Keywords: Motion Correction, Breast, MRI Hardware, CPU-GPU

Motivation: MRI motion-compensated reconstruction programs rely on several computationally intensive algorithms. For clinical use, they need to use efficiently the computational resources of compute nodes to achieve a good performance.

Goal(s): Evaluate the performance of a program across architectures and optimize its execution without any code modification.

Approach: We investigate the different parallelization paradigms. We use the roofline model, and the performance profiling tools to derive the architectural efficiency on CPU. A Matlab GPU implementation of the reconstruction kernel is used to draw comparisons.

Results: The optimal parallel mapping reduces considerably the reconstruction time. Moreover, GPUs don’t outperform CPUs in every reconstruction problem.

Impact: We demonstrate the importance of micro-architectural study in the overall behavior of MRI motion-compensated reconstruction codes. This study provides insights about how to select the most suitable architecture to a given code, depending on its hardware limitations.

Keywords: Motion Correction, Radiotherapy

Motivation: Real-time 4D MRI on the MR-Linac is still sensitive to respiratory motion baseline drifts.

Goal(s): To further develop MRSIGMA for real-time adaptation to out-of-range anatomical changes during signature matching.

Approach: The motion dictionary was continuously updated using a sliding window of stack-of-stars data and fast motion-resolved Movienet reconstruction. MRSIGMA with adaptive motion learning was implemented in real-time using an external computer connected to the MR-Linac and tested on a programmable 4D phantom and a patient with pancreatic cancer.

Results: The adaptive motion learning approach was able to update the dictionary after 2 sliding window periods, which improved robustness of real-time volumetric motion tracking.

Impact: MRSIGMA with adaptive learning would enable real-time volumetric motion tracking robust to respiratory motion baseline drifts and other anatomical changes on the MR-Linac for improved monitoring and adaptation of radiotherapy of tumors affected by respiratory motion.

Keywords: Motion Correction, Motion Correction

Motivation: Motion navigation with improved data efficiency and robustness is still needed for free-breathing MRI. 

Goal(s): To develop VAR-NAV, a new 2D auto-navigation technique with 100% data efficiency for stack-of-stars acquisitions. 

Approach: VAR-NAV directly estimates the position of the liver dome using 2D projection images and AM-FM demodulation. Results can be corrected retrospectively, like in soccer video assistant referee (VAR). VAR-NAV is used to sort continuously acquired data and reconstruct motion-resolved 4D images. 

Results: VAR-NAV outperforms conventional 1D PCA-based navigation and does not require additional acquisition as in previous 2D navigation techniques, offering an efficient and accurate method for clinical 4D MRI.

Impact: VAR-NAV estimates a motion signal that represents displacements on 2D images, outperforming conventional PCA-based 1D navigation. No extra navigation data are required, and results can be retrospectively analyzed like in soccer video-assisted-referee (VAR). VAR-NAV promises to improve clinical 4D MRI.

Keywords: Motion Correction, Cardiovascular, Pilot Tone, Cardiac Trigger

Motivation: This research aims to refine Pilot Tone (PT) cardiac triggering, which can be impacted by changes in signal amplitude caused by breath-holding.

Goal(s): Its primary goal is to develop and validate a real-time correction algorithm that improves the PT cardiac trigger stability during breath-holds without introducing additional variability during free breathing.

Approach: The algorithm analyzes PT data for respiratory patterns to adapt beat-by-beat amplitude correction of the cardiac signal. The methodology was tested with different breathing paradigms at 1.5T and 3T to assess robustness.

Results: The correction successfully adjusted PT cardiac signal amplitude, reducing missed triggers across respiratory patterns without increasing triggering variation.

Impact: The described real-time correction improves the reliability of cardiac PT triggers during inspiratory breath-hold maneuvers, increasing the opportunities to replace ECG triggering with PT triggering, and therefore to eliminate the need for ECG leads.

Keywords: Motion Correction, Motion Correction, PROPELLER, free breathing, registration, body MRI

Motivation: The motion correction in PROPELLER is typically limited to rigid body motion and only the averaging effect is exploited for motion artifact reduction for abdominal scans.

Goal(s): Our goal was to reduce motion artifacts and improve image quality in abdominal MRI.

Approach: We proposed an AI framework for reconstructing high-resolution motion-free image from a free-breathing abdominal PROPELLER scan. The AI model was trained on synthetic data and tested on abdominal T2W TSE scans.

Results: The proposed AI reconstruction outperforms the conventional PROPELLER reconstruction and PROPELLER with non-rigid motion correction in terms of residual motion artifacts and general image quality.

