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

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.

Resolving the initial contour problem of GVF snake in the sequential images.

GVF algorithm has been studied actively and it will be able to apply to quite many applications. This paper presents GVF algorithm for the segmentation of a sequence of images. Traditional GVF algorithm can extract contour of object in an image. However, in the video sequence images or CT images traditional GVF algorithm had some problems. We can treat these problems under motion tracking using Motion Estimation using Mean Square Error (MSE). Initial point problem is very important in GVF algorithm. We applied extracted initial point of first image to the second image. In other word, previous initial contour can be used to the next initial contour.