ReviewPhysica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)2020
Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods.
Review in Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
- Differential performance of statistical versus machine learning methods in partial volume correction in oncologic F18-FDG PET/CT scanning.Annals of nuclear medicine · 2026Article
- CT-to-PET Synthesis in the Head-Neck and Thoracic Region via Conditional 3D Latent Diffusion Modeling.Bioengineering (Basel, Switzerland) · 2026Article
- Clinical evaluation of deep learning-based CT-free PET reconstruction image: a dual-center study.European journal of nuclear medicine and molecular imaging · 2026Article
- Using multimodal PET+MR data as conditional generative adversarial network inputs improves pseudo-CT and attenuation correction estimates for brain PET/MR.American journal of nuclear medicine and molecular imaging · 2026Article
- Artificial Intelligence in the Diagnosis and Treatment of Brain Gliomas.Biomedicines · 2025Review
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- Sex-Based Bias in Artificial Intelligence-Based Segmentation Models in Clinical Oncology.Clinical oncology (Royal College of Radiologists (Great Britain)) · 2025Review
- PE-CycleGAN network based CBCT-sCT generation for nasopharyngeal carsinoma adaptive radiotherapy.Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- Article
- Enhancement and evaluation for deep learning-based classification of volumetric neuroimaging with 3D-to-2D knowledge distillation.Scientific reports · 2024Article
- Learning CT-free attenuation-corrected total-body PET images through deep learning.European radiology · 2024Article
- Full-dose whole-body PET synthesis from low-dose PET using high-efficiency denoising diffusion probabilistic model: PET consistency model.Medical physics · 2024Article
- A Review of Machine Learning Approaches for Brain Positron Emission Tomography Data Analysis.Nuclear medicine and molecular imaging · 2024Review
- Deep learning-based whole-body PSMA PET/CT attenuation correction utilizing Pix-2-Pix GAN.Oncotarget · 2024Article
- Sequential deep learning image enhancement models improve diagnostic confidence, lesion detectability, and image reconstruction time in PET.EJNMMI physics · 2024Article
- Machine Learning Meets Cancer.Cancers · 2024Review
- Empowering PET: harnessing deep learning for improved clinical insight.European radiology experimental · 2024Review
- Generating PET Attenuation Maps via Sim2Real Deep Learning-Based Tissue Composition Estimation Combined with MLACF.Journal of imaging informatics in medicine · 2024Article
- Artificial Intelligence Analysis Using MRI and PET Imaging in Gliomas: A Narrative Review.Cancers · 2024Review
- Modular GAN: positron emission tomography image reconstruction using two generative adversarial networks.Frontiers in radiology · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
The rapid expansion of machine learning is offering a new wave of opportunities for nuclear medicine. This paper reviews applications of machine learning for the study of attenuation correction (AC) and low-count image reconstruction in quantitative positron emission tomography (PET). Specifically, we present the developments of machine learning methodology, ranging from random forest and dictionary learning to the latest convolutional neural network-based architectures. For application in PET attenuation correction, two general strategies are reviewed: 1) generating synthetic CT from MR or non-AC PET for the purposes of PET AC, and 2) direct conversion from non-AC PET to AC PET. For low-count PET reconstruction, recent deep learning-based studies and the potential advantages over conventional machine learning-based methods are presented and discussed. In each application, the proposed methods, study designs and performance of published studies are listed and compared with a brief discussion. Finally, the overall contributions and remaining challenges are summarized.
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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.