D
David MacDonald
Researcher at Montreal Neurological Institute and Hospital
Publications - 25
Citations - 5209
David MacDonald is an academic researcher from Montreal Neurological Institute and Hospital. The author has contributed to research in topics: Parametric statistics & MINC. The author has an hindex of 17, co-authored 25 publications receiving 4986 citations. Previous affiliations of David MacDonald include Autodesk & McGill University.
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Journal ArticleDOI
Growth patterns in the developing brain detected by using continuum mechanical tensor maps
TL;DR: The creation of spatially complex, four-dimensional quantitative maps of growth patterns in the developing human brain, detected using a tensor mapping strategy with greater spatial detail and sensitivity than previously obtainable is reported.
Journal ArticleDOI
Automated 3-D Extraction of Inner and Outer Surfaces of Cerebral Cortex from MRI
TL;DR: A general method of deforming polyhedra is presented here, with two novel features that are used advantageously to identify automatically the total surface of the outer and inner boundaries of cerebral cortical gray matter from normal human MR images, accurately locating the depths of the sulci.
Journal ArticleDOI
Automated 3-D extraction and evaluation of the inner and outer cortical surfaces using a Laplacian map and partial volume effect classification
June Sic Kim,Vivek Singh,Junki Lee,Jason P. Lerch,Yasser Ad-Dab'bagh,David MacDonald,Jongmin Lee,Sun I. Kim,Alan C. Evans +8 more
TL;DR: A novel method for improving the conventional ASP algorithm by making use of partial volume information through probabilistic classification in order to allow for topology preservation across a less restricted range of cortical thickness values is presented.
Book ChapterDOI
An MRI-Based Probabilistic Atlas of Neuroanatomy
TL;DR: Two new acquisition techniques with gradient echo as opposed to spin echo techniques allow for an improved signal-to-noise ratio in thin slices in times compatible with clinical examinations and the advent of high resolution MRI scanning offers finer spatial and contrast resolution in normal brain in vivo.
Journal ArticleDOI
Detecting changes in nonisotropic images.
TL;DR: The solution offered here is to suppose that the image can be warped or flattened into a space where the data are isotropic and the subsequent corrected P values do not depend on finding this warping; it is sufficient only to know that such a warping exists.