Accelerated Dynamic Magnetic Resonance Imaging Using Learned Representations: A New Frontier in Biomedical Imaging

被引：6

作者：

Christodoulou, Anthony G. ^{[1
,2
,3
,4
,5
]}

Lingala, Sajan Goud ^{[2
,6
,7
,8
]}

机构：

[1] Cedars Sinai Med Ctr, Biomed Imaging Res Inst, Los Angeles, CA 90048 USA

[2] IEEE, Piscataway, NJ 08854 USA

[3] SCMR, Mt Royal, NJ 08061 USA

[4] Int Soc Magnet Resonance Med, Concord, CA 94520 USA

[5] Amer Heart Assoc, Dallas, TX 75231 USA

[6] Univ Iowa, Dept Biomed Engn, Iowa City, IA 52242 USA

[7] Univ Iowa, Dept Radiol, Iowa City, IA 52242 USA

[8] ISMRM, Concord, CA USA

来源：

IEEE SIGNAL PROCESSING MAGAZINE | 2020年 / 37卷 / 01期

关键词：

MRI; RECONSTRUCTION; REGULARIZATION; SPARSITY; MODEL; FRAMEWORK;

D O I：

10.1109/MSP.2019.2942180

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Dynamic magnetic resonance imaging (MRI) can be used to scan a wide range of dynamic processes within the body, including the motion of internal organs, tissue-level nuclear magnetic resonance (NMR) relaxation, and dynamic contrast enhancement (DCE) of dye agents. The ability of MRI to safely provide unique soft-tissue contrast and comprehensive functional information has made dynamic MRI central to a number of imaging exams for cardiac, interventional, vocal tract, cancer, and gastrointestinal applications, among others. Unfortunately, MRI is a notoriously slow imaging modality due to fundamental physical and physiological limitations. These limitations result in tradeoffs between spatial and temporal resolutions, spatial coverage, and the signal-to-noise ratio and have made dynamic MRI a challenging technical goal. © 1991-2012 IEEE.

引用

页码：83 / 93

页数：11

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