Free-breathing single-beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices: A reproducibility study
Background: Exercise cardiovascular magnetic resonance (Ex-CMR) can reveal pathophysiologies not evident at rest by quantifying biventricular volume and function during or immediately after exercise. However, achieving reproducible Ex-CMR measurements is challenging due to limited spatial and tempor...
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Elsevier
2025-01-01
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| Series: | Journal of Cardiovascular Magnetic Resonance |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1097664725000638 |
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| author | Fahime Ghanbari Alexander Schulz Manuel A. Morales Jennifer Rodriguez Jordan A. Street Kathryn Arcand Scott Johnson Patrick Pierce Christopher W. Hoeger Connie W. Tsao Warren J. Manning Reza Nezafat |
| author_facet | Fahime Ghanbari Alexander Schulz Manuel A. Morales Jennifer Rodriguez Jordan A. Street Kathryn Arcand Scott Johnson Patrick Pierce Christopher W. Hoeger Connie W. Tsao Warren J. Manning Reza Nezafat |
| author_sort | Fahime Ghanbari |
| collection | DOAJ |
| description | Background: Exercise cardiovascular magnetic resonance (Ex-CMR) can reveal pathophysiologies not evident at rest by quantifying biventricular volume and function during or immediately after exercise. However, achieving reproducible Ex-CMR measurements is challenging due to limited spatial and temporal resolution. This study aimed to develop and evaluate a free-breathing, high-spatiotemporal-resolution single-beat Ex-CMR cine enhanced by generative artificial intelligence. We assessed image analysis reproducibility, scan-rescan reproducibility, and impact of the reader's experience on the analysis. Methods: Imaging was performed on a 3T CMR system using a free-breathing, highly accelerated, multi-slice, single-beat cine sequence (in-plane spatiotemporal resolution of 1.9 × 1.9 mm² and 37 ms, respectively). High acceleration was achieved by combining compressed sensing reconstruction with a resolution-enhancement generative adversarial inline neural network. Ex-CMR was performed using a supine ergometer positioned immediately outside the magnet bore. Single-beat cine images were acquired at rest and immediately post-exercise. In a prospective study, the protocol was evaluated in 141 subjects. A structured image analysis workflow was implemented. Four expert readers, with or without prior training in single-beat Ex-CMR, independently rated all images for diagnostic and image quality. The subjective assessment used two 3-point Likert scales. Biventricular parameters were calculated. Inter- and intra-observer reproducibility were assessed. Fifteen healthy subjects were re-imaged 1 year later for scan-rescan reproducibility. Reproducibility was assessed using intraclass correlation coefficient (ICC), with agreement evaluated via Bland-Altman analysis, linear regression, and Pearson correlation. Results: Free-breathing, single-beat Ex-CMR cine enabled imaging of the beating heart within 30 ± 6 s, with technically successful scans in 96% (136/141) of subjects. Post-exercise single-beat cine images were assessed as diagnostic in 98% (133/136), 96% (131/136), 82% (112/136), and 65% (89/136) of cases by four readers (ordered by descending years of Ex-CMR experience). Good image quality was reported in 74% (100/136) to 80% (109/136) of subjects. Biventricular parameters were successfully measured in all subjects, demonstrating good to excellent inter-observer reproducibility. Scan/rescan reproducibility over 1 year, assessed by two independent readers, showed excellent inter-visit ICCs (0.96–1.0) and strong correlations (R² ≥ 0.92, p < 0.001 for left ventricle; R² ≥ 0.95, p < 0.001 for right ventricle). Conclusion: Single-beat Ex-CMR enabled evaluation of biventricular volumetric and functional indices with excellent reproducibility. |
| format | Article |
| id | doaj-art-f00a4d1b0ec845cfab063cd08e54d53f |
| institution | OA Journals |
| issn | 1097-6647 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
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| series | Journal of Cardiovascular Magnetic Resonance |
