Increasing the image quality of positron emission tomography (PET) is an essential topic in the PET community. For instance, thin-pixelated crystals have been used to provide high spatial resolution images but at the cost of sensitivity and manufacture expense. In this paper, we proposed an approach to enhance the PET image resolution and noise property for PET scanners with large pixelated crystals. To address the problem of coarse blurred sinograms with large parallax errors associated with large crystals, we developed a data-driven, single-image superresolution (SISR) method for sinograms, based on the novel deep residual convolutional neural network (CNN). Unlike the CNN-based SISR on natural images, periodically padded sinogram data and dedicated network architecture were used to make it more efficient for PET imaging. Moreover, we included the transfer learning scheme in the approach to process cases with poor labeling and small training data set. The approach was validated via analytically simulated data (with and without noise), Monte Carlo simulated data, and pre-clinical data. Using the proposed method, we could achieve comparable image resolution and better noise property with large crystals of bin sizes 4 × of thin crystals with a bin size from 1 × 1 mm ² to 1.6 × 1.6 mm ² . Our approach uses external PET data as the prior knowledge for training and does not require additional information during inference. Meanwhile, the method can be added into the normal PET imaging framework seamlessly, thus potentially finds its application in designing low-cost high-performance PET systems.

We present a nonuniform multiphase (NUMP) method to construct a high-resolution time-to-digital converter (TDC) for low-cost field-programmable gate array (FPGA) devices. The NUMP method involves a system clock being passed through a series of delay elements to generate multiple clocks with different phase shifts. The phases of the rising and falling edges of all the clocks are sorted in order and the states of all the clocks are latched when a hit signal arrives. The sizes of the time bins (and precision) of the NUMP method are not limited by the uniformity and minimum value of the time delays of the delay lines. In theory, any delay sources with small jitters in an FPGA, not just very fine carry chains, can be used in the NUMP method to delay and randomize the clocks. Thus, the NUMP method can achieve excellent TDC timing resolutions in low-cost FPGAs without very fine delay lines. We implemented four NUMP TDC channels in a low-cost FPGA device (an Altera Cyclone V 5CEBA4F23C7N). The performance of the four NUMP TDCs was evaluated using both internal and external pulses. The root mean square (rms) for the timing resolution measured using the internal and the external pulses with short-time intervals (less than 1 ns) was 2.3 and 5.2 ps, respectively. A 14.1-ps rms timing resolution was measured at a time interval of 517 ns. The NUMP method is suitable for applications that require a number of high-performance TDC channels in a low-cost FPGA.

Background Translocator protein (TSPO) PET has been used to visualize microglial activation in neuroinflammation and is a potential imaging tool for detecting autoimmune encephalitis (AIE). Purpose To compare the detection rate between TSPO radioligand fluorine 18 (¹⁸F) DPA-714 PET and conventional MRI and assess the relationship between ¹⁸F-DPA-714 uptake and clinical features in participants with AIE. Materials and Methods Healthy volunteers and patients with AIE were enrolled in this prospective study between December 2021 and April 2023. All participants underwent hybrid brain ¹⁸F-DPA-714 PET/MRI and antibody testing. Modified Rankin scale scoring and AIE-related symptoms were assessed in participants with AIE. Positive findings were defined as intensity of ¹⁸F-DPA-714 uptake above a threshold of the mean standardized uptake value ratio (SUVR) plus 2 SD inside the corresponding brain regions of healthy controls. The McNemar test was used to compare the positive detection rate between the two imaging modalities; the independent samples t test was used to compare continuous variables; and correlation with Bonferroni correction was used to assess the relationship between ¹⁸F-DPA-714 uptake and clinical features. Results A total of 25 participants with AIE (mean age, 39.24 years ± 19.03 [SD]) and 10 healthy controls (mean age, 28.70 years ± 5.14) were included. The positive detection rate of AIE was 72% (18 of 25) using ¹⁸F-DPA-714 PET compared to 44% (11 of 25) using conventional MRI, but the difference was not statistically significant (P = .065). Participants experiencing seizures exhibited significantly higher mean SUVR in the entire cortical region than those without seizures (1.23 ± 0.21 vs 1.15 ± 0.18; P = .003). Of the 13 participants with AIE who underwent follow-up PET/MRI, 11 (85%) demonstrated reduced uptake of ¹⁸F-DPA-714 accompanied by relief of symptoms after immunosuppressive treatment. Conclusion ¹⁸F-DPA-714 PET has potential value in supplementing MRI for AIE detection. Clinical trial registration no. NCT05293405 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.

The performance of radiation detectors used in positron-emission tomography (PET) is determined by the intrinsic properties of the scintillators, the geometry and surface treatment of the scintillator crystals and the electrical and optical characteristics of the photosensors. Experimental studies were performed to assess the timing resolution and energy resolution of detectors constructed with samples of different scintillator materials (LaBr₃, CeBr₃, LFS, LSO, LYSO: Ce, Ca and GAGG) that were fabricated into different shapes with various surface treatments. The saturation correction of SiPMs was applied for tested detectors based on a Tracepro simulation. Overall, we tested 28 pairs of different forms of scintillators to determine the one with the best CTR and light output. Two common high-performance silicon photomultipliers (SiPMs) provided by SensL (J-series, 6 mm) or AdvanSiD (NUV, 6 mm) were used for photodetectors. The PET detector constructed with 6 mm CeBr₃ cubes achieved the best CTR with a FWHM of 74 ps. The 4 mm co-doped LYSO: Ce, Ca pyramid crystals achieved 88.1 ps FWHM CTR. The 2 mm, 4 mm and 6 mm 0.2% Ce, 0.1% Ca co-doped LYSO cubes achieved 95.6 ps, 106 ps and 129 ps FWHM CTR, respectively. The scintillator crystals with unpolished surfaces had better timing than those with polished surfaces. The timing resolution was also improved by using certain geometric factors, such as a pyramid shape, to improve light transportation in the scintillator crystals.

68Ga-DOTATATE PET-CT is routinely used for imaging neuroendocrine tumor (NET) somatostatin receptor subtype 2 (SSTR2) density in patients, and is complementary to FDG PET-CT for improving the accuracy of NET detection, characterization, grading, staging, and predicting/monitoring NET responses to treatment. Performing sequential 18F-FDG and 68Ga-DOTATATE PET scans would require 2 or more days and can delay patient care. To align temporal and spatial measurements of 18F-FDG and 68Ga-DOTATATE PET, and to reduce scan time and CT radiation exposure to patients, we propose a single-imaging session dual-tracer dynamic PET acquisition protocol in the study. A recurrent extreme gradient boosting (rXGBoost) machine learning algorithm was proposed to separate the mixed 18F-FDG and 68Ga-DOTATATE time activity curves (TACs) for the region of interest (ROI) based quantification with tracer kinetic modeling. A conventional parallel multi-tracer compartment modeling method was also implemented for reference. Single-scan dual-tracer dynamic PET was simulated from 12 NET patient studies with 18F-FDG and 68Ga-DOTATATE 45-min dynamic PET scans separately obtained within 2 days. Our experimental results suggested an 18F-FDG injection first followed by 68Ga-DOTATATE with a minimum 5 min delayed injection protocol for the separation of mixed 18F-FDG and 68Ga-DOTATATE TACs using rXGBoost algorithm followed by tracer kinetic modeling is highly feasible.