Dataset Information

Creating hemodynamic atlases of cardiac 4D flow MRI.

ABSTRACT:

Purpose

Hemodynamic atlases can add to the pathophysiological understanding of cardiac diseases. This study proposes a method to create hemodynamic atlases using 4D Flow magnetic resonance imaging (MRI). The method is demonstrated for kinetic energy (KE) and helicity density (H_d ).

Materials and methods

Thirteen healthy subjects underwent 4D Flow MRI at 3T. Phase-contrast magnetic resonance cardioangiographies (PC-MRCAs) and an average heart were created and segmented. The PC-MRCAs, KE, and H_d were nonrigidly registered to the average heart to create atlases. The method was compared with 1) rigid, 2) affine registration of the PC-MRCAs, and 3) affine registration of segmentations. The peak and mean KE and H_d before and after registration were calculated to evaluate interpolation error due to nonrigid registration.

Results

The segmentations deformed using nonrigid registration overlapped (median: 92.3%) more than rigid (23.1%, P < 0.001), and affine registration of PC-MRCAs (38.5%, P < 0.001) and affine registration of segmentations (61.5%, P < 0.001). The peak KE was 4.9 mJ using the proposed method and affine registration of segmentations (P?=?0.91), 3.5 mJ using rigid registration (P < 0.001), and 4.2 mJ using affine registration of the PC-MRCAs (P < 0.001). The mean KE was 1.1 mJ using the proposed method, 0.8 mJ using rigid registration (P < 0.001), 0.9 mJ using affine registration of the PC-MRCAs (P < 0.001), and 1.0 mJ using affine registration of segmentations (P?=?0.028). The interpolation error was 5.2?±?2.6% at mid-systole, 2.8?±?3.8% at early diastole for peak KE; 9.6?±?9.3% at mid-systole, 4.0?±?4.6% at early diastole, and 4.9?±?4.6% at late diastole for peak H_d . The mean KE and H_d were not affected by interpolation.

Conclusion

Hemodynamic atlases can be obtained with minimal user interaction using nonrigid registration of 4D Flow MRI.

Level of evidence

2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2017;46:1389-1399.

SUBMITTER: Cibis M

PROVIDER: S-EPMC5655727 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

Creating hemodynamic atlases of cardiac 4D flow MRI.

Cibis Merih M Bustamante Mariana M Eriksson Jonatan J Carlhäll Carl-Johan CJ Ebbers Tino T

Journal of magnetic resonance imaging : JMRI 20170313 5

<h4>Purpose</h4>Hemodynamic atlases can add to the pathophysiological understanding of cardiac diseases. This study proposes a method to create hemodynamic atlases using 4D Flow magnetic resonance imaging (MRI). The method is demonstrated for kinetic energy (KE) and helicity density (H<sub>d</sub> ).<h4>Materials and methods</h4>Thirteen healthy subjects underwent 4D Flow MRI at 3T. Phase-contrast magnetic resonance cardioangiographies (PC-MRCAs) and an average heart were created and segmented. ...[more]

PMID: 28295788

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Project description:PurposeTo generate fully automated and fast 4D-flow MRI-based 3D segmentations of the aorta using deep learning for reproducible quantification of aortic flow, peak velocity, and dimensions.MethodsA total of 1018 subjects with aortic 4D-flow MRI (528 with bicuspid aortic valve, 376 with tricuspid aortic valve and aortic dilation, 114 healthy controls) comprised the data set. A convolutional neural network was trained to generate 3D aortic segmentations from 4D-flow data. Manual segmentations served as the ground truth (N = 499 training, N = 101 validation, N = 418 testing). Dice scores, Hausdorff distance, and average symmetrical surface distance were calculated to assess performance. Aortic flow, peak velocity, and lumen dimensions were quantified at the ascending, arch, and descending aorta and compared using Bland-Altman analysis. Interobserver variability of manual analysis was assessed on a subset of 40.ResultsConvolutional neural network segmentation required 0.438 ± 0.355 seconds versus 630 ± 254 seconds for manual analysis and demonstrated excellent performance with a median Dice score of 0.951 (0.930-0.966), Hausdorff distance of 2.80 (2.13-4.35), and average symmetrical surface distance of 0.176 (0.119-0.290). Excellent agreement was found for flow, peak velocity, and dimensions with low bias and limits of agreement less than 10% difference versus manual analysis. For aortic volume, limits of agreement were moderate within 16.3%. Interobserver variability (median Dice score: 0.950; Hausdorff distance: 2.45; and average symmetrical surface distance: 0.145) and convolutional neural network-based analysis (median Dice score: 0.953-0.959; Hausdorff distance: 2.24-2.91; and average symmetrical surface distance: 0.145-1.98 to observers) demonstrated similar reproducibility.ConclusionsDeep learning enabled fast and automated 3D aortic segmentation from 4D-flow MRI, demonstrating its potential for efficient clinical workflows. Future studies should investigate its utility for other vasculature and multivendor applications.

Dataset Information

Creating hemodynamic atlases of cardiac 4D flow MRI.

Purpose

Materials and methods

Results

Conclusion

Level of evidence

Publications

Creating hemodynamic atlases of cardiac 4D flow MRI.

Similar Datasets

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