当前位置: X-MOL 学术IEEE Signal Proc. Mag. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Optimization Methods for Magnetic Resonance Image Reconstruction: Key Models and Optimization Algorithms
IEEE Signal Processing Magazine ( IF 14.9 ) Pub Date : 2020-01-01 , DOI: 10.1109/msp.2019.2943645
Jeffrey A Fessler 1
Affiliation  

The development of compressed-sensing (CS) methods for magnetic resonance (MR) image reconstruction led to an explosion of research on models and optimization algorithms for MR imaging (MRI). Roughly 10 years after such methods first appeared in the MRI literature, the U.S. Food and Drug Administration (FDA) approved certain CS methods for commercial use, making CS a clinical success story for MRI. This article reports on several key models and optimization algorithms for MR image reconstruction. Included are both methods that the FDA has approved for clinical use and more recent methods being considered in the research community that use data-adaptive regularizers. It presents in a single survey the many algorithms devised to exploit the structure of the system model and regularizers used in MRI.

中文翻译:

磁共振图像重建的优化方法:关键模型和优化算法

用于磁共振 (MR) 图像重建的压缩传感 (CS) 方法的发展导致了 MR 成像 (MRI) 模型和优化算法研究的爆炸式增长。在此类方法首次出现在 MRI 文献中大约 10 年后,美国食品和药物管理局 (FDA) 批准了某些 CS 方法用于商业用途,使 CS 成为 MRI 的临床成功案例。本文报告了 MR 图像重建的几个关键模型和优化算法。包括 FDA 批准用于临床的方法和研究界正在考虑的使用数据自适应正则化器的最新方法。它在一次调查中介绍了为利用 MRI 中使用的系统模型和正则化器的结构而设计的许多算法。
更新日期:2020-01-01
down
wechat
bug