Keywords: Image Reconstruction, Image Reconstruction, Dynamic MRI reconstruction
Motivation: Partially separable functions (PSF) are commonly used in dynamic MRI to model the signal in the (x,t)-space; however, the number of components in the PSF expansion can be restrictive in applications where voxels present different temporal/spectral characteristics (e.g., gastrointestinal MRI reconstruction).
Goal(s): To propose a parsimonious representation for the dynamic MRI signal, called spatiotemporal maps (STM).
Approach: STM are derived by proving that shift-invariant linear predictability relationships can exist across the k-spaces of multiple time-frames.
Results: STM provide a parsimonious representation for the spatiotemporal MRI signal; they can be efficiently calculated from autocalibration data; and they can be synergistically combined with modern regularizers.
Impact: Spatiotemporal maps provide a parsimonious voxel-dependent expansion for the dynamic MRI signal, even when voxels present various temporal/spectral characteristics. They can be efficiently calculated from autocalibration data, and can be synergistically combined with modern regularizers to reconstruct highly accelerated data.
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