Robust MR image super‐resolution reconstruction with cross‐modal edge‐preserving regularization

Abstract

AbstractIn this article, we propose a novel image super‐resolution (SR) reconstruction method in the field of magnetic resonance imaging, which is based on a cross‐modal edge‐preserving regularization integrating the internal gradient prior from the target‐modal image itself and the external gradient prior from the reference‐modal image obtained by pre‐scan in many medical imaging scenes. The reference‐modal image is a high‐resolution guidance image that has much shareable information such as gradient orientation on edge regions, which can be used to improve the image resolution of the target modal. In addition, to be robust against the misalignment between the target‐modal image and reference‐modal image, a multimodal registration is incorporated in the SR reconstruction process. In this work, the proposed SR method can be formulated as an alternating optimization problem, that is, the target‐modal and reference‐modal images are alternately updated through iterations. Experimental results on simulated and realistic images show the superior performance of the proposed approach over several state‐of‐the‐art SR techniques.