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Robust sliding‐window reconstruction for Accelerating the acquisition of MR fingerprinting
Author(s) -
Cao Xiaozhi,
Liao Congyu,
Wang Zhixing,
Chen Ying,
Ye Huihui,
He Hongjian,
Zhong Jianhui
Publication year - 2017
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.26521
Subject(s) - imaging phantom , sliding window protocol , computer science , artificial intelligence , matching (statistics) , pattern recognition (psychology) , parametric statistics , iterative reconstruction , image quality , computer vision , contrast (vision) , window (computing) , image (mathematics) , mathematics , nuclear medicine , statistics , medicine , operating system
Purpose To develop a method for accelerated and robust MR fingerprinting (MRF) with improved image reconstruction and parameter matching processes. Theory and Methods A sliding‐window (SW) strategy was applied to MRF, in which signal and dictionary matching was conducted between fingerprints consisting of mixed‐contrast image series reconstructed from consecutive data frames segmented by a sliding window, and a precalculated mixed‐contrast dictionary. The effectiveness and performance of this new method, dubbed SW‐MRF, was evaluated in both phantom and in vivo. Error quantifications were conducted on results obtained with various settings of SW reconstruction parameters. Results Compared with the original MRF strategy, the results of both phantom and in vivo experiments demonstrate that the proposed SW‐MRF strategy either provided similar accuracy with reduced acquisition time, or improved accuracy with equal acquisition time. Parametric maps of T 1 , T 2 , and proton density of comparable quality could be achieved with a two‐fold or more reduction in acquisition time. The effect of sliding‐window width on dictionary sensitivity was also estimated. Conclusion The novel SW‐MRF recovers high quality image frames from highly undersampled MRF data, which enables more robust dictionary matching with reduced numbers of data frames. This time efficiency may facilitate MRF applications in time‐critical clinical settings. Magn Reson Med 78:1579–1588, 2017. © 2016 International Society for Magnetic Resonance in Medicine.