Using the axis of rotation of polar navigator echoes to rapidly measure 3D rigid-body motion

Motion and temporal B0-shift corrections for QSM and R 2 * mapping using dual-echo spiral navigators and conjugate-phase reconstruction

PURPOSE

To develop an efficient navigator-based motion and temporal B0-shift correction technique for 3D multi-echo gradient-echo (ME-GRE) MRI for quantitative susceptibility mapping (QSM) andR 2 * $$ {\mathrm{R}}_2^{\ast } $$ mapping.

THEORY AND METHODS

A dual-echo 3D stack-of-spiral navigator was designed to interleave with the Cartesian multi-echo gradient-echo acquisitions, allowing the acquisition of both low-echo and high-echo time signals. We additionally designed a novel conjugate phase-based reconstruction method for the joint correction of motion and temporal B0 shifts. We performed numerical simulation, phantom scans, and in vivo human scans to assess the performance of the methods.

RESULTS

Numerical simulation and human brain scans demonstrated that the proposed technique successfully corrected artifacts induced by both head motions and temporal B0 changes. Efficient B0-change correction with conjugate-phase reconstruction can be performed on fewer than 10 clustered k-space segments. In vivo scans showed that combining temporal B0 correction with motion correction further reduced artifacts and improved image quality in bothR 2 * $$ {\mathrm{R}}_2^{\ast } $$ and QSM images.

CONCLUSION

Our proposed approach of using 3D spiral navigators and a novel conjugate-phase reconstruction method can improve susceptibility-related measurements using MR.

Quantifying MR Head Motion in the Rhineland Study - A Robust Method for Population Cohorts

Head motion during MR acquisition reduces image quality and has been shown to bias neuromorphometric analysis. The quantification of head motion, therefore, has both neuroscientific as well as clinical applications, for example, to control for motion in statistical analyses of brain morphology, or as a variable of interest in neurological studies. The accuracy of markerless optical head tracking, however, is largely unexplored. Furthermore, no quantitative analysis of head motion in a general, mostly healthy population cohort exists thus far. In this work, we present a robust registration method for the alignment of depth camera data that sensitively estimates even small head movements of compliant participants. Our method outperforms the vendor-supplied method in three validation experiments: 1. similarity to fMRI motion traces as a low-frequency reference, 2. recovery of the independently acquired breathing signal as a high-frequency reference, and 3. correlation with image-based quality metrics in structural T1-weighted MRI. In addition to the core algorithm, we establish an analysis pipeline that computes average motion scores per time interval or per sequence for inclusion in downstream analyses. We apply the pipeline in the Rhineland Study, a large population cohort study, where we replicate age and body mass index (BMI) as motion correlates and show that head motion significantly increases over the duration of the scan session. We observe weak, yet significant interactions between this within-session increase and age, BMI, and sex. High correlations between fMRI and camera-based motion scores of proceeding sequences further suggest that fMRI motion estimates can be used as a surrogate score in the absence of better measures to control for motion in statistical analyses.

Suppressing motion artefacts in MRI using an Inception-ResNet network with motion simulation augmentation

The suppression of motion artefacts from MR images is a challenging task. The purpose of this paper was to develop a standalone novel technique to suppress motion artefacts in MR images using a data-driven deep learning approach. A simulation framework was developed to generate motion-corrupted images from motion-free images using randomly generated motion profiles. An Inception-ResNet deep learning network architecture was used as the encoder and was augmented with a stack of convolution and upsampling layers to form an encoder-decoder network. The network was trained on simulated motion-corrupted images to identify and suppress those artefacts attributable to motion. The network was validated on unseen simulated datasets and real-world experimental motion-corrupted in vivo brain datasets. The trained network was able to suppress the motion artefacts in the reconstructed images, and the mean structural similarity (SSIM) increased from 0.9058 to 0.9338. The network was also able to suppress the motion artefacts from the real-world experimental dataset, and the mean SSIM increased from 0.8671 to 0.9145. The motion correction of the experimental datasets demonstrated the effectiveness of the motion simulation generation process. The proposed method successfully removed motion artefacts and outperformed an iterative entropy minimization method in terms of the SSIM index and normalized root mean squared error, which were 5-10% better for the proposed method. In conclusion, a novel, data-driven motion correction technique has been developed that can suppress motion artefacts from motion-corrupted MR images. The proposed technique is a standalone, post-processing method that does not interfere with data acquisition or reconstruction parameters, thus making it suitable for routine clinical practice.

