Medical Image Imputation

Abstract:
We present an algorithm for creating high resolution anatomically
plausible images that are consistent with acquired clinical brain MRI
scans with large inter-slice spacing. Although large databases of
clinical images contain a wealth of information, medical acquisition
constraints result in sparse scans that miss much of the
anatomy. These characteristics often render computational analysis
impractical as standard processing algorithms tend to fail when
applied to such images. Our goal is to enable application of existing
algorithms that were originally developed for high resolution research
scans to severely undersampled images. We illustrate the applications
of the method in the context of neurodegeneration and white matter
disease studies in stroke patients.

Biography:
Polina Golland is a Henry Ellis Warren (1894) professor of Electrical
Engineering and Computer Science at MIT and a principal investigator
in the MIT Computer Science and Artificial Intelligence Laboratory
(CSAIL). She received her PhD in 2001 from MIT and her Bachelor and
Masters degrees in 1993 and 1995 from Technion, Israel. Polina’s
primary research interest is in developing novel techniques for
medical image analysis and understanding. With her students, Polina
has demonstrated novel approaches to image segmentation, shape
analysis, functional image analysis and population studies. She has
served as an associate editor of the IEEE Transactions on Medical
Imaging and of the IEEE Transactions on Pattern Analysis. Polina is
currently on the editorial board of the Journal of Medical Image
Analysis. She is a Fellow of the International Society for Medical
Image Computing and Computer Assisted Interventions.

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