EVCMR: A tool for the quantitative evaluation and visualization of cardiac MRI data

• Published in: Computers in biology and medicine Vol. 111; p. 103334
• Main Authors: Kim, Yoon-Chul, Kim, Khu Rai, Choi, Kwanghee, Kim, Minwoo, Chung, Younjoon, Choe, Yeon Hyeon
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01-08-2019; Elsevier Limited
• Subjects: Cardiovascular diseases; Computer applications; Coronary artery disease; Correlation coefficient; Correlation coefficients; Deep learning; Evaluation; Gadolinium; Heart; Heart diseases; Image processing; Image segmentation; Machine learning; Magnetic resonance imaging; Mapping; Masks; Modules; Morphology; MRI; Myocardium; NMR; Nuclear magnetic resonance; Open source software; Perfusion; Post-production processing; Public domain; Python; Quantitative analysis; Software packages; User interface; Ventricle; Visualization; Zooming; Germany; Heart; Deep learning; Image segmentation; Visualization; MRI; Python
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2019.103334

Quantitative evaluation of diseased myocardium in cardiac magnetic resonance imaging (MRI) plays an important role in the diagnosis and prognosis of cardiovascular disease. The development of a user interface with state-of-the-art techniques would be beneficial for the efficient post-processing and analysis of cardiac images. The aim of this study was to develop a custom user interface tool for the quantitative evaluation of the short-axis left ventricle (LV) and myocardium. Modules for cine, perfusion, late gadolinium enhancement (LGE), and T1 mapping data analyses were developed in Python, and a module for three-dimensional (3D) visualization was implemented using PyQtGraph library. The U-net segmentation and manual contour correction in the user interface were effective in generating reference myocardial segmentation masks, which helped obtain labeled data for deep learning model training. The proposed U-net segmentation resulted in a mean Dice score of 0.87 (±0.02) in cine diastolic myocardial segmentation. The LV mass measurement of the proposed method showed good agreement with that of manual segmentation (intraclass correlation coefficient = 0.97, mean difference and 95% Bland-Altman limits of agreement = 4.4 ± 12.2 g). C++ implementation of voxel-wise T1 mapping and its binding via pybind11 led to a significant computational gain in calculating the T1 maps. The 3D visualization enabled fast user interactions in rotating and zooming-in/out of the 3D myocardium and scar transmurality. The custom tool has the potential to provide a fast and comprehensive analysis of the LV and myocardium from multi-parametric MRI data in clinical settings.