Presentation description

Heart disease is responsible for death in almost 700,000 individuals in the United States in 2020 according to the CDC. Coronary heart disease (CAD) is the most common form of heart disease. Current methods of diagnosing involve administering gadolinium-based contrast which has proven to be malignant on the pancreatic system. At times, patients are given additional contrast if they move while inside a claustrophobic MRI machine, making the images too blurry to diagnose with precision. Alternative methods of diagnosis are imperative for patients that already have kidney issues. T1 mapping methods have shown to be a promising alternative for this issue. In this project, I've compared several T1 mapping software including Matlab and our in-house software known as CVI 42. Utilizing this software has been shown to accurately diagnose Coronary heart disease with minimal error. In this project, we've successfully detected CAD using Matlab and CVI 42. Future implications and improvements of this project could speed up the diagnosing process using deep learning methods. Additional improvements could make T1 mapping more accurate than administering contrast, even if the patient moves.