Abstract
Speeding up Magnetic Resonance Imaging (MRI) is an inevitable task in capturing multi-contrast MR images for medical diagnosis. In MRI, some sequences, e.g., in T2 weighted imaging, require long scanning time, while T1 weighted images are captured by short-time sequences. To accelerate MRI, in this paper, we propose a model-driven deep attention network, dubbed as MD-DAN, to reconstruct highly under-sampled long-time sequence MR image with the guidance of a certain short-time sequence MR image. MD-DAN is a novel deep architecture inspired by the iterative algorithm optimizing a novel MRI reconstruction model regularized by cross-contrast prior using a guided contrast image. The network is designed to automatically learn cross-contrast prior by learning corresponding proximal operator. The backbone network to model the proximal operator is designed as a dual-path convolutional network with channel and spatial attention modules. Experimental results on a brain MRI dataset substantiate the superiority of our method with significantly improved accuracy. For example, MD-DAN achieves PSNR up to 35.04 dB at the ultra-fast 1/32 sampling rate.
Y. Yang, N. Wang—Contributed equally to this work.
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Acknowledgement
This work was supported in part by NSFC under Grants 11971373, 61976173, 11690011, 61721002, U1811461, and in part by the National Key Research and Development Program of China under Grant 2018AAA0102201.
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Yang, Y., Wang, N., Yang, H., Sun, J., Xu, Z. (2020). Model-Driven Deep Attention Network for Ultra-fast Compressive Sensing MRI Guided by Cross-contrast MR Image. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12262. Springer, Cham. https://doi.org/10.1007/978-3-030-59713-9_19
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