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Abstract #0388

High Resolution MR Spectroscopic Imaging Using Deep Image Prior Constrained Subspace Modeling

Kuang Gong1, Paul Kyu Han1, Thibault Marin1, Georges El Fakhri1, Quanzheng Li1, and Chao Ma1
1Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States

The subspace-based method, known as SPICE, is an emerging technique that achieves rapid high-resolution MRSI with good SNR. In SPICE, the spectrum at each voxel is represented as a low-dimensional subspace or manifold, where the basis functions or features are learned from training data. The spatial coefficients of the subspace model are estimated by fitting the model to the k-space data for image reconstruction. In this work, we propose to extend the SPICE framework by representing the spatial coefficients of the subspace model using deep image prior for improved image reconstruction.

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