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 introduce a generative model that captures fine-scale anatomical similarity across subjects in clinical image collections and use it to fill in the missing data in scans with large slice spacing. Our experimental results demonstrate the promise of the resulting algorithm in a context of large studies of neurodegeneration and acute stroke.

Short Bio: Polina Golland is a professor of EECS at MIT CSAIL. She received her Ph.D. from MIT in and her Bachelor and Masters degree from Technion, Israel. Polina’s primary research interest is in developing novel techniques for medical image analysis and understanding. With her students, she has demonstrated novel approaches to image segmentation, shape analysis, functional image analysis and population studies. Polina has served as an associate editor of the IEEE Transactions on Medical Imaging and of the IEEE Transactions on Pattern Analysis and Machine Intelligence and is currently serving on the editorial board of Journal of Medical Image Analysis. She is a Fellow of the International Society for Medical Image Computing and Computer Assisted Interventions.