Medical Image Reconstruction and Processing

Medical imaging modalities like MRI, CT, and MPI don't hand you a picture — they hand you samples in Fourier or projection space, and turning that raw data into a diagnostically useful image is what this course is fundamentally about. You'll work through how sampling patterns (Cartesian, non-Cartesian, undersampled) dictate reconstruction strategy, implementing gridding, parallel imaging algorithms like SENSE and GRAPPA, and compressed sensing recovery across five homeworks and a project, then move into post-processing topics like denoising, registration, and segmentation. It builds directly on signals-and-systems and Fourier intuition from earlier EEE courses, and is the natural follow-on if you want to work in medical imaging research, industry MRI/CT vendors, or anywhere inverse problems show up.