.png){kind=icon}
返回
Parallel image reconstruction using b-spline approximation (PROBER)
.png){kind=icon}
OA
AI
DOI：10.1002/mrm.21366.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
A new reconstruction method for parallel MRI called PROBER is proposed. The method PROBER works in an image domain similar to methods based on Sensitivity Encoding (SENSE). However, unlike SENSE, which first estimates the spatial sensitivity maps, PROBER approximates the reconstruction coefficients directly by B-splines. Also, B-spline coefficients are estimated at once in order to minimize the reconstruction error instead of estimating the reconstruction in each pixel independently (as in SENSE). This makes the method robust to noise in reference images. No presmoothing of reference images is necessary. The number of estimated parameters is reduced, which speeds up the estimation process. PROBER was tested on simulated, phantom, and in vivo data. The results are compared with commercial implementations of the algorithms SENSE and GRAPPA (Generalized Autocalibrating Partially Parallel Acquisitions) in terms of elapsed time and reconstruction quality. The experiments showed that PROBER is faster than GRAPPA and SENSE for images wider than 150 x 150 pixels for comparable reconstruction quality. With more basis functions, PROBER outperforms both SENSE and GRAPPA in reconstruction quality at the cost of slightly increased computational time.
Keyword:
parallel imaging
SENSE
B-splines
GRAPPA
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A nonparametric method for automatic correction of intensity nonuniformity in MRI dataMRI数据强度非均匀性自动校正的非参数方法
OAAI
OAAI分享收藏Tailored SMASH image reconstructions for robust in vivo parallel MR imaging量身定制的SMASH图像重建，用于鲁棒的体内并行MR成像OAAI
OAAI分享收藏
查看更多内容