Impact: The proposed AI-based reconstruction allows obtaining motion-free images with high-spatial resolution from PROPELLER MRI scans, which facilitates abnormality detection.

Keywords: Motion Correction, Motion Correction

Motivation: Motion correction algorithms based on Image-based co-registration of retrospectively ordered motion states have limited effectiveness for highly accelerated scans.

Goal(s): To develop a robust motion correction algorithm for highly accelerated dynamic MRI scans.

Approach: We developed an approach that jointly, and data-consistently, estimates motion-corrected images and motion fields.

Results: Simulated and experimental results demonstrate that the proposed approach yields improved motion-corrected images at high acceleration factors during dynamic MRI scans.

Impact: The proposed approach could remove previously reported limitations on the use of retrospectively re-ordered dynamic MRI scans.

Keywords: Motion Correction, Motion Correction

Motivation: 3D high-SNR cardiac MRI may be achieved by acquiring undersampled images with low SNR during multiple heartbeats and averaging these volumes after motion correction. 

Goal(s): To generate 3D high SNR cardiac MRI from free-breathing multi-heartbeat undersampled acquisitions.

Approach: We proposed an algorithm that implemented deep-learning based undersampled image reconstruction and deformable motion correction to generate high SNR 3D MRI.

Results: For 11 subjects, our approach demonstrated effectiveness in respiratory and cardiac motion correction and generated 3D MR images with SNR 1.7x higher than single heartbeat acquisition and 1.4x higher than that without motion correction.

Impact: The proposed approach provides a way for respiratory and cardiac motion correction and enables 3D MRI with high SNR that is required for a wide range of clinical applications.

Keywords: Motion Correction, Liver, Perfusion

Motivation: The requirements for multiple breath-holds would cause motion artifacts, which may impair the accurate evaluation of lesion properties in dynamic perfusion scans. 

Goal(s): To evaluate the clinical feasibility of free-breathing dynamic liver MR perfusion(FBP) compared with breath-holding dynamic perfusion(BHP) method.

Approach: A case-control study of 58 oncology patients were collected. The image quality, intervolume motion effect and diagnostic confidence were compared between the two groups.

Results: Four-dimensional free-breathing dynamic liver MR perfusion (4D-FBP) could provide comparable image quality but significantly fewer motions than routine method, making 4D-FBP an attractive alternative to existing breath-holding techniques in clinical dynamic liver MR scans.

Impact: Free-breathing dynamic liver MR perfusion provides significantly improved intervolume motion and comparable image quality in comparison to the routine breath-holding method, which may promote the clinical translation of liver quantitative perfusion.

Keywords: Motion Correction, Motion Correction, PROPELLER

Motivation: PROPELLER MRI has been widely used to mitigate patient motion; however, conventional approach is not very robust, and does not address inter-slice misalignment that tends to occur on few moving patients. 

Goal(s): Develop new robust inter-blade and inter-slice motion correction technique.

Approach: We propose a novel inter-blade and inter-slice motion correction reconstruction technique for PROPELLER MRI using volumetric calibration scan as reference. 

Results: The results show large improvement brought by the proposed method compared to convention Motion Correction on both in-plane motion artifact mitigation and inter-slice alignment.

Impact: Reducing repeat scans due to patient motion, hence, increase patient throughput.
Enable scanning patient who cannot keep still in the scanner, such as pediatric patients.

Keywords: Motion Correction, Motion Correction, multi-parametric mapping

Motivation:  Motion correction (MoCo) for cardiac parametric mapping can be challenging due to the dynamic signal variations. Traditional model-based methods need an analytical model, which is often unavailable for multi-parametric mapping applications.

Goal(s): To propose a model-free dictionary matching-based MoCo method for cardiac multi-parametric mapping.

Approach: The method alternates between dictionary matching and image registration. In vivo validation was performed in 10 healthy subjects for cardiac joint T1 and T2 mapping with controlled breathing.

Results: Compared with non-MoCo, the proposed method significantly reduced inter-image misalignment and improved the quality of the T1 and T2 maps.

Impact: The proposed MoCo method can be applied to any quantitative MRI application with a signal dictionary, which includes both single-parametric and multi-parametric mapping.

Keywords: Motion Correction, Motion Correction, Torso, Optical Tracking, Upright

Motivation: The study focuses on the development of a MoCo technique for torso MRI in upright scanner.

Goal(s): Respiratory gated MRI acquisition was achieved by tracking respiratory-related torso motion under the rigid-body assumption.

Approach: Optical tracking of torso motion was used to trigger the MRI acquisition at the beginning of each exhalation period.

