A novel framework to enhance scientific knowledge of cardiovascular MRI biomarkers and their application to pediatric cardiomyopathy classification

Cardiovascular Magnetic Resonance Imaging (CMRI) has become a powerful popular non-invasive tool for detecting biomarkers of various types of subtle pediatric cardiomyopathies yielding BIG temporal, high-resolution data. The complexities associated with the annotation of images and extraction of markers, necessitate the development of efficient workflows to acquire, manage and transform this data into actionable knowledge for patient care. We develop and test a novel framework called CMRI-BED for biomarker extraction and discovery from pediatric cardiac MRI data involving the use of a suite of tools for image processing, marker extraction and predictive modeling. We applied the workflow to obtain and analyze a small dataset containing CMRI-derived biomarkers for classifying positive versus negative findings of cardiomyopathy in children. Preliminary results show the feasibility of our framework for processing such data while also yielding actionable predictive classification rules that can augment knowledge conveyed in cardiac radiology outcome reports.