Real-time motion and retrospective coil sensitivity correction for CEST using volumetric navigators (vNavs) at 7T

PURPOSE

To explore the impact of temporal motion-induced coil sensitivity changes on CEST-MRI at 7T and its correction using interleaved volumetric EPI navigators, which are applied for real-time motion correction.

METHODS

Five healthy volunteers were scanned via CEST. A 4-fold correction pipeline allowed the mitigation of (1) motion, (2) motion-induced coil sensitivity variations, ΔB1- , (3) motion-induced static magnetic field inhomogeneities, ΔB₀ , and (4) spatially varying transmit RF field fluctuations, ΔB1+ . Four CEST measurements were performed per session. For the first 2, motion correction was turned OFF and then ON in absence of voluntary motion, whereas in the other 2 controlled head rotations were performed. During post-processing ΔB1- was removed additionally for the motion-corrected cases, resulting in a total of 6 scenarios to be compared. In all cases, retrospective ∆B₀ and - ΔB1+ corrections were performed to compute artifact-free magnetization transfer ratio maps with asymmetric analysis (MTR_asym ).

RESULTS

Dynamic ΔB1- correction successfully mitigated signal deviations caused by head motion. In 2 frontal lobe regions of volunteer 4, induced relative signal errors of 10.9% and 3.9% were reduced to 1.1% and 1.0% after correction. In the right frontal lobe, the motion-corrected MTR_asym contrast deviated 0.92%, 1.21%, and 2.97% relative to the static case for Δω = 1, 2, 3 ± 0.25 ppm. The additional application of ΔB1- correction reduced these deviations to 0.10%, 0.14%, and 0.42%. The fully corrected MTR_asym values were highly consistent between measurements with and without intended head rotations.

CONCLUSION

Temporal ΔB1- cause significant CEST quantification bias. The presented correction pipeline including the proposed retrospective ΔB1- correction significantly reduced motion-related artifacts on CEST-MRI.

Retrospective 3D motion correction using spherical navigator echoes

PURPOSE

To develop and evaluate a rapid spherical navigator echo (SNAV) motion correction technique, then apply it for retrospective correction of brain images.

METHODS

The pre-rotated, template matching SNAV method (preRot-SNAV) was developed in combination with a novel hybrid baseline strategy, which includes acquired and interpolated templates. Specifically, the SNAV templates are only rotated around X- and Y-axis; for each rotated SNAV, simulated baseline templates that mimic object rotation about the Z-axis were interpolated. The new method was first evaluated with phantom experiments. Then, a customized SNAV-interleaved gradient echo sequence was used to image three volunteers performing directed head motion. The SNAV motion measurements were used to retrospectively correct the brain images. Experiments were performed using a 3.0T whole-body MRI scanner and both single and 8-channel head coils.

RESULTS

Phantom rotations and translations measured using the hybrid baselines agreed to within 0.9° and 1mm compared to those measured with the original preRot-SNAV method. Retrospective motion correction of in vivo images using the hybrid preRot-SNAV effectively corrected for head rotation up to 4° and 4mm.

CONCLUSIONS

The presented hybrid approach enables the acquisition of pre-rotated baseline templates in as little as 2.5s, and results in accurate measurement of rotations and translations. Retrospective 3D motion correction successfully reduced motion artifacts in vivo.

Motion correction of magnetic resonance imaging data by using adaptive moving least squares method

Image artifacts caused by subject motion during the imaging sequence are one of the most common problems in magnetic resonance imaging (MRI) and often degrade the image quality. In this study, we develop a motion correction algorithm for the interleaved-MR acquisition. An advantage of the proposed method is that it does not require either additional equipment or redundant over-sampling. The general framework of this study is similar to that of Rohlfing et al. [1], except for the introduction of the following fundamental modification. The three-dimensional (3-D) scattered data approximation method is used to correct the artifacted data as a post-processing step. In order to obtain a better match to the local structures of the given image, we use the data-adapted moving least squares (MLS) method that can improve the performance of the classical method. Numerical results are provided to demonstrate the advantages of the proposed algorithm.

