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Abstract #0518

Accelerated Spectral Fitting Using Convolutional Neural Networks

Saumya Gurbani1, Sulaiman Sheriff2, Andrew Maudsley2, Lee Cooper3, and Hyunsuk Shim1,4

1Radiation Oncology, Emory University, Atlanta, GA, United States, 2Radiology and Imaging Sciences, University of Miami, Miami, FL, United States, 3Biomedical Informatics, Emory University, Atlanta, GA, United States, 4Radiology and Imaging Sciences, Emory University, Atlanta, GA, United States

3D whole-brain spectroscopic MRI can measure quantitative metabolite concentrations without any contrast agents and is useful in identifying occult glioblastoma beyond that seen on standard MRI. However, a key hurdle in its widespread adoption is spectral fitting, which can take up to an hour for scan consisting of ~10,000 voxels. In this work, we develop a deep learning architecture for rapid spectral fitting within the context of an a priori spectral model. We demonstrate that this architecture can perform whole-brain spectral fitting in <30 seconds, pushing spectroscopic MRI towards on-board scanner processing to fit in the rapid clinical workflow.

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