ArticleJournal of magnetic resonance imaging : JMRI2020
Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI.
Article in Journal of magnetic resonance imaging : JMRI, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 2 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Prognostic Value of Stress Perfusion Cardiac MRI in Cardiovascular Disease: A Systematic Review and Meta-Analysis of the Effects of the Scanner, Stress Agent, and Analysis Technique.Radiology. Cardiothoracic imaging · 2024Pooled it
- Society for cardiovascular magnetic resonance expert consensus statement on quantitative myocardial perfusion cardiovascular magnetic resonance imaging.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceGuideline
- A two-stage preprocessing and classification approach for accurate COVID-19 detection in X-ray images.Scientific reports · 2026Article
- Artificial Intelligence-Based Arterial Input Function for the Quantitative Assessment of Myocardial Blood Flow and Perfusion Reserve in Cardiac Magnetic Resonance: A Validation Study.Diagnostics (Basel, Switzerland) · 2025Article
- AI Applied to Cardiac Magnetic Resonance for Precision Medicine in Coronary Artery Disease: A Systematic Review.Journal of cardiovascular development and disease · 2025Review
- Toward Informative Representations of Blood-Based Infrared Spectra via Unsupervised Deep Learning.Journal of biophotonics · 2025Article
- Deep learning-based segmentation of left ventricular myocardium on dynamic contrast-enhanced MRI: a comprehensive evaluation across temporal frames.International journal of computer assisted radiology and surgery · 2024Article
- Automated cardiovascular MR myocardial scar quantification with unsupervised domain adaptation.European radiology experimental · 2024Article
- Article
- Deep learning for accelerated and robust MRI reconstruction.Magma (New York, N.Y.) · 2024Review
- Improved robustness for deep learning-based segmentation of multi-center myocardial perfusion cardiovascular MRI datasets using data-adaptive uncertainty-guided space-time analysis.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2024Article
- High-resolution quantification of stress perfusion defects by cardiac magnetic resonance.European heart journal. Imaging methods and practice · 2024Article
- Automatic calculation of myocardial perfusion reserve using deep learning with uncertainty quantification.Quantitative imaging in medicine and surgery · 2023Article
- Temporal Uncertainty Localization to Enable Human-in-the-Loop Analysis of Dynamic Contrast-Enhanced Cardiac MRI Datasets.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Role of pulmonary perfusion magnetic resonance imaging for the diagnosis of pulmonary hypertension: A review.World journal of radiology · 2023Review
- Stress Cardiovascular Magnetic Resonance Imaging for the Detection of Coronary Artery Disease.Reviews in cardiovascular medicine · 2023Review
- AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance.European heart journal. Digital health · 2023Article
- Cardiovascular Magnetic Resonance Parametric Mapping Techniques for the Assessment of Chronic Coronary Syndromes.Journal of cardiovascular development and disease · 2022Review
- Impact of Temporal Resolution and Methods for Correction on Cardiac Magnetic Resonance Perfusion Quantification.Journal of magnetic resonance imaging : JMRI · 2022Article
- Physics-informed neural networks for myocardial perfusion MRI quantification.Medical image analysis · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundQuantitative myocardial perfusion cardiac MRI can provide a fast and robust assessment of myocardial perfusion status for the noninvasive diagnosis of myocardial ischemia while being more objective than visual assessment. However, it currently has limited use in clinical practice due to the challenging postprocessing required, particularly the segmentation. PURPOSE: To evaluate the efficacy of an automated deep learning (DL) pipeline for image processing prior to quantitative analysis. STUDY TYPE: Retrospective. POPULATION: In all, 175 (350 MRI scans; 1050 image series) clinical patients under both rest and stress conditions (135/10/30 training/validation/test). FIELD STRENGTH/SEQUENCE: 3.0T/2D multislice saturation recovery T ASSESSMENT: Accuracy was assessed, as compared to the manual operator, through the mean square error of the distance between landmarks and the Dice similarity coefficient of the segmentation and bounding box detection. Quantitative perfusion maps obtained using the automated DL-based processing were compared to the results obtained with the manually processed images. STATISTICAL TESTS: Bland-Altman plots and intraclass correlation coefficient (ICC) were used to assess the myocardial blood flow (MBF) obtained using the automated DL pipeline, as compared to values obtained by a manual operator.
resultsThe mean (SD) error in the detection of the time of peak signal enhancement in the left ventricle was 1.49 (1.4) timeframes. The mean (SD) Dice similarity coefficients for the bounding box and myocardial segmentation were 0.93 (0.03) and 0.80 (0.06), respectively. The mean (SD) error in the RV insertion point was 2.8 (1.8) mm. The Bland-Altman plots showed a bias of 2.6% of the mean MBF between the automated and manually processed MBF values on a per-myocardial segment basis. The ICC was 0.89, 95% confidence interval = [0.87, 0.90]. DATA
conclusionWe showed high accuracy, compared to manual processing, for the DL-based processing of myocardial perfusion data leading to quantitative values that are similar to those achieved with manual processing. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:1689-1696.
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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.