Motion artifacts in MRI: A complex problem with many partial solutions

Subject motion during magnetic resonance imaging (MRI) has been problematic since its introduction as a clinical imaging modality. While sensitivity to particle motion or blood flow can be used to provide useful image contrast, bulk motion presents a considerable problem in the majority of clinical applications. It is one of the most frequent sources of artifacts. Over 30 years of research have produced numerous methods to mitigate or correct for motion artifacts, but no single method can be applied in all imaging situations. Instead, a "toolbox" of methods exists, where each tool is suitable for some tasks, but not for others. This article reviews the origins of motion artifacts and presents current mitigation and correction methods. In some imaging situations, the currently available motion correction tools are highly effective; in other cases, appropriate tools still need to be developed. It seems likely that this multifaceted approach will be what eventually solves the motion sensitivity problem in MRI, rather than a single solution that is effective in all situations. This review places a strong emphasis on explaining the physics behind the occurrence of such artifacts, with the aim of aiding artifact detection and mitigation in particular clinical situations.

Rigid body motion compensation for spiral projection imaging

Spiral projection imaging (SPI) is a 3D, spiral based magnetic resonance imaging (MRI) acquisition scheme that allows for self-navigated motion estimation of all six degrees-of-freedom. The trajectory, a set of spiral planes, is enhanced to accommodate motion tracking by adding orthogonal planes. Rigid-body motion tracking is accomplished by comparing the overlapping data and deducing the motion that is consistent with the comparisons. The accuracy of the proposed method is quantified for simulated data and for data collected using both a phantom and a volunteer. These tests were repeated to measure the effect of off-resonance blurring, coil sensitivity, gradient warping, undersampling, and nonrigid motion (e.g., neck). The artifacts of off-resonance, coils sensitivity, and gradient warping impose an unnotable effect on the accuracy of motion estimation. The worst mean accuracy is 0.15° and 0.20 mm for the phantom while the worst mean accuracy is 0.48° and 0.34 mm when imaging a brain, indicating that the nonrigid component in human subjects slightly degrades accuracy. When applied to in vivo motion, the proposed technique considerably reduces motion artifact.

Head motion detection using FID navigators

This work explores a concept for motion detection in brain MR examinations using high channel-count RF coil arrays. It applies ultrashort (<100 μsec) free induction decay signals, making use of the knowledge that motion induces variations in these signals when compared to a reference free induction decay signal. As a proof-of-concept, the method was implemented in a standard structural MRI sequence. The stability of the free induction decay-signal was verified in phantom experiments. Human experiments demonstrated that the observed variations in the navigator data provide a sensitive measure for detection of relevant and common subject motion patterns. The proposed methodology provides a means to monitor subject motion throughout a MRI scan while causing little or no impact on the sequence timing and image contrast. It could hence complement available motion detection and correction methods, thus further reducing motion sensitivity in MR applications.

Combination of multidimensional navigator echoes data from multielement RF coil

Until now, only one-dimensional navigator-echo techniques have been implemented with multielement RF coils. For the multidimensional navigator echoes, which extract six-degree of freedom motion information from the raw k-space data, an efficient raw data combination approach is needed. In this work, three combination approaches, including summation of the complex raw data, summation following phase alignment, and summation of the squares of the k-space magnitude profiles, were evaluated with the spherical navigator echoes (SNAV) technique. In vivo brain imaging experiments were used to quantify accuracy and precision and demonstrated that SNAVs acquired with an eight-channel head coil can determine the rotation and translation in range up to 10° and 20 mm with subdegree and submillimeter accuracy, respectively. Results from a 3D brain volume realignment experiment showed excellent agreement between baseline images and SNAV-aligned follow-up volumes.

Intrinsic detection of motion in segmented sequences

While many motion correction techniques for MRI have been proposed, their use is often limited by increased patient preparation, decreased patient comfort, additional scan time, or the use of specialized sequences not available on many commercial scanners. For this reason, we propose a simple self-navigating technique designed to detect motion in segmented sequences. We demonstrate that comparing two segments containing adjacent sets of k-space lines results in an aliased error function. A global shift of the aliased error function indicates the presence of in-plane rigid-body translation, while other types of motion are evident in the dispersion or breadth of the error function. Since segmented sequences commonly acquire data in sets of adjacent k-space lines, this method provides these sequences with an inherent method of detecting object motion. Motion corrupted data can then be reacquired proactively or in some cases corrected or removed retrospectively.

