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Purpose: Aging is a major societal concern due to age-related functional losses. Synapses are crucial components of neural circuits, and synaptic density could be a sensitive biomarker to evaluate brain function. [11C]UCB-J is a positron emission tomography (PET) ligand targeting synaptic vesicle glycoprotein 2A (SV2A), which can be used to evaluate brain synaptic density in vivo. Methods: We evaluated age-related changes in gray matter synaptic density, volume, and blood flow using [11C]UCB-J PET and magnetic resonance imaging (MRI) in a wide age range of 80 cognitive normal subjects (21–83 years old). Partial volume correction was applied to the PET data. Results: Significant age-related decreases were found in 13, two, and nine brain regions for volume, synaptic density, and blood flow, respectively. The prefrontal cortex showed the largest volume decline (4.9% reduction per decade: RPD), while the synaptic density loss was largest in the caudate (3.6% RPD) and medial occipital cortex (3.4% RPD). The reductions in caudate are consistent with previous SV2A PET studies and likely reflect that caudate is the site of nerve terminals for multiple major tracts that undergo substantial age-related neurodegeneration. There was a non-significant negative relationship between volume and synaptic density reductions in 16 gray matter regions. Conclusion: MRI and [11]C-UCB-J PET showed age-related decreases of gray matter volume, synaptic density, and blood flow; however, the regional patterns of the reductions in volume and SV2A binding were different. Those patterns suggest that MR-based measures of GM volume may not be directly representative of synaptic density. [ABSTRACT FROM AUTHOR]

A novel deep learning (DL)-based attenuation correction (AC) framework was applied to clinical whole-body oncology studies using 18F-FDG, 68 Ga-DOTATATE, and 18F-Fluciclovine. The framework used activity (λ-MLAA) and attenuation (µ-MLAA) maps estimated by the maximum likelihood reconstruction of activity and attenuation (MLAA) algorithm as inputs to a modified U-net neural network with a novel imaging physics-based loss function to learn a CT-derived attenuation map (µ-CT). Methods: Clinical whole-body PET/CT datasets of 18F-FDG (N = 113), 68 Ga-DOTATATE (N = 76), and 18F-Fluciclovine (N = 90) were used to train and test tracer-specific neural networks. For each tracer, forty subjects were used to train the neural network to predict attenuation maps (µ-DL). µ-DL and µ-MLAA were compared to the gold-standard µ-CT. PET images reconstructed using the OSEM algorithm with µ-DL (OSEMDL) and µ-MLAA (OSEMMLAA) were compared to the CT-based reconstruction (OSEMCT). Tumor regions of interest were segmented by two radiologists and tumor SUV and volume measures were reported, as well as evaluation using conventional image analysis metrics. Results: µ-DL yielded high resolution and fine detail recovery of the attenuation map, which was superior in quality as compared to µ-MLAA in all metrics for all tracers. Using OSEMCT as the gold-standard, OSEMDL provided more accurate tumor quantification than OSEMMLAA for all three tracers, e.g., error in SUVmax for OSEMMLAA vs. OSEMDL: − 3.6 ± 4.4% vs. − 1.7 ± 4.5% for 18F-FDG (N = 152), − 4.3 ± 5.1% vs. 0.4 ± 2.8% for 68 Ga-DOTATATE (N = 70), and − 7.3 ± 2.9% vs. − 2.8 ± 2.3% for 18F-Fluciclovine (N = 44). OSEMDL also yielded more accurate tumor volume measures than OSEMMLAA, i.e., − 8.4 ± 14.5% (OSEMMLAA) vs. − 3.0 ± 15.0% for 18F-FDG, − 14.1 ± 19.7% vs. 1.8 ± 11.6% for 68 Ga-DOTATATE, and − 15.9 ± 9.1% vs. − 6.4 ± 6.4% for 18F-Fluciclovine. Conclusions: The proposed framework provides accurate and robust attenuation correction for whole-body 18F-FDG, 68 Ga-DOTATATE and 18F-Fluciclovine in tumor SUV measures as well as tumor volume estimation. The proposed method provides clinically equivalent quality as compared to CT in attenuation correction for the three tracers. [ABSTRACT FROM AUTHOR]

