Head motion detection using FID navigators

Run-time motion and first-order shim control by expanded servo navigation

PURPOSE

To provide a navigator-based run-time motion and first-order field correction for three-dimensional human brain imaging with high precision, minimal calibration and acquisition, and fast processing.

METHODS

A complex-valued linear perturbation model with feedback control is extended to estimate and correct for gradient shim fields using orbital navigators (2.3 ms). Two approaches for sensitizing the model to gradient fields are presented, one based on finite differences with three additional navigators, and another projection-based approximation requiring no additional navigators. A mechanism for noise decorrelation of the matrix and the data is proposed and evaluated to reduce unwanted parameter biases.

RESULTS

The rigid motion and first-order field control achieves robust motion and gradient shim corrections improving image quality in a series of phantom and in vivo experiments with varying field conditions. In phantom scans, magnet drifts, forced gradient field perturbations and field distortions from shifts of a second bottle phantom are successfully corrected. Field estimates of the magnet drifts are in good agreement with concurrent field probe measurements. For in vivo scans, the proposed method mitigates field variations from torso motions while being robust to head motion. In vivo gradient field precisions were30   nT / m $$ 30\;\mathrm{nT}/\mathrm{m} $$ along with single-digit micrometer and millidegree rigid precisions.

CONCLUSION

The navigator-based method achieves accurate, high-precision run-time motion and field corrections with low sequence impact and calibration requirements.

Sequence-agnostic motion-correction leveraging efficiently calibrated Pilot Tone signals

PURPOSE

This study leverages externally generated Pilot Tone (PT) signals to perform motion-corrected brain MRI for sequences with arbitrary k-space sampling and image contrast.

THEORY AND METHODS

PT signals are promising external motion sensors due to their cost-effectiveness, easy workflow, and consistent performance across contrasts and sampling patterns. However, they lack robust calibration pipelines. This work calibrates PT signal to rigid motion parameters acquired during short blocks (˜4 s) of motion calibration (MC) acquisitions, which are short enough to unobstructively fit between acquisitions. MC acquisitions leverage self-navigated trajectories that enable state-of-the-art motion estimation methods for efficient calibration. To capture the range of patient motion occurring throughout the examination, distributed motion calibration (DMC) uses data acquired from MC scans distributed across the entire examination. After calibration, PT is used to retrospectively motion-correct sequences with arbitrary k-space sampling and image contrast. Additionally, a data-driven calibration refinement is proposed to tailor calibration models to individual acquisitions. In vivo experiments involving 12 healthy volunteers tested the DMC protocol's ability to robustly correct subject motion.

RESULTS

The proposed calibration pipeline produces pose parameters consistent with reference values, even when distributing only six of these approximately 4-s MC blocks, resulting in a total acquisition time of 22 s. In vivo motion experiments reveal significant (p < 0.05 $$ p<0.05 $$ ) improved motion correction with increased signal to residual ratio for both MPRAGE and SPACE sequences with standard k-space acquisition, especially when motion is large. Additionally, results highlight the benefits of using a distributed calibration approach.

CONCLUSIONS

This study presents a framework for performing motion-corrected brain MRI in sequences with arbitrary k-space encoding and contrast, using externally generated PT signals. The DMC protocol is introduced, promoting observation of patient motion occurring throughout the examination and providing a calibration pipeline suitable for clinical deployment. The method's application is demonstrated in standard volumetric MPRAGE and SPACE sequences.

Distortionless, free-breathing, and respiratory resolved 3D diffusion weighted imaging of the abdomen

PURPOSE

Abdominal imaging is frequently performed with breath holds or respiratory triggering to reduce the effects of respiratory motion. Diffusion weighted sequences provide a useful clinical contrast but have prolonged scan times due to low signal-to-noise ratio (SNR), and cannot be completed in a single breath hold. Echo-planar imaging (EPI) is the most commonly used trajectory for diffusion weighted imaging but it is susceptible to off-resonance artifacts. A respiratory resolved, three-dimensional (3D) diffusion prepared sequence that obtains distortionless diffusion weighted images during free-breathing is presented. Techniques to address the myriad of challenges including: 3D shot-to-shot phase correction, respiratory binning, diffusion encoding during free-breathing, and robustness to off-resonance are described.

METHODS

A twice-refocused, M1-nulled diffusion preparation was combined with an RF-spoiled gradient echo readout and respiratory resolved reconstruction to obtain free-breathing diffusion weighted images in the abdomen. Cartesian sampling permits a sampling density that enables 3D shot-to-shot phase navigation and reduction of transient fat artifacts. Theoretical properties of a region-based shot rejection are described. The region-based shot rejection method was evaluated with free-breathing (normal and exaggerated breathing), and respiratory triggering. The proposed sequence was compared in vivo with multishot DW-EPI.

RESULTS

The proposed sequence exhibits no evident distortion in vivo when compared to multishot DW-EPI, robustness to B0 and B1 field inhomogeneities, and robustness to motion from different respiratory patterns.

CONCLUSION

Acquisition of distortionless, diffusion weighted images is feasible during free-breathing with a b-value of 500 s/mm2, scan time of 6 min, and a clinically viable reconstruction time.

Servo navigators: Linear regression and feedback control for rigid-body motion correction

PURPOSE

Navigator-based correction of rigid-body motion reconciling high precision with minimal acquisition, minimal calibration and simple, fast processing.

METHODS

A short orbital navigator (2.3 ms) is inserted in a three-dimensional (3D) gradient echo sequence for human head imaging. Head rotation and translation are determined by linear regression based on a complex-valued model built either from three reference navigators or in a reference-less fashion, from the first actual navigator. Optionally, the model is expanded by global phase and field offset. Run-time scan correction on this basis establishes servo control that maintains validity of the linear picture by keeping its expansion point stable in the head frame of reference. The technique is assessed in a phantom and demonstrated by motion-corrected imaging in vivo.

RESULTS

The proposed approach is found to establish stable motion control both with and without reference acquisition. In a phantom, it is shown to accurately detect motion mimicked by rotation of scan geometry as well as change in global B0 . It is demonstrated to converge to accurate motion estimates after perturbation well beyond the linear signal range. In vivo, servo navigation achieved motion detection with precision in the single-digit range of micrometers and millidegrees. Involuntary and intentional motion in the range of several millimeters were successfully corrected, achieving excellent image quality.

