.png){kind=icon}
返回
Parallel MRI reconstruction using variance partitioning regularization
.png){kind=icon}
OA
AI
DOI：10.1002/mrm.21356.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Multiple receivers can be utilized to enhance the spatiotemporal resolution of MRI by employing the parallel imaging technique. Previously, we have reported the L-curve Tikhonov regularization technique to mitigate noise amplification resulting from the geometrical correlations between channels in a coil array. Nevertheless, one major disadvantage of regularized image reconstruction is lengthy computational time in regularization parameter estimation. At a fixed noise level, L-curve regularization parameter estimation was also found not to be robust across repetitive measurements, particularly for low signal-to-noise ratio (SNR) acquisitions. Here we report a computationally efficient and robust method to estimate the regularization parameter by partitioning the variance of the noise-whitened encoding matrix based on the estimated SNR of the aliased pixel set in parallel MRI data. The proposed Variance Partitioning Regularization (VPR) method can improve computational efficiency by 2-5-fold, depending on image matrix sizes and acceleration rates. Our anatomical and functional MRI results show that the VPR method can be applied to both static and dynamic MRI experiments to suppress noise amplification in parallel MRI reconstructions for improved image quality.
Keyword:
regularization
VPR
SNR
L-curve
SVD
variance partitioning
Tikhonov
parallel MRI
SENSE
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3
论文数:
1.2W
被引数:
3.1W
机构
暂无机构信息
引用论文
.png){kind=icon}
Simultaneous acquisition of spatial harmonics (SMASH): Fast imaging with radiofrequency coil arrays同时采集空间谐波 (SMASH): 使用射频线圈阵列进行快速成像
PREAI
分享收藏
分享收藏Design of a SENSE-optimized high-sensitivity MRI receive coil for brain imaging用于脑成像的SENSE优化高灵敏度MRI接收线圈设计OAAI
OAAI分享收藏
查看更多内容