Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2006

Abstract:: This team taught, multidisciplinary course covers the fundamentals of magnetic resonance imaging relevant to the conduct and interpretation of human brain mapping studies. The challenges inherent in advancing our knowledge about brain function using fMRI are presented first to put the work in context. The course then provides 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, building and applying statistical models for fMRI data. Human subjects issues including informed consent, institutional review board requirements and safety in the high field environment are presented.

"Functional Magnetic Resonance Imaging: Data Acquisition and Analysis, Fall 2008"

Abstract:: " 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 "

Abstract:: Deriving a symbolic description of the environment from an image. Understanding physics of image formation. Image analysis as an inversion problem. Binary image processing and filtering of images as preprocessing steps. Recovering shape, lightness, orientation, and motion. Using constraints to reduce the ambiguity. Photometric stereo and extended Gaussian sphere. Applications to robotics; intelligent interaction of machines with their environment. Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.