CONCLUSION

The combination of linear regression and feedback control enables prospective motion correction for head imaging with high precision and accuracy, short navigator readouts, fast run-time computation, and minimal demand for reference data.

Rapid and accurate navigators for motion and B0 tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators

PURPOSE

To develop a framework that jointly estimates rigid motion and polarizing magnetic field (B0 ) perturbations (δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ ) for brain MRI using a single navigator of a few milliseconds in duration, and to additionally allow for navigator acquisition at arbitrary timings within any type of sequence to obtain high-temporal resolution estimates.

THEORY AND METHODS

Methods exist that match navigator data to a low-resolution single-contrast image (scout) to estimate either motion orδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . In this work, called QUEEN (QUantitatively Enhanced parameter Estimation from Navigators), we propose combined motion andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimation from a fast, tailored trajectory with arbitrary-contrast navigator data. To this end, the concept of a quantitative scout (Q-Scout) acquisition is proposed from which contrast-matched scout data is predicted for each navigator. Finally, navigator trajectories, contrast-matched scout, andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ are integrated into a motion-informed parallel-imaging framework.

RESULTS

Simulations and in vivo experiments show the need to modelδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ to obtain accurate motion parameters estimated in the presence of strongδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Simulations confirm that tailored navigator trajectories are needed to robustly estimate both motion andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Furthermore, experiments show that a contrast-matched scout is needed for parameter estimation from multicontrast navigator data. A retrospective, in vivo reconstruction experiment shows improved image quality when using the proposed Q-Scout and QUEEN estimation.

CONCLUSIONS

We developed a framework to jointly estimate rigid motion parameters andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ from navigators. Combing a contrast-matched scout with the proposed trajectory allows for navigator deployment in almost any sequence and/or timing, which allows for higher temporal-resolution motion andδ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimates.

Prospective motion correction for brain MRI using spherical navigators

PURPOSE

To demonstrate for the first time the feasibility of performing prospective motion correction using spherical navigators (SNAVs).

METHODS

SNAVs were interleaved in a 3D FLASH sequence with an additional short baseline scan (6.8 s) for fast rotation estimation. Assessment of SNAV-based prospective motion correction was performed in six volunteers. Participant motion was guided using randomly generated stepwise prompts as well as prompts derived from real motion cases. Experiments were performed on a 3 T MRI scanner using a 32-channel head coil.

RESULTS

When optimized for real-time application, SNAV-based motion estimates were computed in 25.8 ± 1.3 ms. Phantom-based quantification of rotation and translation accuracy indicated mean absolute errors of 0.10 ± 0.09° and 0.25 ± 0.14 mm, respectively. Implementing SNAV-based motion estimates for prospective motion correction led to a clear improvement in image quality with minimal increase in scan time (<5%).

CONCLUSION

Optimization of SNAV processing for real-time application enables prospective motion correction with low latency and minimal scan time requirements.

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.

Motion guidance lines for robust data consistency-based retrospective motion correction in 2D and 3D MRI

PURPOSE

To develop a robust retrospective motion-correction technique based on repeating k-space guidance lines for improving motion correction in Cartesian 2D and 3D brain MRI.

METHODS

The motion guidance lines are inserted into the standard sequence orderings for 2D turbo spin echo and 3D MPRAGE to inform a data consistency-based motion estimation and reconstruction, which can be guided by a low-resolution scout. The extremely limited number of required guidance lines are repeated during each echo train and discarded in the final image reconstruction. Thus, integration within a standard k-space acquisition ordering ensures the expected image quality/contrast and motion sensitivity of that sequence.

RESULTS

Through simulation and in vivo 2D multislice and 3D motion experiments, we demonstrate that respectively 2 or 4 optimized motion guidance lines per shot enables accurate motion estimation and correction. Clinically acceptable reconstruction times are achieved through fully separable on-the-fly motion optimizations (˜1 s/shot) using standard scanner GPU hardware.

CONCLUSION

The addition of guidance lines to scout accelerated motion estimation facilitates robust retrospective motion correction that can be effectively introduced without perturbing standard clinical protocols and workflows.

Effect of motion, cortical orientation and spatial resolution on quantitative imaging of cortical R2* and magnetic susceptibility at 0.3 mm in-plane resolution at 7 T

MR images of the effective relaxation rate R2* and magnetic susceptibility χ derived from multi-echo T2*-weighted (T2*w) MRI can provide insight into iron and myelin distributions in the brain, with the potential of providing biomarkers for neurological disorders. Quantification of R2* and χ at submillimeter resolution in the cortex in vivo has been difficult because of challenges such as head motion, limited signal to noise ratio, long scan time, and motion related magnetic field fluctuations. This work aimed to improve the robustness for quantifying intracortical R2* and χ and analyze the effects from motion, spatial resolution, and cortical orientation. T2*w data was acquired with a spatial resolution of 0.3 × 0.3 × 0.4 mm3 at 7 T and downsampled to various lower resolutions. A combined correction for motion and B0 changes was deployed using volumetric navigators. Such correction improved the T2*w image quality rated by experienced image readers and test-retest reliability of R2* and χ quantification with reduced median inter-scan differences up to 10 s-1 and 5 ppb, respectively. R2* and χ near the line of Gennari, a cortical layer high in iron and myelin, were as much as 10 s-1 and 10 ppb higher than the region at adjacent cortical depth. In addition, a significant effect due to the cortical orientation relative to the static field (B0) was observed in χ with a peak-to-peak amplitude of about 17 ppb. In retrospectively downsampled data, the capability to distinguish different cortical depth regions based on R2* or χ contrast remained up to isotropic 0.5 mm resolution. This study highlights the unique characteristics of R2* and χ along the cortical depth at submillimeter resolution and the need for motion and B0 corrections for their robust quantification in vivo.

