Depth-encoding using optical photon TOF in a prism-PET detector with tapered crystals.

BACKGROUND: High-resolution brain positron emission tomography (PET) scanner is emerging as a significant and transformative non-invasive neuroimaging tool to advance neuroscience research as well as improve diagnosis and treatment in neurology and psychiatry. Time-of-flight (TOF) and depth-of-interaction (DOI) information provide markedly higher PET imaging performance by increasing image signal-to-noise ratio and mitigating spatial resolution degradation due to parallax error, respectively. PET detector modules that utilize light sharing can inherently carry DOI information from the multiple timestamps that are generated per gamma event. The difference between two timestamps that are triggered by scintillation photons traveling in opposite directions signifies the event's depth-dependent optical photon TOF (oTOF). However, light leak at the crystal-readout interface substantially degrades the resolution of this oTOF-based depth encoding. PURPOSE: We demonstrate the feasibility of oTOF-based depth encoding by mitigating light leak in single-ended-readout Prism-PET detector modules using tapered crystals. Minimizing light leak also improved both energy-based DOI and coincidence timing resolutions. METHODS: The tapered Prism-PET module consists of a 16  × $\times$  16 array of 1.5  × $\times$  1.5  × $\times$  20  mm 3 ${\rm {mm}}^3$ lutetium yttrium oxyorthosillicate (LYSO) crystals, which are tapered down to 1.2  × $\times$  1.2  mm 2 ${\rm {mm}}^2$ at the crystal-readout interface. The LYSO array couples 4-to-1 to an 8  × $\times$  8 array of 3  × $\times$  3  mm 2 ${\rm {mm}}^2$ silicon photomultiplier (SiPM) pixels on the tapered end and to a segmented prismatoid light guide array on the opposite end. Performance of tapered and non-tapered Prism-PET detectors was experimentally characterized and evaluated by measuring flood histogram, energy resolution, energy-, and oTOF-based DOI resolutions, and coincidence timing resolution. Sensitivities of scanners using different Prism-PET detector designs were simulated using Geant4 application for tomographic emission (GATE). RESULTS: For the tapered (non-tapered) Prism-PET module, the measured full width at half maximum (FWHM) energy, timing, energy-based DOI, and oTOF-based DOI resolutions were 8.88 (11.18)%, 243 (286) ps, 2.35 (3.18) mm, and 5.42 (13.87) mm, respectively. The scanner sensitivities using non-tapered and tapered crystals, and 10 rings of detector modules, were simulated to be 30.9 and 29.5 kcps/MBq, respectively. CONCLUSIONS: The tapered Prism-PET module with minimized light leak enabled the first experimental report of oTOF-based depth encoding at the detector module level. It also enabled the utilization of thinner (i.e., 0.1 mm) inter-crystal spacing with barium sulfate as the reflector while also improving energy-based DOI and timing resolutions.

Resolving inter-crystal scatter in a light-sharing depth-encoding PET detector.

Objective.Inter-crystal scattering (ICS) in light-sharing positron emission tomography (PET) detectors leads to ambiguity in positioning the initial interaction, which significantly degrades the contrast, quantitative accuracy, and spatial resolution of the resulting image. Here, we attempt to resolve the positioning ambiguity of ICS in a light-sharing depth-encoding detector by exploiting the confined, deterministic light-sharing enabled by the segmented light guide unique to Prism-PET.Approach.We first considered a test case of ICS between two adjacent crystals using an analytical and a neural network approach. The analytical approach used a Bayesian estimation framework constructed from a scatter absorption model-the prior-and a detector response model-the likelihood. A simple neural network was generated for the same scenario, to provide mutual validation for the findings. Finally, we generalized the solution to three-dimensional event positioning that handles all events in the photopeak using a convolutional neural network with unique architecture that separately predicts the identity and depth-of-interaction (DOI) of the crystal containing the first interaction.Main results.The analytical Bayesian method generated an estimation error of 20.5 keV in energy and 3.1 mm in DOI. Further analysis showed that the detector response model was sufficiently robust to achieve adequate performance via maximum likelihood estimation (MLE), without prior information. We then found convergent results using a simple neural network. In the generalized solution using a convolutional neural network, we found crystal identification accuracy of 83% and DOI estimation error of 3.0 mm across all events. Applying this positioning algorithm to simulated data, we demonstrated significant improvements in image quality over the baseline, centroid-based positioning approach, attaining 38.9% improvement in intrinsic spatial resolution and enhanced clarity in hot spots of diameters 0.8 to 2.5 mm.Significance.The accuracy of our findings exceeds those of previous reports in the literature. The Prism-PET light guide, mediating confined and deterministic light-sharing, plays a key role in ICS recovery, as its mathematical embodiment-the detector response model-was the essential driver of accuracy in our results.

