Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks
Abstract Background Cardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-01-01
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| Series: | Journal of Cardiovascular Magnetic Resonance |
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| Online Access: | http://link.springer.com/article/10.1186/s12968-018-0516-1 |
| _version_ | 1797203635488161792 |
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| author | Ahmed S. Fahmy Hossam El-Rewaidy Maryam Nezafat Shiro Nakamori Reza Nezafat |
| author_facet | Ahmed S. Fahmy Hossam El-Rewaidy Maryam Nezafat Shiro Nakamori Reza Nezafat |
| author_sort | Ahmed S. Fahmy |
| collection | DOAJ |
| description | Abstract Background Cardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility. In this study, we propose an automated method for combined myocardium segmentation, alignment, and T1 calculation for myocardial T1 mapping. Methods A deep fully convolutional neural network (FCN) was used for myocardium segmentation in T1 weighted images. The segmented myocardium was then resampled on a polar grid, whose origin is located at the center-of-mass of the segmented myocardium. Myocardium T1 maps were reconstructed from the resampled T1 weighted images using curve fitting. The FCN was trained and tested using manually segmented images for 210 patients (5 slices, 11 inversion times per patient). An additional image dataset for 455 patients (5 slices and 11 inversion times per patient), analyzed by an expert reader using a semi-automatic tool, was used to validate the automatically calculated global and regional T1 values. Bland-Altman analysis, Pearson correlation coefficient, r, and the Dice similarity coefficient (DSC) were used to evaluate the performance of the FCN-based analysis on per-patient and per-slice basis. Inter-observer variability was assessed using intraclass correlation coefficient (ICC) of the T1 values calculated by the FCN-based automatic method and two readers. Results The FCN achieved fast segmentation (< 0.3 s/image) with high DSC (0.85 ± 0.07). The automatically and manually calculated T1 values (1091 ± 59 ms and 1089 ± 59 ms, respectively) were highly correlated in per-patient (r = 0.82; slope = 1.01; p < 0.0001) and per-slice (r = 0.72; slope = 1.01; p < 0.0001) analyses. Bland-Altman analysis showed good agreement between the automated and manual measurements with 95% of measurements within the limits-of-agreement in both per-patient and per-slice analyses. The intraclass correllation of the T1 calculations by the automatic method vs reader 1 and reader 2 was respectively 0.86/0.56 and 0.74/0.49 in the per-patient/per-slice analyses, which were comparable to that between two expert readers (=0.72/0.58 in per-patient/per-slice analyses). Conclusion The proposed FCN-based image processing platform allows fast and automatic analysis of myocardial native T1 mapping images mitigating the burden and observer-related variability of manual analysis. |
| first_indexed | 2024-04-24T08:22:28Z |
| format | Article |
| id | doaj.art-54213698d1c24df79e68975ede727c10 |
| institution | Directory Open Access Journal |
| issn | 1532-429X |
| language | English |
| last_indexed | 2024-04-24T08:22:28Z |
| publishDate | 2019-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Cardiovascular Magnetic Resonance |
| spelling | doaj.art-54213698d1c24df79e68975ede727c102024-04-17T00:03:01ZengElsevierJournal of Cardiovascular Magnetic Resonance1532-429X2019-01-0121111210.1186/s12968-018-0516-1Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networksAhmed S. Fahmy0Hossam El-Rewaidy1Maryam Nezafat2Shiro Nakamori3Reza Nezafat4Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical SchoolDepartment of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical SchoolDepartment of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical SchoolDepartment of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical SchoolDepartment of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical SchoolAbstract Background Cardiovascular magnetic resonance (CMR) myocardial native T1 mapping allows assessment of interstitial diffuse fibrosis. In this technique, the global and regional T1 are measured manually by drawing region of interest in motion-corrected T1 maps. The manual analysis contributes to an already lengthy CMR analysis workflow and impacts measurements reproducibility. In this study, we propose an automated method for combined myocardium segmentation, alignment, and T1 calculation for myocardial T1 mapping. Methods A deep fully convolutional neural network (FCN) was used for myocardium segmentation in T1 weighted images. The segmented myocardium was then resampled on a polar grid, whose origin is located at the center-of-mass of the segmented myocardium. Myocardium T1 maps were reconstructed from the resampled T1 weighted images using curve fitting. The FCN was trained and tested using manually segmented images for 210 patients (5 slices, 11 inversion times per patient). An additional image dataset for 455 patients (5 slices and 11 inversion times per patient), analyzed by an expert reader using a semi-automatic tool, was used to validate the automatically calculated global and regional T1 values. Bland-Altman analysis, Pearson correlation coefficient, r, and the Dice similarity coefficient (DSC) were used to evaluate the performance of the FCN-based analysis on per-patient and per-slice basis. Inter-observer variability was assessed using intraclass correlation coefficient (ICC) of the T1 values calculated by the FCN-based automatic method and two readers. Results The FCN achieved fast segmentation (< 0.3 s/image) with high DSC (0.85 ± 0.07). The automatically and manually calculated T1 values (1091 ± 59 ms and 1089 ± 59 ms, respectively) were highly correlated in per-patient (r = 0.82; slope = 1.01; p < 0.0001) and per-slice (r = 0.72; slope = 1.01; p < 0.0001) analyses. Bland-Altman analysis showed good agreement between the automated and manual measurements with 95% of measurements within the limits-of-agreement in both per-patient and per-slice analyses. The intraclass correllation of the T1 calculations by the automatic method vs reader 1 and reader 2 was respectively 0.86/0.56 and 0.74/0.49 in the per-patient/per-slice analyses, which were comparable to that between two expert readers (=0.72/0.58 in per-patient/per-slice analyses). Conclusion The proposed FCN-based image processing platform allows fast and automatic analysis of myocardial native T1 mapping images mitigating the burden and observer-related variability of manual analysis.http://link.springer.com/article/10.1186/s12968-018-0516-1T1 mappingConvolutional neural networksAutomatic analysisMyocardium segmentation |
| spellingShingle | Ahmed S. Fahmy Hossam El-Rewaidy Maryam Nezafat Shiro Nakamori Reza Nezafat Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks Journal of Cardiovascular Magnetic Resonance T1 mapping Convolutional neural networks Automatic analysis Myocardium segmentation |
| title | Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks |
| title_full | Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks |
| title_fullStr | Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks |
| title_full_unstemmed | Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks |
| title_short | Automated analysis of cardiovascular magnetic resonance myocardial native T1 mapping images using fully convolutional neural networks |
| title_sort | automated analysis of cardiovascular magnetic resonance myocardial native t1 mapping images using fully convolutional neural networks |
| topic | T1 mapping Convolutional neural networks Automatic analysis Myocardium segmentation |
| url | http://link.springer.com/article/10.1186/s12968-018-0516-1 |
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