Feasibility of tracking involuntary head movement for MRI using a coil as a magnetic dipole in a time-varying gradient

Accurate tracking involuntary head movements is fairly a challenging problem in MR imaging of the brain. Though there are few techniques available to monitor the head movement of the subject for a prospective motion correction, it is still an unsolved problem in MRI. In this theoretical study, we aim to describe an analytical investigation to track head movement inside an MR scanner by calculating the change in induced voltage in the head-mounted coils during the execution of time-varying gradients. We derive an expression to calculate the change in induced voltage in a coil placed in a time-varying gradient. We also derive a general equation to investigate the changes in the induced voltage in a set of coils mounted onto the head for the planar position and orientation of the coils. Each coil is considered as a magnetic dipole with location and sensitivity vectors. The changes of the vectors can track the head movement in the MR scanner by measuring the changes in the induced voltage in the coils. The dipole concept is valid for a wide range of coils. The changes in induced voltage in the coils are linear due to small changes in pose of the head. Movement parameters are estimated from the induced voltage changes. If the random noise voltage is less than 100 μV, it does not significantly affect movement parameters because the change in induced voltage in the coils dominates over the small noise voltage. This method and array of the coils may provide a real-life solution to the long-standing problem of head motion during MRI.

The MotoNet: A 3 Tesla MRI-Conditional EEG Net with Embedded Motion Sensors

We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat.

METHODS

The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers.

RESULTS

MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG.

CONCLUSIONS

The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.

Prospective motion correction in kidney MRI using FID navigators

PURPOSE

Abdominal MRI scans may require breath-holding to prevent image quality degradation, which can be challenging for patients, especially children. In this study, we evaluate whether FID navigators can be used to measure and correct for motion prospectively, in real-time.

METHODS

FID navigators were inserted into a 3D radial sequence with stack-of-stars sampling. MRI experiments were conducted on 6 healthy volunteers. A calibration scan was first acquired to create a linear motion model that estimates the kidney displacement due to respiration from the FID navigator signal. This model was then applied to predict and prospectively correct for motion in real time during deep and continuous deep breathing scans. Resultant images acquired with the proposed technique were compared with those acquired without motion correction. Dice scores were calculated between inhale/exhale motion states. Furthermore, images acquired using the proposed technique were compared with images from extra-dimensional golden-angle radial sparse parallel, a retrospective motion state binning technique.

RESULTS

Images reconstructed for each motion state show that the kidneys' position could be accurately tracked and corrected with the proposed method. The mean of Dice scores computed between the motion states were improved from 0.93 to 0.96 using the proposed technique. Depiction of the kidneys was improved in the combined images of all motion states. Comparing results of the proposed technique and extra-dimensional golden-angle radial sparse parallel, high-quality images can be reconstructed from a fraction of spokes using the proposed method.

CONCLUSION

The proposed technique reduces blurriness and motion artifacts in kidney imaging by prospectively correcting their position both in-plane and through-slice.

Prospective motion correction and automatic segmentation of penetrating arteries in phase contrast MRI at 7 T

PURPOSE

To develop a prospective motion correction (MC) method for phase contrast (PC) MRI of penetrating arteries (PAs) in centrum semiovale at 7 T and to evaluate its performance using automatic PA segmentation.

METHODS

Head motion was monitored and corrected during the scan based on fat navigator images. Two convolutional neural networks (CNN) were developed to automatically segment PAs and exclude surface vessels. Real-life scans with MC and without MC (NoMC) were performed to evaluate the MC performance. Motion score was calculated from the ranges of translational and rotational motion parameters. MC versus NoMC pairs with similar motion scores during MC and NoMC scans were compared. Data corrupted by motion were reacquired to further improve PA visualization.

RESULTS

PA counts (N_PA ) and PC and magnitude contrasts (MgC) relative to neighboring tissue were significantly correlated with motion score and were higher in MC than NoMC images at motion scores above 0.5-0.8 mm. Data reacquisition further increased PC but had no significant effect on N_PA and MgC. CNNs had higher sensitivity and Dice similarity coefficient for detecting PAs than a threshold-based method.

CONCLUSIONS

Prospective MC can improve the count and contrast of segmented PAs in the presence of severe motion. CNN-based PA segmentation has improved performance in delineating PAs than the threshold-based method.

Real-time B0 compensation during gantry rotation in a 0.35 T MRI-Linac

BACKGROUND

Rotation of the ferromagnetic gantry of a low magnetic field MRI-Linac was previously demonstrated to cause large center frequency offsets of ±400 Hz. The B₀ off-resonances cause image artifacts and imaging isocenter shifts that would preclude MRI-guided arc therapy.

PURPOSE

The purpose of this study was to measure and compensate for center frequency offsets in real-time during gantry rotation on a 0.35 T MRI-Linac using a free induction decay (FID) navigator.

METHODS

A nonselective FID navigator was added before each 2D balanced steady-state free precession (bSSFP) cine image acquisition on a 0.35 T MRI-Linac. Images were acquired at 7.3 frames per second. Phase data from the initial FID navigator (while the gantry was stationary) was used as a reference. The phase data from each subsequent FID navigator was used to calculate the real-time B₀ off-resonance. The transmitter/receiver phase and the phase accrual over the adjacent image acquisition were adjusted to correct for the center frequency offset. Measurements were performed using a MRI-Linac dynamic phantom prior to and while the gantry rotated clockwise and counterclockwise. Image quality and signal-to-noise ratio were compared between uncorrected and B₀ corrected MRIs using a reference image acquired while the gantry was stationary. Four targets in the phantom were manually contoured on the first image frame and an active contouring algorithm was used retrospectively on each subsequent frame to assess image variations and calculate Dice coefficients. Additionally, three healthy volunteers were imaged using the same pulse sequences with and without real-time B₀ compensation during gantry rotation. Normalized root mean square errors (nRMSEs) were calculated for the phantom and in vivo to assess the efficacy of the B₀ compensation on image quality. The measured center frequency offsets from the volunteer and MRI dynamic phantom navigator data were also compared. The sinusoidal behavior of the center frequency offsets was modeled based on the gantry layout and long time constant eddy currents resulting from gantry rotation.

RESULTS

The duration of the FID navigator and processing was 4.5 ms. The FID navigator resulted in a ≤11% drop in signal-to-noise ratio (SNR) in the phantom and in vivo (liver). Dice coefficients from the MR-IGRT phantom contour measurements remained above 0.8 with B₀ compensation. Without B₀ compensation, the Dice coefficients dropped below 0.8 for up to 21% of the time depending on the contour. Real-time B₀ compensation resulted in mean reductions in nRMSE of 51% and 16% for the MR-IGRT phantom and in vivo, respectively. Peak-to-peak center frequency offsets ranged from 757 to 773 Hz in the phantom and 670 to 871 Hz in vivo.

