Memory-friendly and Robust Deep Learning Architecture for Accelerated MRI

作者：Zi Wang, Chen Qian, Di Guo, Hongwei Sun, Rushuai Li, Xiaobo Qu · 发表于：Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · 年份：2023 · DOI：10.58530/2022/4301 · 研究领域：Advanced MRI Techniques and Applications、Medical Imaging Techniques and Applications、Advanced X-ray and CT Imaging

Deep learning has shown astonishing performance in accelerated MRI. Most methods adopt the convolutional neural network and perform 2D convolution since many MR images or their corresponding k-space are in 2D. In this work, we try a different approach that explores the memory-friendly 1D convolution, making the deep network easier to be trained and generalized. Furthermore, a one-dimensional deep learning architecture (ODL) is proposed for MRI reconstruction. Results demonstrate that, the proposed ODL provides improved reconstructions than state-of-the-art methods and shows nice robustness to some mismatches between the training and test data.