ReviewEJNMMI physics2023
Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies.
Review in EJNMMI physics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
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Who cites it
12 citing papers in PubMed, 31 citations in OpenAlex.
- Comparative evaluation of PET quantitative methods for myocardial viability assessment in CAD patients and its prognostic value.The international journal of cardiovascular imaging · 2026Article
- Acquisition Time-Specific Deep Learning-Guided Image Quality Restoration in Accelerated I-123 DaTSCAN Brain SPECT on a Ring-Shaped CZT-Based Camera.Journal of imaging informatics in medicine · 2026Article
- Cross-Modality Deep Learning Denoising for Low-Dose μSPECT: Transfer of PET-Trained U‑Net and Diffusion Models.Chemical & biomedical imaging · 2026Article
- Approximate CT-free patient specific attenuation correction method for cardiac-dedicated pinhole CZT system: a feasibility phantom study.EJNMMI physics · 2026Article
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.PloS one · 2025Article
- Machine learning for prognostic prediction in coronary artery disease with SPECT data: a systematic review and meta-analysis.EJNMMI research · 2024Review
- Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Technological Advances in SPECT and SPECT/CT Imaging.Diagnostics (Basel, Switzerland) · 2024Review
- Gastric Emptying Scintigraphy Protocol Optimization Using Machine Learning for the Detection of Delayed Gastric Emptying.Diagnostics (Basel, Switzerland) · 2024Article
- Utility of myocardial blood flow assessment with dynamic CZT single photon emission computed tomography in patients with myocardial bridging: Is this 'wishful thinking' in this dynamic situation?Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Artificial intelligence in nuclear cardiology: your crucial role in transforming potential into reality.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Deep learning (DL) has a growing popularity and is a well-established method of artificial intelligence for data processing, especially for images and videos. Its applications in nuclear medicine are broad and include, among others, disease classification, image reconstruction, and image de-noising. Positron emission tomography (PET) and single-photon emission computerized tomography (SPECT) are major image acquisition technologies in nuclear medicine. Though several studies have been conducted to apply DL in many nuclear medicine domains, such as cancer detection and classification, few studies have employed such methods for cardiovascular disease applications. The present paper reviews recent DL approaches focused on cardiac SPECT imaging. Extensive research identified fifty-five related studies, which are discussed. The review distinguishes between major application domains, including cardiovascular disease diagnosis, SPECT attenuation correction, image denoising, full-count image estimation, and image reconstruction. In addition, major findings and dominant techniques employed for the mentioned task are revealed. Current limitations of DL approaches and future research directions are discussed.
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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.