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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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Saumya Gurbani
Biomedical Engineering Ph.D. Thesis Defense
Date: Tuesday, March 12, 2019
Time: 3-4pm
Location: Kauffman Auditorium, 5th floor Winship Cancer Institute, Emory University
Advisors:
Hyunsuk Shim, PhD
Lee Cooper, PhD
Committee Members:
Melissa Kemp, PhD
Peng Qiu, PhD
Hui-Kuo Shu, MD, PhD
David Yu, MD, PhD
Title: Machine Learning Enables the use of Spectroscopic MRI to Guide Radiation Therapy in Patients with Glioblastoma
Abstract:
Glioblastoma is the most common adult primary brain tumor and is highly aggressive due to its diffusely infiltrative nature - median survival is just 15 months despite resection, radiation, and chemotherapy. Radiation therapy has been shown to be the best single treatment for improving prognosis but requires accurate pre-therapy imaging for proper radiation dosage planning. Proton spectroscopic magnetic resonance imaging (sMRI) is an advanced imaging modality that measures specific in vivo metabolite levels within the brain and has shown to be highly sensitive and specific in the detection of proliferative pathology. Clinical application of sMRI has been extremely limited due to computational challenges in sMRI data analysis. Current analysis pipelines require skilled user intervention at multiple points, there are no consistent models for spectral quality assessment and artifact removal, and existing computational techniques are inadequate for reliable tumor segmentation. In this work, we utilize novel machine learning architectures to develop a software framework to close the gap for clinical utilization of sMRI in radiation therapy planning. First, we develop convolutional neural network that is able to identify and remove spectral artifacts that lead to erroneous measurement. Next, we develop an algorithm for internally normalizing sMRI volumes, enabling voxel-to-voxel comparison across subjects and allowing threshold-based techniques to be used for target delineation. Third, we create a novel unsupervised learning framework to perform accelerated spectral quantitation, reducing the computational time and power needed to utilize sMRI. Finally, we develop a web-based software framework that bridges the gap between sMRI and its clinical use, and demonstrate the feasibility of using this software in a multi-site clinical study to guide a radiation boost to regions of metabolic abnormality in patients with glioblastoma.