CONCLUSION

Dynamic real-time B₀ compensation significantly improved image quality and reduced artifacts during gantry rotation in the phantom and in vivo. However, the FID navigator resulted in a small drop in the imaging duty cycle and SNR. This article is protected by copyright. All rights reserved.

In-scanner head motion and structural covariance networks

In-scanner head motion systematically reduces estimated regional gray matter volumes obtained from structural brain MRI. Here, we investigate how head motion affects structural covariance networks that are derived from regional gray matter volumetric estimates. We acquired motion-affected and low-motion whole brain T1-weighted MRI in 29 healthy adult subjects and estimated relative regional gray matter volumes using a voxel-based morphometry approach. Structural covariance network analyses were undertaken while systematically increasing the number of included motion-affected scans. We demonstrate that the standard deviation in regional gray matter estimates increases as the number of motion-affected scans increases. This increases pairwise correlations between regions, a key determinant for construction of structural covariance networks. We further demonstrate that head motion systematically alters graph theoretic metrics derived from these networks. Finally, we present evidence that weighting correlations using image quality metrics can mitigate the effects of head motion. Our findings suggest that in-scanner head motion is a source of error that violates the assumption that structural covariance networks reflect neuroanatomical connectivity between brain regions. Results of structural covariance studies should be interpreted with caution, particularly when subject groups are likely to move their heads in the scanner.

Scout accelerated motion estimation and reduction (SAMER)

PURPOSE

To demonstrate a navigator/tracking-free retrospective motion estimation technique that facilitates clinically acceptable reconstruction time.

METHODS

Scout accelerated motion estimation and reduction (SAMER) uses a single 3-5 s, low-resolution scout scan and a novel sequence reordering to independently determine motion states by minimizing the data-consistency error in a SENSE plus motion forward model. This eliminates time-consuming alternating optimization as no updates to the imaging volume are required during the motion estimation. The SAMER approach was assessed quantitatively through extensive simulation and was evaluated in vivo across multiple motion scenarios and clinical imaging contrasts. Finally, SAMER was synergistically combined with advanced encoding (Wave-CAIPI) to facilitate rapid motion-free imaging.

RESULTS

The highly accelerated scout provided sufficient information to achieve accurate motion trajectory estimation (accuracy ~0.2 mm or degrees). The novel sequence reordering improved the stability of the motion parameter estimation and image reconstruction while preserving the clinical imaging contrast. Clinically acceptable computation times for the motion estimation (~4 s/shot) are demonstrated through a fully separable (non-alternating) motion search across the shots. Substantial artifact reduction was demonstrated in vivo as well as corresponding improvement in the quantitative error metric. Finally, the extension of SAMER to Wave-encoding enabled rapid high-quality imaging at up to R = 9-fold acceleration.

CONCLUSION

SAMER significantly improved the computational scalability for retrospective motion estimation and correction.

LAPNet: Non-Rigid Registration Derived in k-Space for Magnetic Resonance Imaging

Physiological motion, such as cardiac and respiratory motion, during Magnetic Resonance (MR) image acquisition can cause image artifacts. Motion correction techniques have been proposed to compensate for these types of motion during thoracic scans, relying on accurate motion estimation from undersampled motion-resolved reconstruction. A particular interest and challenge lie in the derivation of reliable non-rigid motion fields from the undersampled motion-resolved data. Motion estimation is usually formulated in image space via diffusion, parametric-spline, or optical flow methods. However, image-based registration can be impaired by remaining aliasing artifacts due to the undersampled motion-resolved reconstruction. In this work, we describe a formalism to perform non-rigid registration directly in the sampled Fourier space, i.e. k-space. We propose a deep-learning based approach to perform fast and accurate non-rigid registration from the undersampled k-space data. The basic working principle originates from the Local All-Pass (LAP) technique, a recently introduced optical flow-based registration. The proposed LAPNet is compared against traditional and deep learning image-based registrations and tested on fully-sampled and highly-accelerated (with two undersampling strategies) 3D respiratory motion-resolved MR images in a cohort of 40 patients with suspected liver or lung metastases and 25 healthy subjects. The proposed LAPNet provided consistent and superior performance to image-based approaches throughout different sampling trajectories and acceleration factors.

Estimation of in-scanner head pose changes during structural MRI using a convolutional neural network trained on eye tracker video

INTRODUCTION

In-scanner head motion is a common cause of reduced image quality in neuroimaging, and causes systematic brain-wide changes in cortical thickness and volumetric estimates derived from structural MRI scans. There are few widely available methods for measuring head motion during structural MRI. Here, we train a deep learning predictive model to estimate changes in head pose using video obtained from an in-scanner eye tracker during an EPI-BOLD acquisition with participants undertaking deliberate in-scanner head movements. The predictive model was used to estimate head pose changes during structural MRI scans, and correlated with cortical thickness and subcortical volume estimates.

METHODS

21 healthy controls (age 32 ± 13 years, 11 female) were studied. Participants carried out a series of stereotyped prompted in-scanner head motions during acquisition of an EPI-BOLD sequence with simultaneous recording of eye tracker video. Motion-affected and motion-free whole brain T1-weighted MRI were also obtained. Image coregistration was used to estimate changes in head pose over the duration of the EPI-BOLD scan, and used to train a predictive model to estimate head pose changes from the video data. Model performance was quantified by assessing the coefficient of determination (R²). We evaluated the utility of our technique by assessing the relationship between video-based head pose changes during structural MRI and (i) vertex-wise cortical thickness and (ii) subcortical volume estimates.

RESULTS

Video-based head pose estimates were significantly correlated with ground truth head pose changes estimated from EPI-BOLD imaging in a hold-out dataset. We observed a general brain-wide overall reduction in cortical thickness with increased head motion, with some isolated regions showing increased cortical thickness estimates with increased motion. Subcortical volumes were generally reduced in motion affected scans.

CONCLUSIONS

We trained a predictive model to estimate changes in head pose during structural MRI scans using in-scanner eye tracker video. The method is independent of individual image acquisition parameters and does not require markers to be to be fixed to the patient, suggesting it may be well suited to clinical imaging and research environments. Head pose changes estimated using our approach can be used as covariates for morphometric image analyses to improve the neurobiological validity of structural imaging studies of brain development and disease.

