M
Michael W. Vannier
Researcher at University of Chicago
Publications - 414
Citations - 20701
Michael W. Vannier is an academic researcher from University of Chicago. The author has contributed to research in topics: Iterative reconstruction & Tomography. The author has an hindex of 69, co-authored 391 publications receiving 19661 citations. Previous affiliations of Michael W. Vannier include University of Washington & Roy J. and Lucille A. Carver College of Medicine.
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Journal ArticleDOI
Subgenual prefrontal cortex abnormalities in mood disorders
Wayne C. Drevets,Joseph L. Price,Joseph R. Simpson,Richard D. Todd,Theodore Reich,Michael W. Vannier,Michael W. Vannier,Marcus E. Raichle +7 more
TL;DR: Using positron emission tomographic images of cerebral blood flow and rate of glucose metabolism to measure brain activity, an area of abnormally decreased activity is localized in the pre-frontal cortex ventral to the genu of the corpus callosum in both familial bipolar depressives and familial unipolar depressives.
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Hippocampal atrophy in recurrent major depression
Yvette I. Sheline,Po W. Wang,Po W. Wang,Mokhtar H. Gado,John G. Csernansky,Michael W. Vannier +5 more
TL;DR: The results suggest that depression is associated with hippocampal atrophy, perhaps due to a progressive process mediated by glucocorticoid neurotoxicity.
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Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?
TL;DR: In this article, the authors examine the reasons for the disconnection between theoretical and application-oriented research in computed tomography (CT) and provide recommendations on how it can be resolved.
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Three dimensional CT reconstruction images for craniofacial surgical planning and evaluation.
TL;DR: Applied to CT studies of complex craniofacial abnormalities, this method has delineated abnormal facial soft tissue and bony morphology, facilitated surgical planning, and improved quantitative postoperative evaluation.
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Iterative deblurring for CT metal artifact reduction
TL;DR: In experiments with synthetic noise-free and additive noisy projection data of dental phantoms, it is found that both simultaneous iterative algorithms produce superior image quality as compared to filtered backprojection after linearly fitting projection gaps.