迭代重建
人工智能
压缩传感
非线性降维
参数统计
核(代数)
成像体模
非线性系统
歧管(流体力学)
计算机科学
模式识别(心理学)
计算机视觉
稀疏逼近
特征向量
数学
算法
降维
组合数学
统计
物理
放射科
工程类
机械工程
医学
量子力学
作者
Yihang Zhou,Haifeng Wang,Yuanyuan Liu,Dong Liang,Leslie Ying
出处
期刊:IEEE Transactions on Biomedical Engineering
[Institute of Electrical and Electronics Engineers]
日期:2022-03-15
卷期号:69 (10): 2996-3007
标识
DOI:10.1109/tbme.2022.3158904
摘要
In this study, we present a novel method to reconstruct the MR parametric maps from highly undersampled k-space data. Specifically, we utilize a nonlinear model to sparsely represent the unknown MR parameter-weighted images in high-dimensional feature space. Each image at a specific time point is assumed to belong to a low-dimensional manifold which is learned from training images created based on the parametric model. The final reconstruction is carried out by venturing the sparse representation of the images in the feature space back to the input space, using the pre-imaging technique. Particularly, among an infinite number of solutions that satisfy the data consistency, the one that is closest to the manifold is selected as the desired solution. The underlying optimization problem is solved using kernel trick, sparse coding, and split Bregman iteration algorithm. In addition, both spatial and temporal regularizations are utilized to further improve the reconstruction quality. The proposed method is validated on both phantom and in vivo human brain T2 mapping data. Results suggest that the proposed method is superior to the conventional linear model-based reconstruction methods, in terms of artifact removal and quantitative estimation accuracy. The proposed method could be potentially beneficial for quantitative MR applications.
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