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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