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Rigid motion‐corrected magnetic resonance fingerprinting
Author(s) -
Cruz Gastão,
Jaubert Olivier,
Schneider Torben,
Botnar Rene M.,
Prieto Claudia
Publication year - 2019
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.27448
Subject(s) - imaging phantom , artificial intelligence , computer vision , computer science , parametric statistics , motion (physics) , rigid body , ground truth , image quality , motion estimation , mathematics , physics , optics , image (mathematics) , classical mechanics , statistics
Purpose Develop a method for rigid body motion‐corrected magnetic resonance fingerprinting (MRF). Methods MRF has shown some robustness to abrupt motion toward the end of the acquisition. Here, we study the effects of different types of rigid body motion during the acquisition on MRF and propose a novel approach to correct for this motion. The proposed method (MC‐MRF) follows 4 steps: (1) sliding window reconstruction is performed to produce high‐quality auxiliary dynamic images; (2) rotation and translation motion is estimated from the dynamic images by image registration; (3) estimated motion is used to correct acquired k‐space data with corresponding rotations and phase shifts; and (4) motion‐corrected data are reconstructed with low‐rank inversion. MC‐MRF was validated in a standard T 1 /T 2 phantom and 2D in vivo brain acquisitions in 7 healthy subjects. Additionally, the effect of through‐plane motion in 2D MC‐MRF was investigated. Results Simulation results show that motion in MRF can introduce artifacts in T 1 and T 2 maps, depending when it occurs. MC‐MRF improved parametric map quality in all phantom and in vivo experiments with in‐plane motion, comparable to the no‐motion ground truth. Reduced parametric map quality, even after motion correction, was observed for acquisitions with through‐plane motion, particularly for smaller structures in T 2 maps. Conclusion Here, a novel method for motion correction in MRF (MC‐MRF) is proposed, which improves parametric map quality and accuracy in comparison to no‐motion correction approaches. Future work will include validation of 3D MC‐MRF to enable also through‐plane motion correction.