Interleaved signal multiplexing readout in depth encoding Prism-PET detectors.

BACKGROUND: Given the large number of readout pixels in clinical positron emission tomography (PET) scanners, signal multiplexing is an indispensable feature to reduce scanner complexity, power consumption, heat output, and cost. PURPOSE: In this paper, we introduce interleaved multiplexing (iMux) scheme that utilizes the characteristic light-sharing pattern of depth-encoding Prism-PET detector modules with single-ended readout. METHODS: In the iMux readout, four anodes from every other silicon photomultiplier (SiPM) pixels across rows and columns, which overlap with four distinct light guides, are connected to the same application-specific integrated circuit (ASIC) channel. The 4-to-1 coupled Prism-PET detector module was used which consisted of a 16 ×  16 array of 1.5 × 1.5 × 20 mm3 lutetium yttrium oxyorthosilicate (LYSO) scintillator crystals coupled to an 8 × 8 array with 3 ×  3 mm2 SiPM pixels. A deep learning-based demultiplexing model was investigated to recover the encoded energy signals. Two different experiments were performed with non-multiplexed and multiplexed readouts to evaluate the spatial, depth of interaction (DOI), and timing resolutions of our proposed iMux scheme. RESULTS: The measured flood histograms, using the decoded energy signals from our deep learning-based demultiplexing architecture, achieved perfect crystal identification of events with negligible decoding error. The average energy, DOI, and timing resolutions were 9.6 ± 1.5%, 2.9 ± 0.9 mm, and 266 ± 19 ps for non-multiplexed readout and 10.3 ± 1.6%, 2.8 ± 0.8 mm, and 311 ± 28 ps for multiplexed readout, respectively. CONCLUSIONS: Our proposed iMux scheme improves on the already cost-effective and high-resolution Prism-PET detector module and provides 16-to-1 crystal-to-readout multiplexing without appreciable performance degradation. Also, only four SiPM pixels are shorted together in the 8 ×  8 array to achieve 4-to-1 pixel-to-readout multiplexing, resulting in lower capacitance per multiplexed channel.

A conformal TOF-DOI Prism-PET prototype scanner for high-resolution quantitative neuroimaging.

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-mm3 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). CONCLUSIONS: Prism-PET will substantially strengthen the utility of quantitative PET in neurology for early diagnosis of neurodegenerative diseases, and in neuro-oncology for improved management of both primary and metastatic brain tumors.

Decision Tree-Based Demultiplexing for Prism-PET.

Signal multiplexing is necessary to reduce a large number of readout channels in positron emission tomography (PET) scanners to minimize cost and achieve lower power consumption. However, the conventional weighted average energy method cannot localize the multiplexed events and more sophisticated approaches are necessary for accurate demultiplexing. The purpose of this paper is to propose a non-parametric decision tree model for demultiplexing signals in prismatoid PET (Prism-PET) detector module that consisted of 16 × 16 lutetium yttrium oxyorthosilicate (LYSO) scintillation crystal array coupled to 8 × 8 silicon photomultiplier (SiPM) pixels with 64:16 multiplexed readout. A total of 64 regression trees were trained individually to demultiplex the encoded readouts for each SiPM pixel. The Center of Gravity (CoG) and Truncated Center of Gravity (TCoG) methods were utilized for crystal identification based on the demultiplexed pixels. The flood histogram, energy resolution, and depth-of-interaction (DOI) resolution were measured for comparison using with and without multiplexed readouts. In conclusion, our proposed decision tree model achieved accurate results for signal demultiplexing, and thus maintained the Prism-PET detector module's high spatial and DOI resolution performance while using our unique light-sharing-based multiplexed readout.

High-resolution and high-sensitivity PET for quantitative molecular imaging of the monoaminergic nuclei: A GATE simulation 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 to achieve quantitative molecular neuroimaging of small but important brain regions with PET clinically viable.

Sub-2 mm depth of interaction localization in PET detectors with prismatoid light guide arrays and single-ended readout using convolutional neural networks.

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  mm 2 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.