Motion correction methods for MRS: experts' consensus recommendations

Long acquisition times due to intrinsically low signal-to-noise ratio and the need for highly homogeneous B₀ field make MRS particularly susceptible to motion or scanner instability compared with MRI. Motion-induced changes in both localization and shimming (ie B₀ homogeneity) degrade MRS data quality. To mitigate the effects of motion three approaches can be employed: (1) subject immobilization, (2) retrospective correction, and (3) prospective real-time correction using internal and/or external tracking methods. Prospective real-time correction methods can simultaneously update localization and the B₀ field to improve MRS data quality. While localization errors can be corrected with both internal (navigators) and external (optical camera, NMR probes) tracking methods, the B₀ field correction requires internal navigator methods to measure the B₀ field inside the imaged volume and the possibility to update the scanner shim hardware in real time. Internal and external tracking can rapidly update the MRS localization with submillimeter and subdegree precision, while scanner frequency and first-order shims of scanner hardware can be updated by internal methods every sequence repetition. These approaches are most well developed for neuroimaging, for which rigid transformation is primarily applicable. Real-time correction greatly improves the stability of MRS acquisition and quantification, as shown in clinical studies on subjects prone to motion, including children and patients with movement disorders, enabling robust measurement of metabolite signals including those with low concentrations, such as gamma-aminobutyric acid and glutathione. Thus, motion correction is recommended for MRS users and calls for tighter integration and wider availability of such methods by MR scanner manufacturers.

Free induction decay navigator motion metrics for prediction of diagnostic image quality in pediatric MRI

Purpose

To investigate the ability of free induction decay navigator (FIDnav)‐based motion monitoring to predict diagnostic utility and reduce the time and cost associated with acquiring diagnostically useful images in a pediatric patient cohort.

Methods

A study was carried out in 102 pediatric patients (aged 0‐18 years) at 3T using a 32‐channel head coil array. Subjects were scanned with an FID‐navigated MPRAGE sequence and images were graded by two radiologists using a five‐point scale to evaluate the impact of motion artifacts on diagnostic image quality. The correlation between image quality and four integrated FIDnav motion metrics was investigated, as well as the sensitivity and specificity of each FIDnav‐based metric to detect different levels of motion corruption in the images. Potential time and cost savings were also assessed by retrospectively applying an optimal detection threshold to FIDnav motion scores.

Results

A total of 12% of images were rated as non‐diagnostic, while a further 12% had compromised diagnostic value due to motion artifacts. FID‐navigated metrics exhibited a moderately strong correlation with image grade (Spearman's rho ≥ 0.56). Integrating the cross‐correlation between FIDnav signal vectors achieved the highest sensitivity and specificity for detecting non‐diagnostic images, yielding total time savings of 7% across all scans. This corresponded to a financial benefit of $2080 in this study.

Conclusions

Our results indicate that integrated motion metrics from FIDnavs embedded in structural MRI are a useful predictor of diagnostic image quality, which translates to substantial time and cost savings when applied to pediatric MRI examinations.

Dynamic distortion correction for functional MRI using FID navigators

PURPOSE

To develop a method for slice-wise dynamic distortion correction for EPI using rapid spatiotemporal B₀ field measurements from FID navigators (FIDnavs) and to evaluate the efficacy of this new approach relative to an established data-driven technique.

METHODS

A low-resolution reference image was used to create a forward model of FIDnav signal changes to enable estimation of spatiotemporal B₀ inhomogeneity variations up to second order from measured FIDnavs. Five volunteers were scanned at 3 T using a 64-channel coil with FID-navigated EPI. The accuracy of voxel shift measurements and geometric distortion correction was assessed for experimentally induced magnetic field perturbations. The temporal SNR was evaluated in EPI time-series acquired at rest and with a continuous nose-touching action, before and after image realignment.

RESULTS

Field inhomogeneity coefficients and voxel shift maps measured using FIDnavs were in excellent agreement with multi-echo EPI measurements. The FID-navigated distortion correction accurately corrected image geometry in the presence of induced magnetic field perturbations, outperforming the data-driven approach in regions with large field offsets. In functional MRI scans with nose touching, FIDnav-based correction yielded temporal SNR gains of 30% in gray matter. Following image realignment, which accounted for global image shifts, temporal SNR gains of 3% were achieved.

CONCLUSIONS

Our proposed application of FIDnavs enables slice-wise dynamic distortion correction with high temporal efficiency. We achieved improved signal stability by leveraging the encoding information from multichannel coils. This approach can be easily adapted to other EPI-based sequences to improve temporal SNR for a variety of clinical and research applications.

Sharpness in motion corrected quantitative imaging at 7T

Sub-millimeter imaging at 7T has opened new possibilities for qualitatively and quantitatively studying brain structure as it evolves throughout the life span. However, subject motion introduces image blurring on the order of magnitude of the spatial resolution and is thus detrimental to image quality. Such motion can be corrected for, but widespread application has not yet been achieved and quantitative evaluation is lacking. This raises a need to quantitatively measure image sharpness throughout the brain. We propose a method to quantify sharpness of brain structures at sub-voxel resolution, and use it to assess to what extent limited motion is related to image sharpness. The method was evaluated in a cohort of 24 healthy volunteers with a wide and uniform age range, aiming to arrive at results that largely generalize to larger populations. Using 3D fat-excited motion navigators, quantitative R₁, R₂^* and Quantitative Susceptibility Maps and T₁-weighted images were retrospectively corrected for motion. Sharpness was quantified in all modalities for selected regions of interest (ROI) by fitting the sigmoidally shaped error function to data within locally homogeneous clusters. A strong, almost linear correlation between motion and sharpness improvement was observed, and motion correction significantly improved sharpness. Overall, the Full Width at Half Maximum reduced from 0.88 mm to 0.70 mm after motion correction, equivalent to a 2.0 times smaller voxel volume. Motion and sharpness were not found to correlate with the age of study participants. We conclude that in our data, motion correction using fat navigators is overall able to restore the measured sharpness to the imaging resolution, irrespective of the amount of motion observed during scanning.

