基于梯度域的卷积稀疏编码磁共振成像重建（英文）

发布时间：2019-05-15 10:59

【摘要】：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(Grad CSC)to address above mentioned issue.As opposed to patch-based methods,Grad CSC 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.
[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(Grad CSC)to address above mentioned issue.As opposed to patch-based methods,Grad CSC 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.
【作者单位】： School
【基金】：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 Jiangxi Advanced Project for Post-Doctoral Research Fund(2014KY02)
【分类号】：TP391.41

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