Abstract
Purpose: Develop a method for rigid body motion corrected Magnetic Resonance Fingerprinting (MRF).
Methods: MRF has shown some robustness to abrupt motion towards 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 four 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 via image registration; 3) estimated motion is used to correct acquired k-space data with corresponding rotations and phase-shifts; 4) motion corrected data is reconstructed with low rank inversion. MC-MRF was validated in a standard T1/T2 phantom and 2D in-vivo brain acquisitions in seven 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 artefacts in T1 and T2 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 T2 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.
Methods: MRF has shown some robustness to abrupt motion towards 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 four 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 via image registration; 3) estimated motion is used to correct acquired k-space data with corresponding rotations and phase-shifts; 4) motion corrected data is reconstructed with low rank inversion. MC-MRF was validated in a standard T1/T2 phantom and 2D in-vivo brain acquisitions in seven 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 artefacts in T1 and T2 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 T2 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.
| Original language | English |
|---|---|
| Journal | Magnetic Resonance in Medicine |
| Early online date | 3 Sept 2018 |
| DOIs | |
| Publication status | E-pub ahead of print - 3 Sept 2018 |
Keywords
- MR fingerprinting
- low rank
- rigid motion correction