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Free keywords:
Open-source framework
Cardiac magnetic resonance imaging
Computer-assisted methods
Heart diagnostic imaging
Image processing
Real-time imaging
Abstract:
In medicine, especially in radiology, artificial intelligence has sparked a growing interest in automated systems, image analysis, and acquisition standardization. In the wake of this standardization, the research field of “radiomics” has gained importance. Using computer-aided analysis, image data and contours can be evaluated to determine numerical values for shape, size, and gray-scale texture, which can then be examined in a clinical context. Especially in cardiovascular imaging, data acquisition and analysis in different cardiac and respiratory phases are of great interest. However, most research studies use parameters that have been laboriously calculated by hand. “ShortCardiac” is a Python-based framework with a user-friendly GUI for the quantitative determination of cardiac MR parameters. This allows researchers to utilize quantitative MR research for their studies without programming knowledge, with just a few clicks. All calculated parameters can be displayed graphically. “shortCardiac” allows the visualization of segmentation contours, the angle-dependent length measurement, the center of gravity and much more, in addition, the background can be hidden, and the images can be cropped automatically. In addition, “shortCardiac” can also be called via python and due to the object-oriented design, it is possible to integrate new segmentation frameworks with little effort in the future as well as to determine additional parameters. However, “ShortCardiac” comes with certain limitations. It only assesses cardiac short-axis data and functions merely as a post-processing framework for determining surrogate parameters based on segmentation and image information. Manual segmentations or usage of fully automated segmentations, such as Circle cvi42, require additional software tools. Regardless of these restrictions, “ShortCardiac” provides an efficient, user-friendly tool, enabling researchers to capitalize on the expanding domain of radiomics.
