Fast Reconstruction of Accelerated Dynamic MRI Using Manifold Kernel Regression

被引：8

作者：

Bhatia, Kanwal K. ^{[1
]}

Caballero, Jose ^{[1
]}

Price, Anthony N. ^{[2
]}

Sun, Ying ^{[3
]}

Hajnal, Jo V. ^{[2
]}

Rueckert, Daniel ^{[1
]}

机构：

[1] Univ London Imperial Coll Sci Technol & Med, Biomed Image Anal Grp, London SW7 2AZ, England

[2] Kings Coll London, Div Imaging Sci & Biomed Engn, London WC2R 2LS, England

[3] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117548, Singapore

来源：

MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION, PT III | 2015年 / 9351卷

基金：

英国工程与自然科学研究理事会;

关键词：

SPARSITY;

D O I：

10.1007/978-3-319-24574-4_61

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We present a novel method for fast reconstruction of dynamic MRI from undersampled k-space data, thus enabling highly accelerated acquisition. The method is based on kernel regression along the manifold structure of the sequence derived directly from k-space data. Unlike compressed sensing techniques which require solving a complex optimisation problem, our reconstruction is fast, taking under 5 seconds for a 30 frame sequence on conventional hardware. We demonstrate our method on 10 retrospectively undersampled cardiac cine MR sequences, showing improved performance over state-of-the-art compressed sensing.

引用

页码：510 / 518

页数：9

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