Joint Edge Optimization Deep Unfolding Network for Accelerated MRI Reconstruction

IF 4.2 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Yu Luo;Yue Cai;Jie Ling;Yingdan Ji;Yanmei Tie;Shun Yao
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引用次数: 0

Abstract

Magnetic Resonance Imaging (MRI) is a widely used imaging technique, however it has the limitation of long scanning time. Though previous model-based and learning-based MRI reconstruction methods have shown promising performance, most of them have not fully utilized the edge prior of MR images, and there is still much room for improvement. In this paper, we build a joint edge optimization model that not only incorporates individual regularizers specific to both the MR image and the edges, but also enforces a co-regularizer to effectively establish a stronger correlation between them. Specifically, the edge information is defined through a non-edge probability map to guide the image reconstruction during the optimization process. Meanwhile, the regularizers pertaining to images and edges are incorporated into a deep unfolding network to automatically learn their respective inherent a-priori information. Numerical experiments, consisting of multi-coil and single-coil MRI data with different sampling schemes at a variety of sampling factors, demonstrate that the proposed method outperforms other state-of-the-art methods.
联合边缘优化深度展开网络加速MRI重建
磁共振成像(MRI)是一种应用广泛的成像技术,但其扫描时间长。虽然以往基于模型和基于学习的MRI重建方法表现出了良好的性能,但大多数方法都没有充分利用MR图像的边缘先验,还有很大的改进空间。在本文中,我们建立了一个联合边缘优化模型,该模型不仅包含了特定于MR图像和边缘的单个正则器,而且还强制执行了一个协正则器,以有效地建立它们之间更强的相关性。具体来说,在优化过程中,通过非边缘概率映射定义边缘信息来指导图像重建。同时,将图像和边缘的正则化器合并到一个深度展开网络中,自动学习它们各自固有的先验信息。数值实验,包括不同采样方案下的多线圈和单线圈MRI数据,在各种采样因素下,表明该方法优于其他最先进的方法。
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来源期刊
IEEE Transactions on Computational Imaging
IEEE Transactions on Computational Imaging Mathematics-Computational Mathematics
CiteScore
8.20
自引率
7.40%
发文量
59
期刊介绍: The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.
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