ReviewQuantitative imaging in medicine and surgery2021
Applications of artificial intelligence in nuclear medicine image generation.
Review in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 1 of them a synthesis that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
27 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics and artificial intelligence in prostate cancer: new tools for molecular hybrid imaging and theragnostics.European radiology experimental · 2022Pooled it
- Emerging frontiers and challenges of artificial intelligence in PSMA-PET imaging: pioneering a new chapter in prostate cancer care.Japanese journal of radiology · 2026Review
- Review
- A study on the generation of traditional patterns in the perspective of AIGC-ancient Egyptian patterns as an example.Scientific reports · 2026Article
- Recent advances and current landscape of software tools for image analysis and dosimetry in nuclear medicine.EJNMMI physics · 2026Review
- Super-Resolution Reconstruction and Detector Geometric Error Correction for Parallel-Beam Low-Resolution Multi-Detector SPECT: A Proof of Concept.Tomography (Ann Arbor, Mich.) · 2026Article
- Artificial intelligence and breast cancer screening in Serbia: a dual-perspective qualitative study among radiologists and screening-aged women.Frontiers in radiology · 2026Article
- Review
- Exploring Curriculum Considerations to Prepare Future Radiographers for an AI-Assisted Health Care Environment: Protocol for Scoping Review.JMIR research protocols · 2025Article
- A review of state-of-the-art resolution improvement techniques in SPECT imaging.EJNMMI physics · 2025Review
- SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network.EJNMMI physics · 2024Article
- Review
- Radiation Detectors and Sensors in Medical Imaging.Sensors (Basel, Switzerland) · 2024Review
- Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying.Diagnostics (Basel, Switzerland) · 2024Article
- Verification of image quality improvement of low-count bone scintigraphy using deep learning.Radiological physics and technology · 2024Article
- Empowering PET: harnessing deep learning for improved clinical insight.European radiology experimental · 2024Review
- Activity quantification and dosimetry in radiopharmaceutical therapy with reference toFrontiers in nuclear medicine · 2024Review
- Observer studies of image quality of denoising reduced-count cardiac single photon emission computed tomography myocardial perfusion imaging by three-dimensional Gaussian post-reconstruction filtering and deep learning.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Prior information-guided reconstruction network for positron emission tomography images.Quantitative imaging in medicine and surgery · 2023Article
- The influence of arm positions on mechanical dyssynchrony measured by gated myocardial perfusion imaging.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, the application of artificial intelligence (AI) in medical imaging (including nuclear medicine imaging) has rapidly developed. Most AI applications in nuclear medicine imaging have focused on the diagnosis, treatment monitoring, and correlation analyses with pathology or specific gene mutation. It can also be used for image generation to shorten the time of image acquisition, reduce the dose of injected tracer, and enhance image quality. This work provides an overview of the application of AI in image generation for single-photon emission computed tomography (SPECT) and positron emission tomography (PET) either without or with anatomical information [CT or magnetic resonance imaging (MRI)]. This review focused on four aspects, including imaging physics, image reconstruction, image postprocessing, and internal dosimetry. AI application in generating attenuation map, estimating scatter events, boosting image quality, and predicting internal dose map is summarized and discussed.
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What OpenQuestion holds
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.