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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