Segmentation-aware MRI reconstruction

Abstract

Deep learning models have been broadly adopted for accelerating MRI acquisitions in recent years. A common approach is to train deep models based on loss functions that place equal emphasis on reconstruction errors across the field-of-view. This homogeneous weighting of loss contributions might be undesirable in cases where the diagnostic focus is on tissues in a specific subregion of the image. In this paper, we propose a framework for segmentation-aware reconstruction based on segmentation as a proxy task. We leverage an end-to-end model comprising reconstruction and segmentation networks; and leverage backpropagation of segmentation error to devise a pseudo-attention effect to focus the reconstruction network. We introduce a novel stabilization method to prevent convergence onto a local minima with unacceptably poor reconstruction or segmentation performance. Our stabilization approach initiates learning on fully-sampled acquisitions, and gradually increases the undersampling rate assumed in the training set to its desired value. We validate our approach for cardiac MR reconstruction on the publicly available OCMR dataset. Segmentation-aware reconstruction significantly outperforms vanilla reconstruction for cardiac imaging.