Background: Positron emission tomography (PET) has had a transformative impact on oncological and neurological applications. However, still much of PET's potential remains untapped with limitations primarily driven by low spatial resolution, which severely hampers accurate quantitative PET imaging via the partial volume effect (PVE). Purpose: We present experimental results of a practical and cost-effective ultra-high resolution brain-dedicated PET scanner, using our depth-encoding Prism-PET detectors arranged along a compact and conformal gantry, showing substantial reduction in PVE and accurate radiotracer uptake quantification in small regions. Methods: The decagon-shaped prototype scanner has a long diameter of 38.5 cm, a short diameter of 29.1 cm, and an axial field-of -view (FOV) of 25.5 mm with a single ring of 40 Prism-PET detector modules. Each module comprises a 16 × 16 array of 1.5 × 1.5 × 20-mm 3 lutetium yttrium oxyorthosillicate (LYSO) scintillator crystals coupled 4-to-1 to an 8 × 8 array of silicon photomultiplier (SiPM) pixels on one end and to a prismatoid light guide array on the opposite end. The scanner's performance was evaluated by measuring depth-of -interaction (DOI) resolution, energy resolution, timing resolution, spatial resolution, sensitivity, and image quality of ultra-micro Derenzo and three-dimensional (3D) Hoffman brain phantoms. Results:The full width at half maximum (FWHM) DOI, energy, and timing resolutions of the scanner are 2.85 mm, 12.6%, and 271 ps, respectively. Not considering artifacts due to mechanical misalignment of detector blocks, the intrinsic spatial resolution is 0.89-mm FWHM. Point source images reconstructed with 3D filtered back-projection (FBP) show an average spatial resolution of 1.53-mm FWHM across the entire FOV. The peak absolute sensitivity is 1.2% for an energy window of 400−650 keV. The ultra-micro Derenzo phantom study demonstrates the highest reported spatial resolution performance for a human brain PET scanner with perfect reconstruction of 1.00-mm diameter hot-rods. Reconstructed images of customized Hoffman brain phantoms prove that Prism-PET enables accurate radiotracer uptake quantification in small brain regions (2-3 mm).Xinjie Zeng and Zipai Wang contributed equally to this study.

Purpose Quantitative in vivo molecular imaging of fine brain structures requires high‐spatial resolution and high‐sensitivity. Positron emission tomography (PET) is an attractive candidate to introduce molecular imaging into standard clinical care due to its highly targeted and versatile imaging capabilities based on the radiotracer being used. However, PET suffers from relatively poor spatial resolution compared to other clinical imaging modalities, which limits its ability to accurately quantify radiotracer uptake in brain regions and nuclei smaller than 3 mm in diameter. Here we introduce a new practical and cost‐effective high‐resolution and high‐sensitivity brain‐dedicated PET scanner, using our depth‐encoding Prism‐PET detector modules arranged in a conformal decagon geometry, to substantially reduce the partial volume effect and enable accurate radiotracer uptake quantification in small subcortical nuclei. Methods Two Prism‐PET brain scanner setups were proposed based on our 4‐to‐1 and 9‐to‐1 coupling of scintillators to readout pixels using 1.5×1.5×20$1.5 \times 1.5 \times 20$ mm3 and 0.987×0.987×20$0.987 \times 0.987 \times 20$ mm3 crystal columns, respectively. Monte Carlo simulations of our Prism‐PET scanners, Siemens Biograph Vision, and United Imaging EXPLORER were performed using Geant4 application for tomographic emission (GATE). National Electrical Manufacturers Association (NEMA) standard was followed for the evaluation of spatial resolution, sensitivity, and count‐rate performance. An ultra‐micro hot spot phantom was simulated for assessing image quality. A modified Zubal brain phantom was utilized for radiotracer imaging simulations of 5‐HT1A receptors, which are abundant in the raphe nuclei (RN), and norepinephrine transporters, which are highly concentrated in the bilateral locus coeruleus (LC). Results The Prism‐PET brain scanner with 1.5 mm crystals is superior to that with 1 mm crystals as the former offers better depth‐of‐interaction (DOI) resolution, which is key to realizing compact and conformal PET scanner geometries. We achieved uniform 1.3 mm full‐width‐at‐half‐maximum (FWHM) spatial resolutions across the entire transaxial field‐of‐view (FOV), a NEMA sensitivity of 52.1 kcps/MBq, and a peak noise equivalent count rate (NECR) of 957.8 kcps at 25.2 kBq/mL using 450–650 keV energy window. Hot spot phantom results demonstrate that our scanner can resolve regions as small as 1.35 mm in diameter at both center and 10 cm away from the center of the transaixal FOV. Both 5‐HT1A receptor and norepinephrine transporter brain simulations prove that our Prism‐PET scanner enables accurate quantification of radiotracer uptake in small brain regions, with a 1.8‐fold and 2.6‐fold improvement in the dorsal RN as well as a 3.2‐fold and 4.4‐fold improvement in the bilateral LC compared to the Biograph Vision and EXPLORER, respectively. Conclusions Based on our simulation results, the proposed high‐resolution and high‐sensitivity Prism‐PET brain scanner is a promising cost‐effective candidate ...

