DocumentCode :
1665668
Title :
Super-resolution reconstruction of dynamic MRI by patch learning
Author :
Yanhong Lu ; Ran Yang
Author_Institution :
Sch. of Inf. Sci. & Technol., Sun Yat-Sen Univ., Guangzhou, China
fYear :
2012
Firstpage :
1443
Lastpage :
1448
Abstract :
Achieving both high spatial and temporal resolution is desired in dynamic Magnetic Resonance Imaging (MRI), however, it is difficult to satisfy because of the slow scanning speed of MRI caused by physical and physiological limits. In order to guarantee the temporal resolution, the amount of acquired k-space data is usually reduced. In this paper, a novel method-patch learning-based dynamic MRI super-resolution reconstruction, is proposed, where high resolution dynamic images are reconstructed based on the sampled low frequency k-space data together with a small amount of fully sampled frames as training data. The proposed method is also demonstrated by in-vivo dynamic MRI data.
Keywords :
biomedical MRI; image reconstruction; image resolution; image sampling; learning (artificial intelligence); medical image processing; dynamic magnetic resonance imaging; high resolution dynamic image; method-patch learning-based dynamic MRI superresolution reconstruction; physical limit; physiological limit; sampled low frequency k-space data; scanning speed; spatial resolution; temporal resolution; Acceleration; Correlation; Dynamics; Image reconstruction; Image resolution; Magnetic resonance imaging; Training data; dynamic MRI; patch learning; super-resolution;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Automation Robotics & Vision (ICARCV), 2012 12th International Conference on
Conference_Location :
Guangzhou
Print_ISBN :
978-1-4673-1871-6
Electronic_ISBN :
978-1-4673-1870-9
Type :
conf
DOI :
10.1109/ICARCV.2012.6485389
Filename :
6485389
Link To Document :
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