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Abstract
Every year, millions of brain MRI scans are acquired in hospitals, which is a figure considerably larger than the size of any research dataset. Therefore, the ability to analyze such scans could transform neuroimaging research. Yet, their potential remains untapped since no automated algorithm is robust enough to cope with the high variability in clinical acquisitions (MR contrasts, resolutions, orientations, artifacts, and subject populations). In this talk, I will present techniques developed by my group over the last 2-3 years that enables robust analysis of heterogeneous clinical datasets "in the wild", including segmentation, registration, super-resolution, and synthesis. I will present results on thousands of brain scans from our hospital with highly heterogeneous orientation, resolution, and contrast, as well as results on low-field scans acquired with a portable scanner.
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Bio
Juan Eugenio Iglesias holds M.Sc. degrees in Telecom and Electrical Engineering from the University of Seville (Spain) and the Royal Institute of Technology (KTH, Stockholm, Sweden), respectively. He received his Ph.D. in Biomedical Engineering from UCLA sponsored by a Fulbright grant. After two postdoctoral positions at the Martinos Center for Biomedical Imaging (MGH / Harvard Medical School) and the BCBL (sponsored by a Marie Curie fellowship), he moved to University College London as junior faculty with a Starting Grant of the ERC. He recently moved back to the Martinos Center in Boston, where he also holds an affiliate appointment at MIT.
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