Purpose: The net uptake rate constant (Ki) derived from dynamic imaging is considered the gold standard quantification index for FDG PET. In this study, we investigated the feasibility and assessed the clinical usefulness of generating Ki images for FDG PET using only two 5‐min scans with population‐based input function (PBIF). Methods: Using a Siemens Biograph mCT, 10 subjects with solid lung nodules underwent a single‐bed dynamic FDG PET scan and 13 subjects (five healthy and eight cancer patients) underwent a whole‐body dynamic FDG PET scan in continuous‐bed‐motion mode. For each subject, a standard Ki image was generated using the complete 0–90 min dynamic data with Patlak analysis (t* = 20 min) and individual patient's input function, while a dual‐time‐point Ki image was generated from two 5‐min scans based on the Patlak equations at early and late scans with the PBIF. Different start times for the early (ranging from 20 to 55 min with an increment of 5 min) and late (ranging from 50 to 85 min with an increment of 5 min) scans were investigated with the interval between scans being at least 30 min (36 protocols in total). The optimal dual‐time‐point protocols were then identified. Regions of interest (ROI) were drawn on nodules for the lung nodule subjects, and on tumors, cerebellum, and bone marrow for the whole‐body‐imaging subjects. Quantification accuracy was compared using the mean value of each ROI between standard Ki (gold standard) and dual‐time‐point Ki, as well as between standard Ki and relative standardized uptake value (SUV) change that is currently used in clinical practice. Correlation coefficients and least squares fits were calculated for each dual‐time‐point protocol and for each ROI. Then, the predefined criteria for identifying a reliable dual‐time‐point Ki estimation for each ROI were empirically determined as: (1) the squared correlation coefficient (R2) between standard Ki and dual‐time‐point Ki is larger than 0.9; (2) the absolute difference between the slope of the equality line (1.0) and that of the fitted line when plotting standard Ki versus dual‐time‐point Ki is smaller than 0.1; (3) the absolute value of the intercept of the fitted line when plotting standard Ki versus dual‐time‐point Ki normalized by the mean of the standard Ki across all subjects for each ROI is smaller than 10%. Using Williams' one‐tailed t test, the correlation coefficient (R) between standard Ki and dual‐time‐point Ki was further compared with that between standard Ki and relative SUV change, for each dual‐time‐point protocol and for each ROI. Results: Reliable dual‐time‐point Ki images were obtained for all the subjects using our proposed method. The percentage error introduced by the PBIF on the dual‐time‐point Ki estimation was smaller than 1% for all 36 protocols. Using the predefined criteria, reliable dual‐time‐point Ki estimation could be obtained in 25 of 36 protocols for nodules and in 34 of 36 protocols for tumors. A longer time interval between scans provided a more accurate Ki estimation in general. Using the protocol of 20–25 min plus 80–85 or 85–90 min, very high correlations were obtained between standard Ki and dual‐time‐point Ki (R2 = 0.994, 0.980, 0.971 and 0.925 for nodule, tumor, cerebellum, and bone marrow), with all the slope values with differences ≤0.033 from 1 and all the intercept values with differences ≤0.0006 mL/min/cm3 from 0. The corresponding correlations were much lower between standard Ki and relative SUV change (R2 = 0.673, 0.684, 0.065, 0.246). Dual‐time‐point Ki showed a significantly higher quantification accuracy with respect to standard Ki than relative SUV change for all the 36 protocols (p < 0.05 using Williams' one‐tailed t test). Conclusions: Our proposed approach can obtain reliable Ki images and accurate Ki quantification from dual‐time‐point scans (5‐min per scan), and provide significantly higher quantification accuracy than relative SUV change that is currently used in clinical practice. [ABSTRACT FROM AUTHOR]