Keywords: Motion Correction, Motion Correction, Navigator, unrolled reconstruction, data consistency

Motivation: Motion is the primary reason for artifacts in MRI.

Goal(s): The proposed method is an attempt at solving motion correction problem for 2D-acquisitions by dropping motion-corrupt sections of k-space to be reconstructed in a fashion similar to under-sampled reconstruction by an unrolled DL framework. 

Approach: The estimation of motion-corrupt shots is proposed using: (a) camera tracking, (b) data relationships between channels, (c) navigator shots. Using these methods to find the dominant pose and outlier shots, reconstruction using an unrolled DL network would fill in for the corrupt k-space shots .

Results: The method shows significant motion correction for T1/T2 FSE/FLAIR sequences.

Impact: The proposed solution has the potential to save tens of thousands of dollars per year per scanner. Breaking up the problem into separate sub-problems can be investigated further, along with the various detection methods mentioned.

Keywords: Motion Correction, Data Acquisition

Motivation: Motion-induced artifacts in pediatric MRI  lead to frequent need for rescan, which increases examination time, costs and patient's discomfort. 

Goal(s): To develop and validate a deep learning-based method for automated assessment of diagnostic image quality, overcoming limitations of existing motion measurement techniques.

Approach: FID navigators embedded into MPRAGE sequence can provide valuable motion information without prolonging scan time. We train a deep neural network on these signals to accurately predict the diagnostic quality of the image that is to be reconstructed. 

Results: Our method surpasses the existing FIDnavΔ approach, achieving AUC of 0.90, with 30% higher specificity and 21% improved precision.

Impact: Our model streamlines MRI procedures by accurately predicting the need for rescans due to patient motion. It has potential to reduce healthcare costs and patient discomfort, and opens new avenues for early scan termination and enhanced clinical workflow efficiency.

Keywords: Motion Correction, Motion Correction

Motivation: Perfusion and angiographic images using arterial spin labeling (ASL) are particularly susceptible to head motion.

Goal(s): To correct motion for Combined Angiography, Structural and Perfusion using Radial Imaging and ASL (CASPRIA) and reduce motion induced aliasing and blurriness for angiographic, perfusion and structural imaging.

Approach: 3D navigators are reconstructed after each ASL preparation using subspace to account for varying contrast. These are used to correct the raw data for motion. A joint label and control reconstruction was formulated to generate motion corrected perfusion and angiographic images.

Results: Motion induced artefacts and blurriness were greatly reduced in angiographic, structural and perfusion data.

Impact: Motion robustness of CASPRIA sequence was improved, facilitating future clinical application with less cooperative patients.

Keywords: Motion Correction, RF Arrays & Systems

Motivation: To investigate the relationship between head motion and scattering for potential future work on motion correction, using radio frequency sensors to detect changes in head pose.

Goal(s): Establish if scattering changes are linearly related to head translations or rotations.

Approach: Simulations were run using the Sim4Life EM FDTD software with a human model at different positions relative to a coil model.

Results: Translations in the x and y directions varied co-linearly with scattering. However, rotations and z-translation display both linear and non-linear relationships.

Impact: These results allow for a greater understanding of the relationship between head motion and scattering, and demonstrates the potential for scattering to be used as a rapid, contact-free head pose tracker.

Keywords: Motion Correction, Motion Correction, Brain, neuroscience, parallel transmit imaging, pTx, b1+ shimming, slice-by-slice, Siemens ICE, Terra, BrainVoyager

Motivation: 7T MRI has the capacity for higher resolution imaging, but is also more sensitive to motion artefacts and B₁⁺ field inhomogeneity. A motion-robust, B₁⁺ homogeneous technique will enable routine 7T usage for motion-sensitive protocols such as in fMRI.

Goal(s): Our goal was to develop integrated real-time motion correction and parallel transmission technique that improves the data quality in 7T fMRI.

Approach: The multislice-to-volume motion correction technique and the capability to execute slice-specific B₁⁺ shims were integrated into an in-house-developed EPI sequence. A cohort was scanned in a task fMRI study.

Results: The combined technique demonstrated reduced motion, increased brain activation and improved tSNR.

Impact: With improved data quality, the integrated real-time motion correction and B₁⁺ shimming parallel transmission technique provides an option for better statistical parametric mapping in neuroscience studies with 7T fMRI, and potentially benefiting other future 7T applications.

Keywords: Motion Correction, Motion Correction, hybrid motion correction

Motivation: Prospective motion correction (PMC) techniques face challenges due to inherent latency and filter-induced delays, particularly impactful in cases of periodic motion, like breathing.

Goal(s): This study examines how periodic motion affects PMC with increased latency and employs retrospective techniques to compensate for latency-induced discrepancies.

