6.03, Introduction to EECS from a Medical Technology Perspective – Spring Semester
Explores biomedical signals generated from electrocardiograms, glucose detectors, ultrasound and magnetic resonance images. Topics include physical characterization and modeling of systems in the time and frequency domains; analog and digital signals and noise; basic machine learning including decision trees, clustering, and classification; and introductory machine vision.
C. M. Stultz, J. White, J. Voldman, B. Anthony, E. Adalsteinsson
6.556/HST.580, Data Acquisition and Image Reconstruction in MRI – Fall Semester
This class applies analysis of signals and noise in linear systems, sampling, and Fourier properties to magnetic resonance (MR) imaging acquisition and reconstruction. Provides adequate foundation for MR physics to enable study of RF excitation design, efficient Fourier sampling, parallel encoding, reconstruction of non-uniformly sampled data, and the impact of hardware imperfections on reconstruction performance. Active areas of MR research are surveyed. Assignments include Matlab-based work with data from clinical scanners. Laboratory sessions and independent projects are performed on table-top MRI systems that are programmable by students.