive licence to The Japanese Society of Nuclear Medicine.)

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 15 O-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 15 O-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., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)