To improve the robustness of clinical brain MRI with respect to patient motion.

Numerous methods to detect and correct for motion in MRI have been explored, including the use of motion detection markers or devices¹, and MR navigators^2–4. The use of motion detection markers in clinical settings has been limited due to the difficulty of robustly attaching them to patients, and the use of MR navigator scans presents a challenge due to low sensitivity and disruption of the optimal timing of the MR pulse sequence into which they are inserted. There have also been attempts to determine non-rigid motion in a data driven manner through the use of receive coil locality and auto-focusing motion metrics⁵. We present TArgeted Motion Estimation and Reduction (TAMER), which retrospectively estimates and corrects for rigid-body motion by jointly solving for image and motion parameters. TAMER does not require prior motion information, but the efficiency of the method can be enhanced with the inclusion of noisy or incomplete navigator or detector measurements. TAMER uses a targeted application of the underlying parallel imaging data consistency model for motion estimation and correction through model reduction and fast optimization techniques.

To demonstrate the benefits of TAMER, we incorporate translation-based motion into the SENSE⁶ model. Although our initial investigation is limited to translations, we hope to generalize the method to full rigid-body motion. For the 2D RARE(TSE,FSE) clinical T2 images we analyze here, each shot acquires “ETL” kspace lines. Ideally we need to determine 6 parameters for each shot, but here we limit our estimation to two in-plane translation parameters. See Fig. 1 for an overview of the method. We initialize TAMER with a conventional reconstruction of the corrupted kspace data ignoring motion, i.e. $$$x_0=\textrm{arg}\min_x\sum_{i=1}^{N}||M_0UFC_ix-k_i||_2$$$ . From this corrupted image, pixels of interest (typically ~5% of image) are selected by identifying high signal regions or by adding small random motion to highlight motion susceptible pixels. The joint optimization is restricted to these key image-space areas in order to remove degeneracy in the solution space and ensure final solution quality. For each iteration of TAMER, a greedy search sequentially determines an estimate of the motion parameters at each shot. The solution of this greedy search is used to jumpstart a global optimization for all motion parameters. Note that at each evaluation in both the greedy and global search, we only consider data consistency across the small targeted subset of pixels. The final motion parameters are used to reconstruct the corruption-free object $$$x_{final}=\textrm{arg}\min_x\sum_{i=1}^{N}||M_{final}UFC_ix-k_i||_2$$$.

In order to highlight the performance of our approach, we consider both simulated data, where we apply TAMER to a motion-free image that has been retrospectively corrupted by simulated motion, as well as experimental data from an anthropomorphic head phantom translated during acquisition by a motion actuator. In both cases we can compare directly to a ground truth (motion-free) image. For the first approach, the motion corrupted kspace data was simulated by adding translation appropriate phase to kspace shots of a T2-weighted 2D TSE acquisition from a healthy volunteer on a 3T Siemens Trio with ETL=8, 224x224mm² FOV, 1x1x3mm³ resolution, 32-channel, TR=6.1s, TE=98ms, refocus angle=150° and R=1,2. The motion-free image was corrupted using the measured translation parameters taken from an Alzheimer's disease patient’s fMRI study. TAMER was also tested on an anthropomorphic phantom using ETL=11, 220x220mm² FOV, and resolution 0.9x0.9x3mm³. To create motion corrupted data the phantom was placed on a translational stage and moved in the A-P direction intermittently throughout the scan. Due to the uniformity of the brain phantom, 17% of the pixels were targeted for the data consistency optimization.

Fig. 2 shows the reduction of motion induced ringing artifacts (compared to ground truth) when TAMER is applied to the volunteer scan, as well as the close agreement between the estimated motion parameters (solid) and the ground truth simulated motion (dotted). Fig. 3 highlights the performance of TAMER applied to the motion corrupted phantom data, also using no prior motion information. The approximate 1D motion path is validated through close agreement of the motion trajectories for two imaging slices.We have demonstrated the effectiveness of TAMER for correcting translational motion in simulated accelerated data and brain phantom data corrupted by a motion actuator. The method is able to efficiently and accurately estimate motion trajectories through the use of targeted parallel imaging and fast greedy search. With the extension to a full 3D approach and modeling terms for rotational motion, TAMER should facilitate retrospective motion correction in clinical settings.