ArticleMedical physics2021
Sub-2 mm depth of interaction localization in PET detectors with prismatoid light guide arrays and single-ended readout using convolutional neural networks.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Inter-crystal scatter recovery of a light-sharing depth-encoding Prism-PET prototype scanner using a diffusion model.Medical physics · 2026Article
- Potential and challenges of Positron Emission Tomography beyond conventional preclinical and clinical imaging.Zeitschrift fur medizinische Physik · 2026Review
- Performance of detector modules designed for the NeuroSphere brain PET system.Medical physics · 2026Article
- ASICs in PET: what we have and what we need.EJNMMI physics · 2025Review
- PET detectors with depth-of-interaction and time-of-flight capabilities.Radiological physics and technology · 2024Review
- Depth-encoding using optical photon TOF in a prism-PET detector with tapered crystals.Medical physics · 2024Article
- Resolving inter-crystal scatter in a light-sharing depth-encoding PET detector.Physics in medicine and biology · 2024Article
- A conformal TOF-DOI Prism-PET prototype scanner for high-resolution quantitative neuroimaging.Medical physics · 2023Article
- Timing, Energy, and 3-D Spatial Resolution of the BING PET Detector Module.IEEE transactions on radiation and plasma medical sciences · 2023Article
- High-resolution and high-sensitivity PET for quantitative molecular imaging of the monoaminergic nuclei: A GATE simulation study.Medical physics · 2022Article
- High resolution detectors for whole-body PET scanners by using dual-ended readout.EJNMMI physics · 2022Article
- Deep residual-convolutional neural networks for event positioning in a monolithic annular PET scanner.Physics in medicine and biology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
purposeDepth 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.
methodsDepth 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
resultsAn 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.
conclusionsOur 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.
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