Approach: Brain MRI scans were conducted on a volunteer performing exaggerated breathing using a PMC-enabled sequence. Retrospective motion correction was used to reverse latency-induced errors and compensate for residual motion.

Results: The hybrid approach combining PMC and RMC yielded superior results when compared to no correction and PMC-only, emphasizing the importance of addressing latency in motion correction for MRI.

Impact: Breathing motion can lead to suboptimal results in prospective motion correction. Implementing this technique broadens PMC's applicability, aiding its integration into clinical practice.

Keywords: Motion Correction, Motion Correction

Motivation: Motion remains a common source of artifacts in brain imaging.

Goal(s): To facilitate retrospective motion correction across all 2D TSE contrasts.

Approach: A 1-2 sec pre-scan (scout) using a saturation preparation and FLASH readout precedes the TSE imaging scan and is then compared to two additional rapid FLASH k-space encoding lines (guidance lines) inserted after every TSE echo train.  The contrast-matched scout and guidance line data provide fully separable estimation of motion parameters shot-by-shot using a SENSE+motion model.

Results: In vivo, rapid motion trajectory estimation and robust artifact mitigation is demonstrated in T1w, T2w and FLAIR TSE scans with instructed subject motion.

Impact: We present a generalized strategy for retrospective motion correction in 2D TSE brain imaging using an ultra-fast pre-scan and the repeated acquisition of two additional k-space lines. This facilitates rapid motion estimation and robust artifact mitigation across all TSE contrasts.

Keywords: Motion Correction, Brain

Motivation: In cases of motion, multi-echo GRE sequences are susceptible to motion artifacts as well as additional phase errors caused by motion-induced B0 inhomogeneity.

Goal(s): Implement jointly acquired FID and spherical navigators for prospective correction of motion and retrospective correction of field offsets in a multi-echo GRE protocol for multiparametric mapping.

Approach: One volunteer was scanned with/without prospective motion correction. Motion was guided by visual prompt to produce repeatable and clinically relevant trajectories.

Results: Prospective motion correction alone provided a notable improvement in image quality, as well as quantitative fat-fraction, R2*, and susceptibility maps. Additional retrospective correction of field offsets reduced residual artifacts.

Impact: Combining spherical navigators with an additional FID readout is a promising approach to simultaneous motion and B0 correction with multiple applications, including quantitative mapping techniques. 

Keywords: Motion Correction, fMRI (resting state), respiration feedback

Motivation: To implement the respiration-feedback system using the respiration monitor signal available at the MRI console and a solenoid shim coil that has been developed for direct neuronal current mapping.

Goal(s): To compensate for the respiration induced B0 fluctuation in a real-time mode.

Approach: The respiration signal was extracted from the MRI console display using a USB screen capture in real-time. The extracted respiration signal was adjusted and driven to the solenoid shim coil using a custom-built amplifier. Its function was checked by monitoring a resonance frequency at a MRS voxel.

Results: The B0 fluctuation was compensated by the captured respiration signal in real-time.

Impact: The respiration signal which is available at any MRI console can be used for real-time feedback with a USB capturing device to compensate for the respiration induced B0 fluctuation.  This will make the respiration feedback system more easily implementable.

Keywords: Motion Correction, Motion Correction, EEG-fMRI

Motivation: Motion tracking in EEG-fMRI has been a challenging area of research in recent years, with existing approaches frequently suffering from limitations in spatial and temporal resolution or requiring additional hardware or calibration scans.

Goal(s): We aim to introduce a motion-tracking approach by modelling the gradient artifacts induced in EEG recordings during EEG-fMRI studies.

Approach: We introduce an algorithm tailored for detecting rigid and non-rigid head motion in EEG-fMRI data and aim to assess its performance by comparing it with camera-based motion detection techniques.

Results: We have demonstrated the capability of our algorithm to accurately identify motion in EEG-fMRI.

Impact: Our method shows potential across a range of uses, such as enhancing EEG data quality, especially in the context of reducing motion-related artifacts in EEG-fMRI studies.

Keywords: Motion Correction, fMRI

Motivation: 3D-EPI overcomes spatial resolution, spin-history and acceleration limitations of 2D-EPI but is rarely used for functional MRI (fMRI) due to its greater sensitivity to motion.

Goal(s): To examine the performance of our volumetric self-navigated (vSNav) 3D-EPI sequence for fMRI acquisition.

Approach: We acquired fMRI data using both our vSNav 3D-EPI and the standard 2D-EPI sequence during a block design finger tapping experiment both without and with intentional motion to compare the quality of the BOLD signal and the impact of different pre-processing steps.