Ultimate MRI

The basic principles of Magnetic Resonance have been understood for over 70 years and a mainstay of medical imaging for over 40. At this point, it's no longer about simply porting these principles to medical imaging. But we are by no means confined to simply polishing either. Significant innovation and even revolution can come to old technologies. The recent revolution in optical microscopy shattered the resolution constraint imposed by a seemingly fundamental physical law (the diffraction limit) and reinvigorated a 500-year-old modality. Progress comes from re-examining old-ways and sidestepping underlying assumptions. This is already underway for MRI; and is fueled by advances in image reconstruction. Reconstruction increasingly employs sophisticated general models often using subtle and hopefully innocuous prior knowledge about the object. This allows a careful re-examination of some basic prerequisites for MRI such as uniform static fields, linear encoding fields, full Nyquist sampling, or even a stationary object. These powerful reconstruction tools are driving changes in acquisition strategy and basic hardware. The scanner of the future will know more about itself and its patient and his/her biology than ever before. This strategy emboldens relaxed hardware constraints and more specialized scanners, hopefully expanding the reach and value offered by MR imaging.

Rapid measurement and correction of spatiotemporal B0 field changes using FID navigators and a multi-channel reference image

PURPOSE

To measure spatiotemporal B₀ field changes in real time using FID navigators (FIDnavs) and to demonstrate the efficacy of retrospectively correcting high-resolution T 2 * -weighted images using a novel FIDnav framework.

METHODS

A forward model of the complex FIDnav signals was generated by simulating the effect of changes in the underlying B₀ inhomogeneity coefficients, with spatial encoding provided by a multi-channel reference image. Experiments were performed at 3T to assess the accuracy of B₀ field estimates from FIDnavs acquired from a 64-channel head coil under different shim settings and in 5 volunteers performing deep-breathing and nose-touching tasks designed to modulate the B₀ field. Second-order, in-plane spherical harmonic (SH) inhomogeneity coefficients estimated from FIDnavs were incorporated into an iterative reconstruction to retrospectively correct 2D gradient-echo images acquired in both axial and sagittal planes.

RESULTS

Spatiotemporal B₀ field changes measured from rapidly acquired FIDnavs were in good agreement with the results of second-order SH fitting to the measured field maps. FIDnav field estimates accounted for a significant proportion of the ΔB₀ variance induced by deep breathing (64 ± 21%) and nose touching (67 ± 34%) across all volunteers. Ghosting, blurring, and intensity modulation artifacts in T 2 * -weighted images, induced by spatiotemporal field changes, were visibly reduced following retrospective correction with FIDnav inhomogeneity coefficients.

CONCLUSIONS

Spatially resolved B₀ inhomogeneity changes up to second order can be characterized in real time using the proposed approach. Retrospective FIDnav correction substantially improves T 2 * -weighted image quality in the presence of strong B₀ field modulations, with potential for real-time shimming.

Retrospective correction of head motion using measurements from an electromagnetic tracker

PURPOSE

To investigate the feasibility of using an electromagnetic (EM) tracker to estimate rigid body head motion parameters, and using these measurements to retrospectively reduce motion artifacts.

THEORY AND METHODS

A clinically used MPRAGE sequence was modified to measure motion using the EM tracking system once per repetition time. A retrospective k-space based motion correction algorithm that corrects for phase ramps (translation in image domain) and rotation of 3D k-space (rotation in image domain) was developed, using the parameters recorded using an EM tracker. The accuracy of the EM tracker for the purpose of motion measurement and correction was tested in phantoms, volunteers, and pediatric patients.

RESULTS

Position localization was accurate to the order of 200 microns compared with registration localization in a phantom study. The quality of reconstructed images was assessed by computing the root mean square error, the structural similarity metric and average edge strength. Image quality improved consistently when motion correction was applied in both volunteer scans with deliberate head motion and in pediatric patient scans. In patients, the average edge strength improved significantly with retrospective motion correction, compared with images with no correction applied.

CONCLUSIONS

EM tracking was effective in measuring head motion in the MRI scanner with high accuracy, and enabled retrospective reconstruction to improve image quality by reducing motion artifacts.

Retrospective correction of motion-affected MR images using deep learning frameworks

PURPOSE

Motion is 1 extrinsic source for imaging artifacts in MRI that can strongly deteriorate image quality and, thus, impair diagnostic accuracy. In addition to involuntary physiological motion such as respiration and cardiac motion, intended and accidental patient movements can occur. Any impairment by motion artifacts can reduce the reliability and precision of the diagnosis and a motion-free reacquisition can become time- and cost-intensive. Numerous motion correction strategies have been proposed to reduce or prevent motion artifacts. These methods have in common that they need to be applied during the actual measurement procedure with a-priori knowledge about the expected motion type and appearance. For retrospective motion correction and without the existence of any a-priori knowledge, this problem is still challenging.

METHODS

We propose the use of deep learning frameworks to perform retrospective motion correction in a reference-free setting by learning from pairs of motion-free and motion-affected images. For this image-to-image translation problem, we propose and compare a variational auto encoder and generative adversarial network. Feasibility and influences of motion type and optimal architecture are investigated by blinded subjective image quality assessment and by quantitative image similarity metrics.

RESULTS

We observed that generative adversarial network-based motion correction is feasible producing near-realistic motion-free images as confirmed by blinded subjective image quality assessment. Generative adversarial network-based motion correction accordingly resulted in images with high evaluation metrics (normalized root mean squared error <0.08, structural similarity index >0.8, normalized mutual information >0.9).

CONCLUSION

Deep learning-based retrospective restoration of motion artifacts is feasible resulting in near-realistic motion-free images. However, the image translation task can alter or hide anatomical features and, therefore, the clinical applicability of this technique has to be evaluated in future studies.

Real-Time Filtering with Sparse Variations for Head Motion in Magnetic Resonance Imaging

Estimating a time-varying signal, such as head motion from magnetic resonance imaging data, becomes particularly challenging in the face of other temporal dynamics such as functional activation. This paper describes a new Kalman filter-like framework that includes a sparse residual term in the measurement model. This additional term allows the extended Kalman filter to generate real-time motion estimates suitable for prospective motion correction when such dynamics occur. An iterative augmented Lagrangian algorithm similar to the alterating direction method of multipliers implements the update step for this Kalman filter. This paper evaluates the accuracy and convergence rate of this iterative method for small and large motion in terms of its sensitivity to parameter selection. The included experiment on a simulated functional magnetic resonance imaging acquisition demonstrates that the resulting method improves the maximum Youden's J index of the time series analysis by 2-3% versus retrospective motion correction, while the sensitivity index increases from 4.3 to 5.4 when combining prospective and retrospective correction.

Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging

Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.

Head motion measurement and correction using FID navigators

PURPOSE

To develop a novel framework for rapid, intrinsic head motion measurement in MRI using FID navigators (FIDnavs) from a multichannel head coil array.

METHODS

FIDnavs encode substantial rigid-body motion information; however, current implementations require patient-specific training with external tracking data to extract quantitative positional changes. In this work, a forward model of FIDnav signals was calibrated using simulated movement of a reference image within a model of the spatial coil sensitivities. A FIDnav module was inserted into a nonselective 3D FLASH sequence, and rigid-body motion parameters were retrospectively estimated every readout time using nonlinear optimization to solve the inverse problem posed by the measured FIDnavs. This approach was tested in simulated data and in 7 volunteers, scanned at 3T with a 32-channel head coil array, performing a series of directed motion paradigms.

RESULTS

FIDnav motion estimates achieved mean absolute errors of 0.34 ± 0.49 mm and 0.52 ± 0.61° across all subjects and scans, relative to ground-truth motion measurements provided by an electromagnetic tracking system. Retrospective correction with FIDnav motion estimates resulted in substantial improvements in quantitative image quality metrics across all scans with intentional head motion.

CONCLUSIONS

Quantitative rigid-body motion information can be effectively estimated using the proposed FIDnav-based approach, which represents a practical method for retrospective motion compensation in less cooperative patient populations.

TArgeted Motion Estimation and Reduction (TAMER): Data Consistency Based Motion Mitigation for MRI Using a Reduced Model Joint Optimization

We introduce a data consistency based retrospective motion correction method, TArgeted Motion Estimation and Reduction (TAMER), to correct for patient motion in Magnetic Resonance Imaging (MRI). Specifically, a motion free image and motion trajectory are jointly estimated by minimizing the data consistency error of a SENSE forward model including rigid-body subject motion. In order to efficiently solve this large non-linear optimization problem, we employ reduced modeling in the parallel imaging formulation by assessing only a subset of target voxels at each step of the motion search. With this strategy we are able to effectively capture the tight coupling between the image voxel values and motion parameters. We demonstrate in simulations TAMER's ability to find similar search directions compared to a full model, with an average error of 22%, vs. 73% error when using previously proposed alternating methods. The reduced model decreased the computation time fold compared to a full image volume evaluation. In phantom experiments, our method successfully mitigates both translation and rotation artifacts, reducing image RMSE compared to a motion-free gold standard from 21% to 14% in a translating phantom, and from 17% to 10% in a rotating phantom. Qualitative image improvements are seen in human imaging of moving subjects compared to conventional reconstruction. Finally, we compare in vivo image results of our method to the state-of-the-art.

Adaptive bulk motion exclusion for improved robustness of abdominal magnetic resonance imaging

Non-Cartesian magnetic resonance imaging (MRI) sequences have shown great promise for abdominal examination during free breathing, but break down in the presence of bulk patient motion (i.e. voluntary or involuntary patient movement resulting in translation, rotation or elastic deformations of the body). This work describes a data-consistency-driven image stabilization technique that detects and excludes bulk movements during data acquisition. Bulk motion is identified from changes in the signal intensity distribution across different elements of a multi-channel receive coil array. A short free induction decay signal is acquired after excitation and used as a measure to determine alterations in the load distribution. The technique has been implemented on a clinical MR scanner and evaluated in the abdomen. Six volunteers were scanned and two radiologists scored the reconstructions. To show the applicability to other body areas, additional neck and knee images were acquired. Data corrupted by bulk motion were successfully detected and excluded from image reconstruction. An overall increase in image sharpness and reduction of streaking and shine-through artifacts were seen in the volunteer study, as well as in the neck and knee scans. The proposed technique enables automatic real-time detection and exclusion of bulk motion during MR examinations without user interaction. It may help to improve the reliability of pediatric MRI examinations without the use of sedation.

A Method for Measuring Orientation Within a Magnetic Resonance Imaging Scanner Using Gravity and the Static Magnetic Field (VectOrient)

In MRI brain imaging, subject motion limits obtainable image clarity. Due to the hardware layout of an MRI scanner, gradient excitations can be used to rapidly detect position. Orientation, however, is more difficult to detect and is commonly calculated by comparing the position measurements of multiple spatially constrained points to a reference dataset. The result is increased size of the apparatus the subject must wear, which can influence the imaging workflow. In optical based methods marker attachment sites are limited due to the line of sight requirement between the camera and marker, and an external reference frame is introduced. To address these challenges a method called VectOrient is proposed for orientation measurement that is based on vector observations of gravity and the MRI scanner's static magnetic field. A prototype device comprising of an accelerometer, magnetometer and angular rate sensor shows good MRI compatibility. Phantom scans of a pineapple with zero scanner specific calibration achieve comparable results to a rigid body registration algorithm with deviations less than 0.8 degrees over 28 degree changes in orientation. Dynamic performance shows potential for prospective motion correction as rapid changes in orientation (peak 20 degrees per second) can be corrected. The pulse sequence implemented achieves orientation updates with a latency estimated to be less than 12.7 ms, of which only a small fraction (<1 ms) is used for computing orientation from the raw sensor signals. The device is capable of quantifying subject respiration and heart rates. The proposed approach for orientation estimation could help address some limitations of existing methods such as orientation measurement range, temporal resolution, ease of use and marker placement.

Motion correction in MRI of the brain

Subject motion in MRI is a relevant problem in the daily clinical routine as well as in scientific studies. Since the beginning of clinical use of MRI, many research groups have developed methods to suppress or correct motion artefacts. This review focuses on rigid body motion correction of head and brain MRI and its application in diagnosis and research. It explains the sources and types of motion and related artefacts, classifies and describes existing techniques for motion detection, compensation and correction and lists established and experimental approaches. Retrospective motion correction modifies the MR image data during the reconstruction, while prospective motion correction performs an adaptive update of the data acquisition. Differences, benefits and drawbacks of different motion correction methods are discussed.

