Joint Optimization of Sampling Pattern and Priors in Model-Based Deep Learning

Deep learning methods are emerging as powerful alternatives for compressed sensing MRI to recover images from highly undersampled data. Unlike compressed sensing, the image redundancies that are captured by these models are not well understood. The lack of theoretical understanding also makes it challenging to choose the sampling pattern that would yield the best possible recovery. To overcome these challenges, we propose to optimize the sampling patterns and the parameters of the reconstruction block in a model-based deep learning framework. We show that the joint optimization by the model-based strategy results in improved performance than direct inversion CNN schemes due to better decoupling of the effect of sampling and image properties. The quantitative and qualitative results confirm the benefits of joint optimization by the model-based scheme over the direct inversion strategy.