ArticleIEEE transactions on radiation and plasma medical sciences2020
TOF-PET Image Reconstruction With Multiple Timing Kernels Applied on Cherenkov Radiation in BGO.
Article in IEEE transactions on radiation and plasma medical sciences, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Time-of-flight: The last frontier in positron emission tomography instrumentation.Zeitschrift fur medizinische Physik · 2026Review
- Exploring spatiotemporal information in a Cherenkov and scintillation photon counting BGO TOF-PET semi-monolithic detector concept.Physics in medicine and biology · 2026Article
- Current Status of Cherenkov-Based Gamma Detectors for TOF-PET and Proton Range Verification.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Optical crosstalk between SiPMs in dual-ended readout.Sensors and actuators. A, Physical · 2025Article
- Improving CTR with the FastIC ASIC for TOF-PET by overcoming SiPM noise with baseline correction.IEEE transactions on radiation and plasma medical sciences · 2025Article
- High-performance dual-ended SiPM readout for TOF-PET with BGO and LYSO:Ce.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Enhancing the Cherenkov over scintillation ratio using dichroic filters in BGO and TlCl for TOF-PET.Scientific reports · 2025Article
- Realistic total-body J-PET geometry optimization: Monte Carlo study.Medical physics · 2025Article
- Timing Performance with Broadcom Metal Trench Silicon Photomultipliers.IEEE transactions on radiation and plasma medical sciences · 2025Article
- TlCl:Be,I: a high sensitivity scintillation and Cherenkov radiator for TOF-PET.IEEE transactions on radiation and plasma medical sciences · 2025Article
- Segmented SiPM Readout for Cherenkov Time-of-Flight Positron Emission Tomography Detectors Based on Bismuth Germanate.ACS photonics · 2025Article
- Improving timing resolution of BGO for TOF-PET: a comparative analysis with and without deep learning.EJNMMI physics · 2025Article
- Recent advances in radionuclide medical imaging techniques.Frontiers in medicine · 2025Review
- Article
- Timing limits of ultrafast cross-luminescence emission in CsZnCl-based crystals for TOF-CT and TOF-PET.EJNMMI physics · 2024Article
- Review
- The SNR of time-of-flight positron emission tomography data for joint reconstruction of the activity and attenuation images.Physics in medicine and biology · 2023Article
- 16-channel SiPM high-frequency readout with time-over-threshold discrimination for ultrafast time-of-flight applications.EJNMMI physics · 2023Article
- Timing Estimation and Limits in TOF-PET Detectors Producing Prompt Photons.IEEE transactions on radiation and plasma medical sciences · 2023Article
- Potential of Depth-of-Interaction-Based Detection Time Correction in Cherenkov Emitter Crystals for TOF-PET.IEEE transactions on radiation and plasma medical sciences · 2023Article
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7 authors.
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Abstract
Today Time-of-Flight (TOF), in PET scanners, assumes a single, well-defined timing resolution for all events. However, recent BGO-Cherenkov detectors, combining prompt Cherenkov emission and the typical BGO scintillation, can sort events into multiple timing kernels, best described by the Gaussian mixture models. The number of Cherenkov photons detected per event impacts directly the detector time resolution and signal rise time, which can later be used to improve the coincidence timing resolution. This work presents a simulation toolkit which applies multiple timing spreads on the coincident events and an image reconstruction that incorporates this information. A full cylindrical BGO-Cherenkov PET model was compared, in terms of contrast recovery and contrast-to-noise ratio, against an LYSO model with a time resolution of 213 ps. Two reconstruction approaches for the mixture kernels were tested: 1) mixture Gaussian and 2) decomposed simple Gaussian kernels. The decomposed model used the exact mixture component applied during the simulation. Images reconstructed using mixture kernels provided similar mean value and less noise than the decomposed. However, typically, more iterations were needed. Similarly, the LYSO model, with a single TOF kernel, converged faster than the BGO-Cherenkov with multiple kernels. The results indicate that the model complexity slows down convergence. However, due to the higher sensitivity, the contrast-to-noise ratio was 26.4% better for the BGO model.
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