| spelling | doaj-art-f00a4d1b0ec845cfab063cd08e54d53f2025-08-20T02:08:57ZengElsevierJournal of Cardiovascular Magnetic Resonance1097-66472025-01-0127110190110.1016/j.jocmr.2025.101901Free-breathing single-beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices: A reproducibility studyFahime Ghanbari0Alexander Schulz1Manuel A. Morales2Jennifer Rodriguez3Jordan A. Street4Kathryn Arcand5Scott Johnson6Patrick Pierce7Christopher W. Hoeger8Connie W. Tsao9Warren J. Manning10Reza Nezafat11Department of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA; Department of Radiology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USADepartment of Medicine (Cardiovascular Division), Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA; Corresponding author.Background: Exercise cardiovascular magnetic resonance (Ex-CMR) can reveal pathophysiologies not evident at rest by quantifying biventricular volume and function during or immediately after exercise. However, achieving reproducible Ex-CMR measurements is challenging due to limited spatial and temporal resolution. This study aimed to develop and evaluate a free-breathing, high-spatiotemporal-resolution single-beat Ex-CMR cine enhanced by generative artificial intelligence. We assessed image analysis reproducibility, scan-rescan reproducibility, and impact of the reader's experience on the analysis. Methods: Imaging was performed on a 3T CMR system using a free-breathing, highly accelerated, multi-slice, single-beat cine sequence (in-plane spatiotemporal resolution of 1.9 × 1.9 mm² and 37 ms, respectively). High acceleration was achieved by combining compressed sensing reconstruction with a resolution-enhancement generative adversarial inline neural network. Ex-CMR was performed using a supine ergometer positioned immediately outside the magnet bore. Single-beat cine images were acquired at rest and immediately post-exercise. In a prospective study, the protocol was evaluated in 141 subjects. A structured image analysis workflow was implemented. Four expert readers, with or without prior training in single-beat Ex-CMR, independently rated all images for diagnostic and image quality. The subjective assessment used two 3-point Likert scales. Biventricular parameters were calculated. Inter- and intra-observer reproducibility were assessed. Fifteen healthy subjects were re-imaged 1 year later for scan-rescan reproducibility. Reproducibility was assessed using intraclass correlation coefficient (ICC), with agreement evaluated via Bland-Altman analysis, linear regression, and Pearson correlation. Results: Free-breathing, single-beat Ex-CMR cine enabled imaging of the beating heart within 30 ± 6 s, with technically successful scans in 96% (136/141) of subjects. Post-exercise single-beat cine images were assessed as diagnostic in 98% (133/136), 96% (131/136), 82% (112/136), and 65% (89/136) of cases by four readers (ordered by descending years of Ex-CMR experience). Good image quality was reported in 74% (100/136) to 80% (109/136) of subjects. Biventricular parameters were successfully measured in all subjects, demonstrating good to excellent inter-observer reproducibility. Scan/rescan reproducibility over 1 year, assessed by two independent readers, showed excellent inter-visit ICCs (0.96–1.0) and strong correlations (R² ≥ 0.92, p < 0.001 for left ventricle; R² ≥ 0.95, p < 0.001 for right ventricle). Conclusion: Single-beat Ex-CMR enabled evaluation of biventricular volumetric and functional indices with excellent reproducibility.http://www.sciencedirect.com/science/article/pii/S1097664725000638Exercise-CMRFree-breathing single-beat cineBiventricular volumetric and functional indices |
| spellingShingle | Fahime Ghanbari Alexander Schulz Manuel A. Morales Jennifer Rodriguez Jordan A. Street Kathryn Arcand Scott Johnson Patrick Pierce Christopher W. Hoeger Connie W. Tsao Warren J. Manning Reza Nezafat Free-breathing single-beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices: A reproducibility study Journal of Cardiovascular Magnetic Resonance Exercise-CMR Free-breathing single-beat cine Biventricular volumetric and functional indices |
| title | Free-breathing single-beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices: A reproducibility study |
| title_full | Free-breathing single-beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices: A reproducibility study |
| title_fullStr | Free-breathing single-beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices: A reproducibility study |
| title_full_unstemmed | Free-breathing single-beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices: A reproducibility study |
| title_short | Free-breathing single-beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices: A reproducibility study |
| title_sort | free breathing single beat exercise cardiovascular magnetic resonance with generative artificial intelligence for evaluation of volumetric and functional cardiac indices a reproducibility study |
| topic | Exercise-CMR Free-breathing single-beat cine Biventricular volumetric and functional indices |
| url | http://www.sciencedirect.com/science/article/pii/S1097664725000638 |
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