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Modern MRI schemes rely on parallel imaging hardware to accelerate the acquisition. The reconstruction quality heavily depends on the specific sampling pattern used during acquisition. The main focus of this work is to jointly optimize the sampling pattern and the deep prior in a Model-Based Deep Learning (MoDL)[1] framework with application to parallel MRI. Model-based schemes use the information of the sampling pattern within the reconstruction algorithm, thus decoupling the CNN block from changes in sampling pattern.
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Joint Model-Based Learning for Optimized Sampling in Paralell MRI
Modern MRI schemes rely on parallel imaging hardware to accelerate the acquisition. The reconstruction quality heavily depends on the specific sampling pattern used during acquisition. The main focus of this work is to jointly optimize the sampling pattern and the deep prior in a Model-Based Deep Learning (MoDL)[1] framework with application to parallel MRI. Model-based schemes use the information of the sampling pattern within the reconstruction algorithm, thus decoupling the CNN block from changes in sampling pattern.