Making a Mathematical Diagnosis: How Combining Medical Imaging with Computational Science can Improve Patient Outcomes
Thursday, September 21, 2017 — 8:30AM - 9:15AM
Medical imaging is essential for diagnosing and treating many diseases. While imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT) provide a visualization of internal anatomy, functional imaging modalities provide information on physiological activity. For instance, positron emission tomography (PET) quantifies metabolic activity and pulmonary ventilation scans measure breathing. However, in comparison to CT imaging, functional imaging requires a longer acquisition time, has a lower spatial resolution, and is often susceptible to motion artifacts, particularly in the lungs. With the goal of addressing these shortcomings, my research team and I developed 4DCT-derived functional imaging (CT-FI). CT-FI is an image processing based modality that uses numerical optimization methods to quantify pulmonary function from dynamic computed tomography (often referred to as 4DCT). In this talk, I will present the mathematical derivation and numerical implementation of CT-FI, as well as how its application within cancer radiotherapy, diagnostic imaging, and emergency room medicine can improve patient outcomes.
Edward Castillo, Beaumont Health Research Institute