A dual-task dual-domain model for blind MRI reconstruction.
Journal:
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Published Date:
Jan 12, 2021
Abstract
MRI reconstruction is the key technology to accelerate MR acquisition. Recent cascade models have gained satisfactory results, however, they deeply rely on the known sample mask, which we call it mask prior. To restore the MR image without mask prior, we designed an auxiliary network to estimate the mask from sampled k-space data. Experimentally, the sample mask can be completely estimated by the proposed network and be used to input to the cascade models. Moreover, we rethink the MRI reconstruction model as a k-space inpainting task. A dual-domain cascade network, which utilized partial convolutional layers to inpaint features in k-space, was presented to restore the MR image. Without the mask prior, our blind reconstruction model demonstrates the best reconstruction ability in both 4x acceleration and 8x acceleration.
