Year: 2021
Type: article
Abstract: Recently, low-dimensional manifold regularization has been recognized as a competitive method for accelerated cardiac MRI, due to its ability to capture temporal correlations. However, existing methods have not been performed with the nonlinear structure of an underlying manifold. In this paper, we propose a deep learning method in an unrolling manner for accelerated cardiac MRI on a low-dimensional manifold. Specifically, a fixed low-rank tensor (Riemannian) manifold is chosen to capture the strong temporal correlations of dynamic signals; the reconstruction problem is modeled as a CS-based optimization problem on this manifold. Following the manifold structure, a Riemannian gradient descent (RGD) method is adopted to solve this problem. Finally, the RGD algorithm is unrolled into a neural network, called Manifold-Net, on the manifold to avoid the long computation time and the challenging parameter selection. The experimental results at high accelerations demonstrate that the proposed method can obtain improved reconstruction compared with three conventional methods (k-t SLR, SToRM and k-t MLSD) and three state-of-the-art deep learning-based methods (DC-CNN, CRNN, and SLR-Net). To our knowledge, this work represents the first study to unroll the iterative optimization procedure into neural networks on manifolds. Moreover, the designed Manifold-Net provides a new mechanism for low-rank priors in dynamic MRI and should also prove useful for fast reconstruction in other dynamic imaging problems.
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: green
Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, China Postdoctoral Science Foundation, Innovation and Technology Commission, Natural Science Foundation of Guangdong Province, Key Laboratory for Magnetic Resonance and Multimodality Imaging of Guangdong Province, Hong Kong Government, Shenzhen Peacock Plan
Awards: G110000886, G1767024223, 基于非线性梯度编码的磁共振快速成像方法研究, G1851828118, G235029948, G2982186687, G3138754842, G3276031618, G3315182727, G3673919701, G403923243, G421072246, G4511200576, 磁共振T1 T2联合对比成像精准检测心肌水肿的关键问题研究, G510270982, G529619121, G529727672, G6540639742, 脑卒中相关血管床粥样硬化斑块的快速磁共振成像及智能诊断研究, G828489505, G8966054533
OpenAlex ID: W3216102226