Automated analysis of cardiovascular magnetic resonance myocardial native T₁ mapping images using fully convolutional neural networks

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.