Rigid motion-corrected magnetic resonance fingerprinting.

Gastão Cruz,Olivier Jaubert,Claudia Prieto,Rene M Botnar,Torben Schneider

doi:10.1002/mrm.27448

Gastão Cruz, Olivier Jaubert + Show 3 more

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https://doi.org/10.1002/mrm.27448

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Abstract

PurposeDevelop a method for rigid body motion‐corrected magnetic resonance fingerprinting (MRF).MethodsMRF 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 T1/T2 phantom and 2D in vivo brain acquisitions in 7 healthy subjects. Additionally, the effect of through‐plane motion in 2D MC‐MRF was investigated.ResultsSimulation results show that motion in MRF can introduce artifacts 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.ConclusionHere, 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.

Highlights

Magnetic Resonance Fingerprinting (MRF) is a novel relaxometry approach based on continuous sampling of the transient steady state magnetization evolution [1]
T1, T2 and M0 maps from MRF reconstruction without motion correction, with image based motion correction (IBMC) and with the proposed motion corrected MRF (MC-MRF) are shown in Figures 2, 3 and 4 for the three different types of rigid motion simulated, respectively: 1) 2D abrupt motion at time-point #250; 2) 2D abrupt motion at time-point
In the second case (Figure 3), abrupt motion occurs near high encoding of T2 and has corresponding effects in T2

Summary

Introduction

Magnetic Resonance Fingerprinting (MRF) is a novel relaxometry approach based on continuous sampling of the transient steady state magnetization evolution [1]. In MRF, sequence parameters, predominantly flip angle (FA) and repetition time (TR), are varied to explore different magnetization states. Undersampled trajectories are employed to sample each combination of sequence parameters (time-points) at high temporal resolution. Under these conditions, each unique tissue parameter (e.g. T1/T2) combination is expected to produce a unique signal evolution (fingerprint) that can be simulated using Bloch equations or Extended Phase Graphs [2, 3]. The set of simulated signal evolutions (dictionary) can be matched to the measured fingerprints to simultaneously determine tissue parameters like T1, T2 and M0. Incoherent spatial and temporal aliasing of the sampled magnetization time-points (due to undersampling) is typically achieved with non-Cartesian trajectories to minimize potential bias in dictionary matching step

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Discussion

Conclusion