Keywords: Motion Correction, Motion Correction, Prospective, motion estimation

Motivation: Novel research reduced the acquisition of motion navigators to a few guidance lines. A prospective correction is not yet possible due to reconstruction and optimization times.

Goal(s): We determine the feasibility of a fast AI-based motion estimation for prospective correction in a 3D MPRAGE research sequence.

Approach: A DL network to prospectively estimate the head pose from seamlessly integrated guidance lines in a 3D MPRAGE research sequence was trained on simulated data and used for a rapid adaption of the FOV to achieve a prospective correction.

Results: In-vivo experiments showed greatly reduced motion artifacts. The motion estimation is accurate and stable.

Impact: Prospectively adapting the FOV using the proposed AI-based method greatly improves the image quality of 3D MPRAGE acquisitions. This unique application of ML enables promising research of prospectively mitigating motion artifacts with minimal changes to the sequence.

Keywords: Motion Correction, Motion Correction, pilot tone

Motivation: Model-based retrospective motion correction has shown good results but sometimes lacks enough information to accurately derive motion parameters.

Goal(s): To use the pilot tone to address the drawbacks of retrospective methods by providing them with high-frequency motion information for more robust and efficient motion correction.

Approach: We use pilot tone to refine and increase the temporal resolution of motion parameters for the Scout Accelerated Motion Estimation and Reduction (SAMER) method.

Results: Motion phantom and volunteer tests show improved image quality of pilot tone + SAMER compared to SAMER-only while the reconstruction takes clinically acceptable 20s.

Impact: Our results demonstrate that pilot tone can be used to improve the precision and temporal resolution of a model-based retrospective motion correction method, while being robust and fast. This will help to further mitigate motion artifacts in clinical routine.

Keywords: Motion Correction, Motion Correction

Motivation: Inserting navigators into short-TR gradient echo pulse sequences increases scan duration as the TR needs to be extended to maintain a steady-state.

Goal(s): Periodically interleave navigators without disrupting stead-state.

Approach: A packed pulse sequence encoding was implemented that divides the phase encoding table into partitions. Each partition begins with two blank-TRs that contain identical RF and spoiling to the parent sequence. The first blank-TR readout is replaced with a navigator acquisition. The second is replaced with computation time to perform a field of view update.

Results: The packed sequences reduced the time penalty from 1-minute to 3-13 seconds, without impacting motion correction efficacy.

Impact: The packed sequence structure allows researchers to augment the acquisition of short-TR GRE sequences with fast (<TR) navigators for almost no additional scan time without affecting motion correction efficacy, and is applicable to all sequences where the TR is minimised.

Keywords: Alzheimer's Disease, MR Value

Motivation: Rising medical imaging utilization and increasing use of automated support systems demand high-quality, fast, and reproducible/robust MRI techniques. Despite rapid scanning afforded by deep learning, motion remains a common source of artifacts.

Goal(s): Integrate retrospective motion correction into a deep learning reconstruction to facilitate high-quality, fast, and motion-robust brain imaging.

Approach: Scout and guidance line-based motion correction was implemented into MPRAGE, SPACE and SWI to enable rapid motion trajectory estimation. A data-consistency driven neural network reconstruction was adapted to perform network regularized motion correction.

Results: Improved SNR and reduced motion artifacts are demonstrated in vivo using 4-6-fold accelerated scans with instructed subject motion.

Impact: Retrospective motion correction was integrated into a deep learning reconstruction to facilitate fast and motion-robust 3D brain imaging across T1, T2, T2 FLAIR and T2*/SWI. This should add clinical value to routine brain exams and emerging neuro-degenerative screening protocols (ARIA).

Keywords: Motion Correction, Motion Correction, Pilot Tone

Motivation: Robust motion correction relies on sequence modifications, either adding navigators or re-ordering the k-space sampling. These modifications might not be possible for every sequence.

Goal(s): To leverage motion-sensitive Pilot Tone (PT) signals to guide motion correction for any standard 3D acquisition.

Approach: We propose the ERIC calibration protocol, which distributes short self-navigated (DISORDER) acquisitions across the whole examination. Combined with data-driven motion correction reconstructions, we can achieve robust PT calibration.

Results: We show the potential to correct standard MPRAGE acquisitions with a linear phase encoding scheme in 4 healthy volunteers (HV) even when using 54 seconds worth of calibration data.

Impact: Correcting motion in any 3D acquisition is an unsolved problem. Combining pre-calibrated PT signals with data-driven optimizations explores a promising avenue. To this end, building a robust calibration model by acquiring ~1min worth of data would easily integrate into examinations.

