自动化学报2017，Vol.43Issue(10)：1841-1849,9.DOI:10.16383/j.aas.2017.e160135
基于梯度域的卷积稀疏编码磁共振成像重建
Convolutional Sparse Coding in Gradient Domain for MRI Reconstruction
摘要
Abstract
Magnetic resonance imaging (MRI) reconstruction from undersampled data has always been a challenging and fascinating task due to its implicit ill-posed nature and its significance accompanied with the emerging compressed sensing (CS) theory.Most state-of-the-art sparse representation based CS approaches partition the image into overlapped patches,and process each patch separately.These methods,however,lose important spatial structures of the signal of interest,and ignore the consistency of pixels,which is a strong constraint for MR image.In this paper,we propose a new reconstruction method,which builds on recently introduced ideas of convolutional sparse coding in gradient domain (GradCSC) to address above mentioned issue.As opposed to patch-based methods,GradCSC directly operates on the whole gradient image to capture the correlation between local neighborhoods and exploits the gradient image global correlation to produce better edges and sharp features of gradient image.It enables local features implicitly existed in the gradient images to be captured effectively.Extensive experimental results demonstrate that the proposed algorithm achieves higher quality reconstructions than alternative methods and converges quickly at various sampling schemes and k-space acceleration factors关键词
Alternating direction method of multipliers/convolutional sparse coding (CSC)/filter learning/gradient image/magnetic resonance imaging (MRI) reconstructionKey words
Alternating direction method of multipliers/convolutional sparse coding (CSC)/filter learning/gradient image/magnetic resonance imaging (MRI) reconstruction引用本文复制引用
熊娇娇,卢红阳,张明辉,刘且根..基于梯度域的卷积稀疏编码磁共振成像重建[J].自动化学报,2017,43(10):1841-1849,9.基金项目
This work was supported by the National Natural Science Foundation of China (61362001,61661031),Jiangxi Province Innovation Projects for Postgraduate Funds (YC2016-S006),the International Postdoctoral Exchange Fellowship Program,and Jiangxi Advanced Project for Post-Doctoral Research Fund (2014KY02). （61362001,61661031）