Results: Respiratory gating reduces motion induced artefacts in MRI of seated subjects, but was not able to eliminate motion effects in MRI of standing subjects. In its future development it would be paired with retrospective motion correction technique

Impact: The aim is to improve MR image quality on an 0.5T upright scanner where body motion is mostly unconstrained. It would particularly improve image quality in patients who are not supine, so increasing  patient comfort and reducing the scanning time.

Keywords: Motion Correction, Motion Correction

Motivation: Navigator-based rigid-body motion correction (MoCo) can improve IQ when the correct motion parameters are extracted from navigator data. However, navigator-based MoCo can reduce IQ when incorrect motion parameters are derived. 

Goal(s): We propose combining IQ assessment with navigator-based MoCo to improve correction robustness.

Approach: The proposed solution was implemented by calculating the entropy (IQ metric) of images with/without navigator-based rigid-body MoCo, and MoCo was applied only when the image entropy decreased. 

Results: During volunteer studies, the proposed solution reduced the number of slices that showed IQ reduction due to rigid-body correction, while maintaining a large majority of slices that benefited from rigid-body correction.

Impact: Navigator-based rigid-body motion correction (MoCo) robustness was improved by using an image quality metric to determine whether MoCo improved IQ.

Keywords: Motion Correction, Motion Correction

Motivation: Motion correction (MOCO) is critical for free-breathing cardiac DTI, but it is challenging due to low SNR and variations in b-value contrast in acquired images.

Goal(s): To compare MOCO schemes for cardiac DTI with the ultimate goal of implementing the entire post-processing workflow inline on the scanner. 

Approach: Three image registration-based MOCO schemes were compared with a previous low rank tensor (LRT) method in 7 volunteers. 

Results: Compared with the LRT method, proposed MOCO methods achieved smaller standard deviations of MD and FA values, more smoothly varying helical structure in HA maps, and visually less motion of the heart in diffusion weighted images. 

Impact: A robust and fast motion correction approach for free-breathing cardiac diffusion tensor imaging (DTI) improves the quality of diffusion maps, and facilitates clinical translation of cardiac DTI.

Keywords: Motion Correction, Motion Correction

Motivation: Respiratory monitoring is critical for robust free-breathing MRI and various methods have been developed.

Goal(s): To leverage sliding-window MRF to evaluate the accuracy of respiratory motion assessment obtained from the Pilot Tone (PT) navigator and BioMatrix Sensor and apply to free-breathing abdominal MRF. 

Approach: Sliding-window MRF was used to measure respiratory motion with a temporal resolution of 0.5 sec to compare with the respiratory motions acquired using PT navigator and BioMatrix Sensor during in vivo scans.

Results: PT navigator presents slight improvement in monitoring respiratory motion, but MRF maps reconstructed based on the motion waveforms from all these approaches are consistent in quality.

Impact: The knowledge obtained in this study will help design free-breathing abdominal imaging in general and provide critical information in performing quantitative abdominal MRI using MRF.

Keywords: Motion Correction, Motion Correction

Motivation: The quality of susceptibility weighted images may deteriorate under subject motion, especially at high fields, impairing further data analysis.

Goal(s): To mitigate motion artifacts through prospective motion correction(PMC) by employing 3D orbital navigators and real-time processing of a linear perturbation model.

Approach: Orbital navigators were integrated into a high-resolution 3D EPI sequence for rapid susceptibility-weighted imaging at 7T, and a real-time motion estimation pipeline was established. The impact of prospective correction for large instructed motion on the resulting images was investigated.

Results: Prospective motion correction successfully preserved the image quality of susceptibility weighted images, yielding results comparable to a scan without instructed motion.

Impact: The improvements in image quality of the susceptibility weighted images underline the high potential of a linear perturbation model with Orbital Navigators for precise and rapid prospective motion correction in ultra-high field gradient echo imaging and potentially many more applications.

Keywords: Motion Correction, Vessels

Motivation: The multi-contrast intracranial vascular imaging sequence iSNAP is less sensitive to motion due to the use of 3D radial readout, but image blurring and thus decreased visibility of distal small vessels may still occur in the case of large head motion.

Goal(s): To evaluate the feasibility of estimating the continuous head motion from iSNAP k-space data under different temporal resolutions.

Approach: Subsets of iSNAP's 3D radial k-space data were used to reconstruct a series of low-resolution images.Then the SPM toolbox was used to perform rigid registration,between the image volumes.

Results: The temporal profiles of the motion parameters are well estimated.

Impact: The results indicate the head motion can be well estimated from iSNAP. The estimated motion parameters can be used to correct the k-space data in the future, thus allowing reconstruction of motion-free iSNAP images.