Keywords: Motion Correction, Quantitative Imaging, Motion Correction, Deep Learning, Brain

Motivation: T2* quantification from GRE-MRI is particularly impacted by subject motion due to its sensitivity to magnetic field inhomogeneities. The current multi-parametric quantitative BOLD motion correction method depends on additional k-space acquisition, extending overall acquisition times.

Goal(s): To develop a learning-based motion correction method tailored to T2* quantification that avoids redundant data acquisition.

Approach: PHIMO leverages multi-echo T2* decay information to identify motion-corrupted k-space lines and excludes them from a data-consistent deep learning reconstruction.

Results: We are able to correct motion artifacts in subjects with stronger motion, approaching the performance of the current motion correction method, while substantially reducing the acquisition time.

Impact: PHIMO reduces strong motion artifacts in T2* maps by utilizing T2* decay information in an unrolled DL reconstruction. PHIMO avoids redundant data acquisition compared to a current correction method and reduces the acquisition time by over 40%, facilitating clinical applicability.

Keywords: Motion Correction, Motion Correction

Motivation: Motion correction in MRF using navigators in sequence deadtime improves imaging robustness, however, temporal resolution of the approaches is limited to 6-10 s.

Goal(s): We aim to achieve accurate motion tracking at 0.5 s temporal resolution, by integrating the QUantitatively-Enhanced parameter Estimation from Navigators (QUEEN) approach into MRF. 

Approach: Compact navigators were inserted throughout the MRF acquisition at a minimal encoding efficiency reduction of ~5%.  The acquisition of the quantitative scout was integrated into the MRF’s dummy scan, resulting in no added scantime.

Results: The estimated in vivo motion parameters have MAE of 0.4 mm and 0.2 deg compared to image registration estimates.  

Impact: The proposed method can achieve high temporal resolution motion estimates, and therefore, is a promising approach for high-precision motion correction in MRF.

Keywords: Motion Correction, Motion Correction, Real-time shimming, Multicoil shim array, Metabolic Imaging

Motivation: Very high quality of MR spectroscopic imaging (MRSI) data is needed for robust and reproducible metabolite quantification. This critically depends on the B₀ shimming and scan stability. Integrated RF-receive/B₀-shim arrays significantly improve spectral quality.

Goal(s): Real-time motion correction and multicoil shimming update with an integrated RF-receive/B₀-shim array for robust whole-brain MRSI.

Approach: We developed a rapid navigator for head tracking and B0 fieldmapping in combination with rapid processing for real-time update of multicoil shim currents and MRSI localization.

Results: Real-time motion correction and multicoil shimming provides significantly narrower linewidth, higher signal-to-noise, reduced quantification errors and reproducible metabolic imaging.

Impact: Whole-brain MRSI is a unique method for non-invasive mapping of brain neurochemistry, and in combination with real-time motion correction and multicoil shim array update provides robust and reproducible quantitative metabolic imaging for clinical use.

Keywords: Motion Correction, Motion Correction, MR Fingerprinting, qMT, low rank

Motivation: Motion-induced artifacts are a significant barrier to achieving clinically acceptable image quality for multi-compartment quantitative MRI techniques, e.g. a 2-pool magnetization transfer model.

Goal(s): To develop a self-navigating approach to estimating motion parameters in an MR-fingerprinting-like acquisition.

Approach:  We optimize a subspace that maximizes the contrast-to-noise ratio between brain parenchyma and cerebrospinal fluid for a low-resolution, time-segmented low-rank reconstruction used to estimate motion.

Results: Compared to the typical SVD basis, the contrast-optimized basis improves the smoothness of the motion estimates and the apparent resolution of the reconstructed coefficient images and quantitative maps. 

Impact: The proposed retrospective, self-navigating motion correction technique does not require any sequence modifications and/or additional scan time. It can therefore be applied to many quantitative MRI techniques where the signal's variation over time can be well-described in a low-rank subspace.

Keywords: Quantitative Imaging, Neuro

Motivation: While 3D multiparametric mapping acquisitions can provide rich and quantitative information, their long acquisition time renders them susceptible to motion.

Goal(s): To develop a rapid, multiparametric technique for motion-robust brain mapping.

Approach: 3D-QALAS acquisition with Cartesian variable-density sampling was implemented to achieve self-navigation while maintaining high scan efficiency. Brain position was estimated for each TR and incorporated in the reconstruction’s forward model.

Results: The proposed method enabled reconstruction of motion-resolved datasets at a time resolution of 4.5s with tracking accuracy of <0.2 degrees and <0.5mm, providing T1 and T2 maps with significantly reduced artifacts and improved agreement with measurements from motion-free scans.

Impact: We propose an efficient, whole-brain quantitative scan at 1mm³ resolution in 3:36min and incorporate self-navigated motion-correction, thereby obviating the need for navigators or external hardware. This benefits clinical translation especially for imaging unsedated children in clinical and research settings.