SCH:Artificial Intelligence for Contrast-Enhanced Imaging

The instances of liver tumors and its associated cost has been increasing steadily in the last few years. Diagnosing liver tumors currently requires injecting patients with a chemical contrast agent while doing magnetic resonance imaging (MRI). These chemical contrast agents are time consuming to administer, expensive, and have morbid side effects for many individuals. This Smart and Connected Health (SCH) award brings together a multidisciplinary team, comprising researchers from computer science, biomedical engineering, and clinical radiology to develop an Artificial Intelligence generated virtual contrast MRI thereby reducing the time, cost, and morbidity due to the chemical contrast agent. This project will provide a diverse group of students and clinical fellows with interdisciplinary training in machine learning, image processing, and medical imaging. Additionally, this project will engage students from middle/high school students (K-12 outreach) to doctoral students and postdoctoral fellows. This project proposes novelties in contextual adversarial learning, uncertainty, and reliability-based analysis to enable fundamental understanding and computer modelling of contrast enhanced imaging. The project proposes to (1) investigate “contrasomics”, a brand new category of contextual features; (2) develop novel cross-domain contextual models to detect, classify, and quantify lesions, and to synthesize virtual contrast images that have equivalent diagnostic value with real contrast enhanced imaging; (3) develop novel uncertainty and reliability analysis to gain the trust of end users; (4) validate the models with liver cancer/tumour classification using MRI. This project brings together computer science, biomedical engineering, and clinical radiology researchers and proposes an integrated multi-disciplinary education and outreach program to achieve the broadest possible dissemination of the knowledge gained from this work. The project also proposes potential future transfer to practice (industry and clinic) and wide dissemination of advances to broad communities beyond the image processing, machine learning, and medical data analytics domains. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.