ArticlePhysics in medicine and biology2018
Using convolutional neural networks to estimate time-of-flight from PET detector waveforms.
Article in Physics in medicine and biology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
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Who cites it
34 citing papers in PubMed.
- Artificial Intelligence Across the PET Reconstruction Pipeline: An Update.AJR. American journal of roentgenology · 2026Review
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Multi-Input Interaction Positioning and Signal Demultiplexing With Deep Learning in Semi-Monolithic Detectors.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Current Status of Cherenkov-Based Gamma Detectors for TOF-PET and Proton Range Verification.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- Energy estimation methods for positron emission tomography detectors composed of multiple scintillators.Biomedical engineering letters · 2025Article
- ASICs in PET: what we have and what we need.EJNMMI physics · 2025Review
- Improving timing resolution of BGO for TOF-PET: a comparative analysis with and without deep learning.EJNMMI physics · 2025Article
- Cross-Detection for Bubble-free Thermometer Codes and Dual-Side Monitoring for FPGA-Based High-Accuracy and High-Precision Time-to-Digital Converters.IEEE transactions on instrumentation and measurement · 2025Article
- Strategies for mitigating inter-crystal scattering effects in positron emission tomography: a comprehensive review.Biomedical engineering letters · 2024Review
- ImprovingJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Accurate Automated Quantification of Dopamine Transporter PET Without MRI Using Deep Learning-based Spatial Normalization.Nuclear medicine and molecular imaging · 2024Article
- Sequential deep learning image enhancement models improve diagnostic confidence, lesion detectability, and image reconstruction time in PET.EJNMMI physics · 2024Article
- Deep learning-based PET image denoising and reconstruction: a review.Radiological physics and technology · 2024Review
- Empowering PET: harnessing deep learning for improved clinical insight.European radiology experimental · 2024Review
- The quest for multifunctional and dedicated PET instrumentation with irregular geometries.Annals of nuclear medicine · 2024Review
- 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
- Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.European journal of nuclear medicine and molecular imaging · 2022Article
- Artificial intelligence-based PET denoising could allow a two-fold reduction in [European journal of nuclear medicine and molecular imaging · 2022Article
- Review
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2 authors.
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Abstract
Although there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time-of-flight information from the detector signals. In most cases, the timing pick-off for each waveform is computed using leading edge discrimination or constant fraction discrimination, as these were historically easily implemented with analog pulse processing electronics. However, now with the availability of fast waveform digitizers, there is opportunity to make use of more of the timing information contained in the coincident detector waveforms with advanced signal processing techniques. Here we describe the application of deep convolutional neural networks (CNNs), a type of machine learning, to estimate time-of-flight directly from the pair of digitized detector waveforms for a coincident event. One of the key features of this approach is the simplicity in obtaining ground-truth-labeled data needed to train the CNN: the true time-of-flight is determined from the difference in path length between the positron emission and each of the coincident detectors, which can be easily controlled experimentally. The experimental setup used here made use of two photomultiplier tube-based scintillation detectors, and a point source, stepped in 5 mm increments over a 15 cm range between the two detectors. The detector waveforms were digitized at 10 GS s
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