Abstract

Objective

T1 mapping provides valuable information regarding cardiomyopathies. Manual drawing is time consuming and prone to subjective errors. Therefore, this study aimed to test a DL algorithm for the automated measurement of native T1 and extracellular volume (ECV) fractions in cardiac magnetic resonance (CMR) imaging with a temporally separated dataset.

Materials and Methods

CMR images obtained for 95 participants (mean age ± standard deviation, 54.5 ± 15.2 years), including 36 left ventricular hypertrophy (12 hypertrophic cardiomyopathy, 12 Fabry disease, and 12 amyloidosis), 32 dilated cardiomyopathy, and 27 healthy volunteers, were included. A commercial deep learning (DL) algorithm based on 2D U-net (Myomics-T1 software, version 1.0.0) was used for the automated analysis of T1 maps. Four radiologists, as study readers, performed manual analysis. The reference standard was the consensus result of the manual analysis by two additional expert readers. The segmentation performance of the DL algorithm and the correlation and agreement between the automated measurement and the reference standard were assessed. Interobserver agreement among the four radiologists was analyzed.

Results

DL successfully segmented the myocardium in 99.3% of slices in the native T1 map and 89.8% of slices in the post-T1 map with Dice similarity coefficients of 0.86 ± 0.05 and 0.74 ± 0.17, respectively. Native T1 and ECV showed strong correlation and agreement between DL and the reference: for T1, r = 0.967 (95% confidence interval [CI], 0.951–0.978) and bias of 9.5 msec (95% limits of agreement [LOA], -23.6–42.6 msec); for ECV, r = 0.987 (95% CI, 0.980–0.991) and bias of 0.7% (95% LOA, -2.8%–4.2%) on per-subject basis. Agreements between DL and each of the four radiologists were excellent (intraclass correlation coefficient [ICC] of 0.98–0.99 for both native T1 and ECV), comparable to the pairwise agreement between the radiologists (ICC of 0.97–1.00 and 0.99–1.00 for native T1 and ECV, respectively).

Conclusion

The DL algorithm allowed automated T1 and ECV measurements comparable to those of radiologists.

Keywords

Magnetic resonance imaging; Heart; Deep learning; T1 mapping; Extracellular volume fraction

Journal: Korean Journal of Radiology

Author

Suyon Chang, Kyunghwa Han, Suji Lee, Young Joong Yang, Pan Ki Kim, Byoung Wook Choi, and Young Joo Suh

DOI: doi.org/10.1007/s00330-022-08936-8