Title :
Motion-compensated compressed-sensing reconstruction for dynamic MRI
Author :
Sungkwang Mun ; Fowler, James E.
Author_Institution :
Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA
Abstract :
Compressed-sensing reconstruction using motion estimation and compensation for dynamic MRI data is proposed. Reconstruction is driven from a residual in the k-space domain between the current-frame measurements and a corresponding motion-compensated prediction. Due to the periodicity commonly exhibited in dynamic MRI, a telescopic motion search through the entire group of pictures is used to determine the best match for the block-based motion estimation. Experimental comparisons demonstrate improved performance as compared to existing dynamic-MRI reconstructions, both those with and without motion compensation.
Keywords :
biomedical MRI; compressed sensing; image reconstruction; motion compensation; motion estimation; block-based motion estimation; compressed-sensing reconstruction; current-frame measurements; dynamic MRI; k-space domain; magnetic resonance imaging; motion compensation; motion-compensated prediction; telescopic motion search; Approximation methods; Compressed sensing; Dynamics; Heuristic algorithms; Image reconstruction; Magnetic resonance imaging; Transforms; compressed sensing; dynamic MRI;
Conference_Titel :
Image Processing (ICIP), 2013 20th IEEE International Conference on
Conference_Location :
Melbourne, VIC
DOI :
10.1109/ICIP.2013.6738208
