This course presents the fundamentals of digital signal processing with particular emphasis on problems in biomedical research and clinical medicine. It covers principles and algorithms for processing both deterministic and random signals. Topics include data acquisition, imaging, filtering, coding, feature extraction, and modeling. The focus of the course is a series of labs that provide practical experience in processing physiological data, with examples from cardiology, speech processing, and medical imaging. The labs are done on the MIT Server in MATLAB® during weekly lab sessions that take place in an electronic classroom. Lectures cover signal processing topics relevant to the lab exercises, as well as background on the biological signals processed in the labs.

This course has been designed as a seminar to give students an understanding of how scientists with medical or scientific degrees conduct research in both hospital and academic settings. There will be interactive discussions with research clinicians and scientists about the career opportunities and research challenges in the biomedical field, which an MIT student might prepare for by obtaining an MD, PhD, or combined degrees. The seminar will be held in a case presentation format, with topics chosen from the radiological sciences, including current research in magnetic resonance imaging, positron emission tomography and other nuclear imaging techniques, and advances in radiation therapy. With the lectures as background, we will also examine alternative and related options such as biomedical engineering, medical physics, and medical engineering. We'll use as examples and points of comparisons the curriculum paths available through MIT's Department of Nuclear Science and Engineering. In past years we have given very modest assignments such as readings in advance of or after a seminar, and a short term project.

" This team-taught multidisciplinary course provides information relevant to the conduct and interpretation of human brain mapping studies. It begins with in-depth coverage of the physics of image formation, mechanisms of image contrast, and the physiological basis for image signals. Parenchymal and cerebrovascular neuroanatomy and application of sophisticated structural analysis algorithms for segmentation and registration of functional data are discussed. Additional topics include: fMRI experimental design including block design, event related and exploratory data analysis methods, and building and applying statistical models for fMRI data; and human subject issues including informed consent, institutional review board requirements and safety in the high field environment. Additional Faculty Div Bolar Dr. Bradford Dickerson Dr. John Gabrieli Dr. Doug Greve Dr. Karl Helmer Dr. Dara Manoach Dr. Jason Mitchell Dr. Christopher Moore Dr. Vitaly Napadow Dr. Jon Polimeni Dr. Sonia Pujol Dr. Bruce Rosen Dr. Mert Sabuncu Dr. David Salat Dr. Robert Savoy Dr. David Somers Dr. A. Gregory Sorensen Dr. Christina Triantafyllou Dr. Wim Vanduffel Dr. Mark Vangel Dr. Lawrence Wald Dr. Susan Whitfield-Gabrieli Dr. Anastasia Yendiki "

This lesson introduces the MRI Safety Grand Challenge question. Students are asked to write journal responses to the question and brainstorm what information they will need to answer the question. The ideas are shared with the class and recorded. Students then watch a video interview with a real life researcher to gain a professional perspective on MRI safety and brainstorm any additional ideas. The associated activity provides students the opportunity to visualize magnetic fields.

X-rays, CT scans and MRI scans are all medical imaging techniques of great practical importance that have been encountered by a great many people in their medical histories. This unit illustrates how these techniques work – and their limitations and advantages.

This lesson ties the preceding lessons together and brings students back to the grand challenge question on MRI safety. During this lesson, students focus on the logistics of magnetic resonance imaging as well as the MRI hardware. Students can then integrate this knowledge with their acquired knowledge on magnetic fields to solve the challenge question.

This course explores the social relevance of neuroscience, considering how emerging areas of brain research at once reflect and reshape social attitudes and agendas. Topics include brain imaging and popular media; neuroscience of empathy, trust, and moral reasoning; new fields of neuroeconomics and neuromarketing; ethical implications of neurotechnologies such as cognitive enhancement pharmaceuticals; neuroscience in the courtroom; and neuroscientific recasting of social problems such as addiction and violence. Guest lectures by neuroscientists, class discussion, and weekly readings in neuroscience, popular media, and science studies.

Principles of tomographic imaging using ionizing and non-ionizing radiation, and ultrasound. Emphasis is placed on fundamental physics and mathematics involved in image formation, including basic interactions, data acquisition and reconstruction. Planar radiographic imaging, multi-dimensional tomography (X-ray CAT, PET, SPECT), ultrasound, and NMR imaging covered. 22.56J aims to give graduate students and advanced undergraduates background in the theory and application of noninvasive imaging methods to biology and medicine, with emphasis on neuroimaging. The course focuses on the modalities most frequently used in scientific research (X-ray CT, PET/SPECT, MRI, and optical imaging), and includes discussion of molecular imaging approaches used in conjunction with these scanning methods. Lectures by the professor will be supplemented by in-class discussions of problems in research, and hands-on demonstrations of imaging systems.

Is it a tumor? Magnetic Resonance Imaging (MRI) can tell. Your head is full of tiny radio transmitters (the nuclear spins of the hydrogen nuclei of your water molecules). In an MRI unit, these little radios can be made to broadcast their positions, giving a detailed picture of the inside of your head.

In this lesson, students begin to focus on the torque associated with a current carrying loop in a magnetic field. Students are prompted with example problems and use diagrams to visualize the vector product. In addition, students learn to calculate the energy of this loop in the magnetic field. Several example problems are included and completed as a class. A homework assignment is also attached as a means of student assessment.