Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI

• URL: http://arxiv.org/abs/2004.10536v1
• Date: Wed, 22 Apr 2020 12:50:03 GMT
• Title: Learning Sampling and Model-Based Signal Recovery for Compressed Sensing MRI
• Authors: Iris A.M. Huijben, Bastiaan S. Veeling, and Ruud J.G. van Sloun
• Abstract summary: Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality. We propose joint learning of both task-adaptive k-space sampling and a subsequent model-based proximal-gradient recovery network. The proposed combination of a highly flexible sampling model and a model-based (sampling-adaptive) image reconstruction network facilitates exploration and efficient training, yielding improved MR image quality.
• Score: 30.838990115880197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Compressed sensing (CS) MRI relies on adequate undersampling of the k-space to accelerate the acquisition without compromising image quality. Consequently, the design of optimal sampling patterns for these k-space coefficients has received significant attention, with many CS MRI methods exploiting variable-density probability distributions. Realizing that an optimal sampling pattern may depend on the downstream task (e.g. image reconstruction, segmentation, or classification), we here propose joint learning of both task-adaptive k-space sampling and a subsequent model-based proximal-gradient recovery network. The former is enabled through a probabilistic generative model that leverages the Gumbel-softmax relaxation to sample across trainable beliefs while maintaining differentiability. The proposed combination of a highly flexible sampling model and a model-based (sampling-adaptive) image reconstruction network facilitates exploration and efficient training, yielding improved MR image quality compared to other sampling baselines.

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