Rapid six-degree-of-freedom motion detection using prerotated baseline spherical navigator echoes

A new spherical navigator echo (SNAV) registration technique is presented. This technique starts by collecting a set of SNAV templates at a reference position. These templates are acquired by rotating the gradient system to result in rotation angles that uniformly cover a predefined range of rotation. The rotation angles between an unknown physically transformed position and the reference position are subsequently determined by finding the template with the lowest sum of squared differences with SNAV at the transformed position. Translations are calculated from the phase differences between the best-match SNAV template and the SNAV acquired at the transformed position. In comparison with the conventional SNAV registration technique, the proposed technique is noniterative, robust, and can detect 3-dimensional rigid body motion in less than 50 msec. The technique was verified with phantom and in vivo experiments, which demonstrated subdegree rotational and submillimeter translational accuracy over a range of simultaneous ±20° and ±10° mm of motion.

Extrapolation and correlation (EXTRACT): a new method for motion compensation in MRI

A postprocessing technique is proposed for the correction of both translational and rotational motion artifacts in magnetic resonance imaging (MRI). The method consists of two steps: 1) k-space extrapolation to generate a motion-free reference, followed by 2) correlation with actual data to estimate motion. In this paper, two different extrapolation methods were investigated for the purpose of motion estimation: edge enhancement and finite-support solution. It was found that finite-support solution performs better near the k-space center, while the edge enhancement method is superior in the outer k-space regions. Therefore, a combination of the two methods was employed to generate a motion-free reference, whose correlations with the acquired data can subsequently determine the object motion. Motion compensation was demonstrated in simulation and in vivo MR experiments. The technique is shown to be robust against noise and various types of motion.

Advantages and limitations of prospective head motion compensation for MRI using an optical motion tracking device

RATIONALE AND OBJECTIVES

Subject motion appears to be a limiting factor in numerous magnetic resonance (MR) imaging (MRI) applications. In particular, head tremor, which often accompanies stroke, may render certain high-resolution two- (2D) and three-dimensional (3D) techniques inapplicable. The reason for that is head movement during acquisition. The study objective is to achieve a method able to compensate for complete motion during data acquisition. The method should be usable for every sequence and easily implemented on different MR scanners.

MATERIALS AND METHODS

The possibility of interfacing the MR scanner with an external optical motion-tracking system capable of determining the object's position with submillimeter accuracy and an update rate of 60 Hz is shown. Movement information on the object position (head) is used to compensate for motion in real time by updating the field of view (FOV) by recalculating the gradients and radiofrequency parameter of the MR scanner during acquisition of k-space data, based on tracking data.

RESULTS

Results of rotation phantom, in vivo experiments, and implementation of three different MRI sequences, 2D spin echo, 3D gradient echo, and echo planar imaging, are presented. Finally, the proposed method is compared with the prospective motion correction software available on the scanner software.

CONCLUSION

A prospective motion correction method that works in real time only by updating the FOV of the MR scanner is presented. Results show the feasibility of using an external optical motion-tracking system to compensate for strong and fast subject motion during acquisition.

Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system

Subject motion and associated artefacts limit the applicability of MRI and the achievable quality of the images acquired. In this paper, a fully integrated method for prospective correction of arbitrary rigid body motion employing an external motion tracking device is demonstrated for the first time. The position of the imaging volume is updated prior to every excitation of the spin system. The performance of the available tracking hardware and its connection to the MR imager is analyzed in detail. With the introduction of a novel calibration procedure the accuracy of motion correction is improved compared to previous approaches. Together with the high geometry update rate even freely moving objects can be imaged without motion related artefacts. The high performance and image quality improvement in case of subject motion are demonstrated for various imaging techniques such as gradient and spin echo, as well as echo planar imaging.

Optimizing spherical navigator echoes for three-dimensional rigid-body motion detection

Spherical navigator (SNAV) echoes show promise in correcting for three-dimensional rigid-body motion. In this paper, several important parameters in the design and performance of the SNAV technique are discussed, including a novel sampling strategy, the optimal k-space radius and sampling density of the navigator, and the execution of the SNAV trajectory by the scanner hardware. A variable-sampling density (VSD) helical-spiral SNAV trajectory, which can acquire data on the entire spherical shell without exceeding the maximum slew rate of the scanner, is presented. To ensure that the VSD SNAV trajectory was properly executed by the scanner hardware, the gradient waveforms were verified using a self-encoding technique. The ability of the VSD SNAV to measure rotational and translational motion was studied with in vitro experiments at various k-space radii and sampling densities. The results of this study show that the best accuracy was attained at k-space radii of 1.4 and 1.6 cm(-1), with 2400 to 4000 samples acquired over the sphere.