Depth-of-interaction (DOI) positron emission tomography (PET) detector with time-of-flight (TOF) readout has been developed in recent years. With DOI, PET scanner could achieve high spatial resolution and high sensitivity simultaneously. With improved TOF readout, better coincidence time resolution (CTR) is achieved, making the localization of events along the lines of response (LORs) more accurate and enhancing the reconstructed image quality. Prism-PET is a novel DOI-TOF PET detector module with single-ended readout. It utilizes segmented prismatoid light guide array for enhanced and localized light sharing and aims to solve the tradeoff among spatial resolution, sensitivity, and cost. In this work, we characterized the DOI resolution and CTR performance of our Prism-PET module. Unprecedented DOI resolutions of 2.57 mm full width at half maximum (FWHM) and 4.01 mm FWHM were achieved in our 4-to-1 and 9-to-1 crystal-to-pixel coupled Prism-PET modules, respectively, outperforming that of a 4-to-1 PET module with a uniform glass light guide (5.72 mm FWHM). Estimated time resolutions (FWHM) after DOI correction were 254 ps and 267 ps for the 4-to-1 and 9-to-1 Prism-PET modules, respectively.

Purpose Depth of interaction (DOI) readout in PET imaging has been researched in efforts to mitigate parallax error, which would enable the development of small diameter, high‐resolution PET scanners. However, DOI PET has not yet been commercialized due to the lack of practical, cost‐effective, and data efficient DOI readout methods. The rationale for this study was to develop a supervised machine learning algorithm for DOI estimation in PET that can be trained and deployed on unique sets of crystals. Methods Depth collimated flood data was experimentally acquired using a Na‐22 source with a depth‐encoding single‐ended readout Prism‐PET module consisting of lutetium yttrium orthosilicate (LYSO) crystals coupled 4‐to‐1 to 3×3 mm2 silicon photomultiplier (SiPM) pixels on one end and a segmented prismatoid light guide array on the other end. A convolutional neural network (CNN) was trained to perform DOI estimation on data from center, edge and corner crystals in the Prism‐PET module using (a) all non‐zero readout pixels and (b) only the 4 highest readout signals per event. CNN testing was performed on data from crystals not included in CNN training. Results An average DOI resolution of 1.84 mm full width at half maximum (FWHM) across all crystals was achieved when using all readout signals per event with the CNN compared to 3.04 mm FWHM DOI resolution using classical estimation. When using only the 4 highest signals per event, an average DOI resolution of 1.92 mm FWHM was achieved, representing only a 4% dropoff in CNN performance compared to using all non‐zero pixels per event. Conclusions Our CNN‐based DOI estimation algorithm provides the best reported DOI resolution in a single‐ended readout module and can be readily deployed on crystals not used for model training.