ArticleScientific reports2019
Classification of Polar Maps from Cardiac Perfusion Imaging with Graph-Convolutional Neural Networks.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Variational autoencoders can detect coronary artery disease in myocardial perfusion SPECT images.European journal of nuclear medicine and molecular imaging · 2026Article
- A Novel Quantitative Analysis in Myocardial Perfusion Imaging: How does it compare to the "Golden Eye"?Zeitschrift fur medizinische Physik · 2026Article
- A Prototype-Guided 3D Deep Learning Framework for Myocardial Perfusion Scintigraphy Segmentation.Journal of clinical medicine · 2026Review
- Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: a comprehensive deep learning study.European journal of nuclear medicine and molecular imaging · 2025Article
- Artificial Intelligence-Enhanced Perfusion Scoring Improves the Diagnostic Accuracy of Myocardial Perfusion Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2025Article
- Between Two Worlds: Investigating the Intersection of Human Expertise and Machine Learning in the Case of Coronary Artery Disease Diagnosis.Bioengineering (Basel, Switzerland) · 2024Article
- Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [Scientific reports · 2024Article
- SPECT-MPI for Coronary Artery Disease: A Deep Learning Approach.Acta medica Philippina · 2024Article
- Direct Risk Assessment From Myocardial Perfusion Imaging Using Explainable Deep Learning.JACC. Cardiovascular imaging · 2023Article
- Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies.EJNMMI physics · 2023Review
- Explainable Deep Learning Improves Physician Interpretation of Myocardial Perfusion Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2022Article
- Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input.Journal of personalized medicine · 2022Article
- Deep Learning-Based Automated Diagnosis for Coronary Artery Disease Using SPECT-MPI Images.Journal of clinical medicine · 2022Article
- Classification of ischemia from myocardial polar maps inScientific reports · 2022Article
- An image processing tool for the detection of anthracycline-induced cardiotoxicity by evaluating the myocardial metabolic activity in [International journal of computer assisted radiology and surgery · 2022Article
- Interaction data are identifiable even across long periods of time.Nature communications · 2022Article
- Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis.Informatics in medicine unlocked · 2022Article
- Quantitative clinical nuclear cardiology, part 2: Evolving/emerging applications.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2021Review
- Proposed Requirements for Cardiovascular Imaging-Related Machine Learning Evaluation (PRIME): A Checklist: Reviewed by the American College of Cardiology Healthcare Innovation Council.JACC. Cardiovascular imaging · 2020Review
- Clinical quantitative cardiac imaging for the assessment of myocardial ischaemia.Nature reviews. Cardiology · 2020Article
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6 authors.
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Abstract
Myocardial perfusion imaging is a non-invasive imaging technique commonly used for the diagnosis of Coronary Artery Disease and is based on the injection of radiopharmaceutical tracers into the blood stream. The patient's heart is imaged while at rest and under stress in order to determine its capacity to react to the imposed challenge. Assessment of imaging data is commonly performed by visual inspection of polar maps showing the tracer uptake in a compact, two-dimensional representation of the left ventricle. This article presents a method for automatic classification of polar maps based on graph convolutional neural networks. Furthermore, it evaluates how well localization techniques developed for standard convolutional neural networks can be used for the localization of pathological segments with respect to clinically relevant areas. The method is evaluated using 946 labeled datasets and compared quantitatively to three other neural-network-based methods. The proposed model achieves an agreement with the human observer on 89.3% of rest test polar maps and on 91.1% of stress test polar maps. Localization performed on a fine 17-segment division of the polar maps achieves an agreement of 83.1% with the human observer, while localization on a coarse 3-segment division based on the vessel beds of the left ventricle has an agreement of 78.8% with the human observer. Our method could thus assist the decision-making process of physicians when analyzing polar map data obtained from myocardial perfusion images.
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