ArticleProceedings of the IEEE. Institute of Electrical and Electronics Engineers2020
Machine Learning in PET: from Photon Detection to Quantitative Image Reconstruction.
Article in Proceedings of the IEEE. Institute of Electrical and Electronics Engineers, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
45 citing papers in PubMed.
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- Dynamic [Research square · 2026Article
- Dynamic positron emission tomography image reconstruction using spatiotemporal kernel method with deep image prior.Quantitative imaging in medicine and surgery · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- Comparison ofEJNMMI physics · 2025Article
- Improving timing resolution of BGO for TOF-PET: a comparative analysis with and without deep learning.EJNMMI physics · 2025Article
- Performance and application of the total-body PET/CT scanner: a literature review.EJNMMI research · 2024Review
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- DLSIA: Deep Learning for Scientific Image Analysis.Journal of applied crystallography · 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
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
- Artificial intelligence and bioinformatics: a journey from traditional techniques to smart approaches.Gastroenterology and hepatology from bed to bench · 2024Review
- Similarity-Driven Fine-Tuning Methods for Regularization Parameter Optimization in PET Image Reconstruction.Sensors (Basel, Switzerland) · 2023Article
- Joint estimation of interaction position and energy deposition in semiconductor SPECT imaging sensors using fully connected neural network.Physics in medicine and biology · 2023Article
- Neural KEM: A Kernel Method With Deep Coefficient Prior for PET Image Reconstruction.IEEE transactions on medical imaging · 2023Article
- Total-body PET: a new paradigm for molecular imaging.The British journal of radiology · 2022Review
- Deep Kernel Representation for Image Reconstruction in PET.IEEE transactions on medical imaging · 2022Article
- Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.European journal of nuclear medicine and molecular imaging · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Machine learning has found unique applications in nuclear medicine from photon detection to quantitative image reconstruction. While 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 and position information from the detector signals. Now with the availability of fast waveform digitizers, machine learning techniques have been applied to estimate the position and arrival time of high-energy photons. In quantitative image reconstruction, machine learning has been used to estimate various corrections factors, including scattered events and attenuation images, as well as to reduce statistical noise in reconstructed images. Here machine learning either provides a faster alternative to an existing time-consuming computation, such as in the case of scatter estimation, or creates a data-driven approach to map an implicitly defined function, such as in the case of estimating the attenuation map for PET/MR scans. In this article, we will review the abovementioned applications of machine learning in nuclear medicine.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.