Results: Although data quality was similar, 3D data were more robust to spatial smoothing.

Impact: A motion-robust 3D-EPI sequence will permit functional MRI with higher spatial and temporal resolutions. However, since the sequence acquires data and performs motion correction in a new way, it requires suitable preprocessing and analyses pipelines.

Keywords: Motion Correction, Motion Correction, Pilot Tone, Neuro, Motion detection, Artifact

Motivation: Motion artifacts are a major cause for rescans or for the patient to be recalled for another scanning session; this results in patient discomfort, workflow disruptions and additional costs.

Goal(s): Prospectively provide MR operators with a “motion sensor signal” indicating subject motion to assist them in detecting and reacting to motion events.

Approach: Design a sequence-agnostic Pilot Tone processing pipeline to detect motion and predict image quality degradation.

Results: We showed that Pilot Tone can be used to detect head movement and predict with high accuracy whether the resulting image will be usable for diagnosis.

Impact: This work shows that Pilot Tone can be used for prospective motion detection and image degradation prediction. Such a method may lead to a practically applicable solution to improve the MR workflow, thus reducing patient burden and costs.

Keywords: Motion Correction, Brain, streaming, motion correction, real-time, image reconstruction, 3D, radial, non-cartesian

Motivation: A real-time motion compensation monitoring system is vital for structural MRI:

• to assess the effectiveness of motion correction even during k-space data collection, 
• to ensure the rapid delivery of corrected images afterwards.

Goal(s): Develop a system that enables real-time:

1. motion-compensation,
2. image reconstruction, 
3. and visualization, 

Approach: Introduced a framework that includes online:

1. coil compression,
2. rigid motion-tracking,
3. motion-compensated gridding, 
4. image generation/streaming.
   

1. no motion,
2. instructed motion. 

Results: Streaming motion-compensated image reconstructions from 3D radial MRI and the progression of image quality improvements were demonstrated in real-time  during k-space data collection.  
 

Impact: Our streaming-MoCo framework enables rapid reconstruction of motion-compensated images both during k-space data collection and post scan. This facilitates immediate assessment of image quality during scans, and potentially trigger corrective action well in advance before the scan concludes. 

Keywords: Motion Correction, Motion Correction, Retrospective Motion Correction, Fat Navigator, Markerless Optical Camera, Brain MRI

Motivation: A comparison between efficacies of navigators and optical camera in retrospective motion correction (MC) on brain MR images remains largely unknown.

Goal(s): Our goal was to compare two motion tracking techniques (fat navigators [FatNav] and markerless optical camera [MoCAP]) and their performance in MC.

Approach: Twenty-one healthy subjects were imaged by T2-weighted turbo-spin-echo sequence with their head movements monitored simultaneously by both techniques. Performance was evaluated by image sharpness calculated at lateral ventricle/white matter boundary.

Results: Compared to MoCAP, FatNav had lower motion scores and less fluctuations at small motion. Images after FatNav-based MC showed greater sharpness than MoCAP.

Impact: The better performance of FatNav-based MC despite low temporal resolution suggest that FatNav can be integrated with MoCAP to achieve robust image quality in the presence of both abrupt and slow head motions.

Keywords: Motion Correction, Data Acquisition

Motivation: Prospective motion correction can correct motion-induced sampling errors in real time but is still sensitive to large motion due to position based non-linear phase differences.

Goal(s): We aim to increase motion robustness of 3D sequences.

Approach: By combining prospective motion correction with radial sampling, non-linear phase errors can be mitigated due to repeated sampling of the k-space center.

Results: We show that prospective motion correction alone is enough for subtle motion but during severe motion (rotations well above ±5°) radial sampling can outperform Cartesian sampling.

Impact: Radial sampling in combination with prospective motion correction allows for imaging during severe motion corruption. This is crucial in imaging e.g. paediatric populations.

Keywords: Motion Correction, Motion Correction, hybrid motion correction

Motivation: Prospective Motion Correction (PMC) demands real-time tracking and may introduce artifacts due to imperfect tracking due to noise. 

Goal(s): To introduce hybridMC, harnessing the strengths of thresholded PMC combined with retrospective motion correction to address motion artifacts comprehensively in neurological MRI.

Approach: Apply a PMC update threshold to avoid incorrect motion correction due to noisy motion tracking. Correct for residual motion artifacts using model-based RMC. 

Results: The hybrid motion correction approach showed superior results when compared to PMC in the presence of noisy tracking, while maintaining good performance for non-noisy tracking.

Impact: To enhance neurological MRI quality, we introduce a hybrid motion correction. This innovation effectively mitigates tracking noise and residual artifacts, offering superior results compared to PMC alone. It promises improved diagnostic accuracy.