Accuracy and Precision of Head Motion Information in Multi-Channel Free Induction Decay Navigators for Magnetic Resonance Imaging

Free induction decay (FID) navigators were found to qualitatively detect rigid-body head movements, yet it is unknown to what extent they can provide quantitative motion estimates. Here, we acquired FID navigators at different sampling rates and simultaneously measured head movements using a highly accurate optical motion tracking system. This strategy allowed us to estimate the accuracy and precision of FID navigators for quantification of rigid-body head movements. Five subjects were scanned with a 32-channel head coil array on a clinical 3T MR scanner during several resting and guided head movement periods. For each subject we trained a linear regression model based on FID navigator and optical motion tracking signals. FID-based motion model accuracy and precision was evaluated using cross-validation. FID-based prediction of rigid-body head motion was found to be with a mean translational and rotational error of 0.14±0.21 mm and 0.08±0.13°, respectively. Robust model training with sub-millimeter and sub-degree accuracy could be achieved using 100 data points with motion magnitudes of ±2 mm and ±1° for translation and rotation. The obtained linear models appeared to be subject-specific as inter-subject application of a "universal" FID-based motion model resulted in poor prediction accuracy. The results show that substantial rigid-body motion information is encoded in FID navigator signal time courses. Although, the applied method currently requires the simultaneous acquisition of FID signals and optical tracking data, the findings suggest that multi-channel FID navigators have a potential to complement existing tracking technologies for accurate rigid-body motion detection and correction in MRI.

Reduction of motion artifacts in carotid MRI using free-induction decay navigators

PURPOSE

To develop a framework for prospective free-induction decay (FID)-based navigator gating for suppression of motion artifacts in carotid magnetic resonance imaging (MRI) and to assess its capability in vivo.

MATERIALS AND METHODS

An FID-navigator, comprising a spatially selective low flip-angle sinc-pulse followed by an analog-to-digital converter (ADC) readout, was added to a conventional turbo spin-echo (TSE) sequence. Real-time navigator processing delivered accept/reject-and-reacquire decisions to the sequence. In this Institutional Review Board (IRB)-approved study, seven volunteers were scanned with a 2D T2-weighted TSE sequence. A reference scan with volunteers instructed to minimize motion as well as nongated and gated scans with volunteers instructed to perform different motion tasks were performed in each subject. Multiple image quality measures were employed to quantify the effect of gating.

RESULTS

There was no significant difference in lumen-to-wall sharpness (2.3 ± 0.3 vs. 2.3 ± 0.4), contrast-to-noise ratio (CNR) (9.0 ± 2.0 vs. 8.5 ± 2.0), or image quality score (3.1 ± 0.9 vs. 2.6 ± 1.2) between the reference and gated images. For images acquired during motion, all image quality measures were higher (P < 0.05) in the gated compared to nongated images (sharpness: 2.3 ± 0.4 vs. 1.8 ± 0.5, CNR: 8.5 ± 2.0 vs. 7.2 ± 2.0, score: 2.6 ± 1.2 vs. 1.8 ± 1.0).

CONCLUSION

Artifacts caused by the employed motion tasks deteriorated image quality in the nongated scans. These artifacts were alleviated with the proposed FID-navigator.

Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project

The Human Connectome Project (HCP) relies primarily on three complementary magnetic resonance (MR) methods. These are: 1) resting state functional MR imaging (rfMRI) which uses correlations in the temporal fluctuations in an fMRI time series to deduce 'functional connectivity'; 2) diffusion imaging (dMRI), which provides the input for tractography algorithms used for the reconstruction of the complex axonal fiber architecture; and 3) task based fMRI (tfMRI), which is employed to identify functional parcellation in the human brain in order to assist analyses of data obtained with the first two methods. We describe technical improvements and optimization of these methods as well as instrumental choices that impact speed of acquisition of fMRI and dMRI images at 3T, leading to whole brain coverage with 2 mm isotropic resolution in 0.7 s for fMRI, and 1.25 mm isotropic resolution dMRI data for tractography analysis with three-fold reduction in total dMRI data acquisition time. Ongoing technical developments and optimization for acquisition of similar data at 7 T magnetic field are also presented, targeting higher spatial resolution, enhanced specificity of functional imaging signals, mitigation of the inhomogeneous radio frequency (RF) fields, and reduced power deposition. Results demonstrate that overall, these approaches represent a significant advance in MR imaging of the human brain to investigate brain function and structure.

DC-gated high resolution three-dimensional lung imaging during free-breathing

PURPOSE

To use the acquisition of the k-space center signal (DC signal) implemented into a Cartesian three-dimensional (3D) FLASH sequence for retrospective respiratory self-gating and, thus, for the examination of the whole human lung in high spatial resolution during free breathing.

MATERIALS AND METHODS

Volunteer as well as patient measurements were performed under free breathing conditions. The DC signal is acquired after the actual image data acquisition within each excitation of a 3D FLASH sequence. The DC signal is then used to track respiratory motion for retrospective respiratory gating.

RESULTS

It is shown that the acquisition of the DC signal after the imaging module can be used in a 3D FLASH sequence to extract respiratory motion information for retrospective respiratory self-gating and allows for shorter echo times (TE) and therefore increased lung parenchyma SNR.

CONCLUSION

The acquisition of the DC signal after image signal acquisition allows successful retrospective gating, enabling the reconstruction of high resolution images of the whole human lung under free breathing conditions.

Prospective motion correction in brain imaging: a review

Motion correction in magnetic resonance imaging by real-time adjustment of the imaging pulse sequence was first proposed more than 20 years ago. Recent advances have resulted from combining real-time correction with new navigator and external tracking mechanisms capable of quantifying rigid-body motion in all 6 degrees of freedom. The technique is now often referred to as "prospective motion correction." This article describes the fundamentals of prospective motion correction and reviews the latest developments in its application to brain imaging and spectroscopy. Although emphasis is placed on the brain as the organ of interest, the same principles apply whenever the imaged object can be approximated as a rigid body. Prospective motion correction can be used with most MR sequences, so it has potential to make a large impact in clinical routine. To maximize the benefits obtained from the technique, there are, however, several challenges still to be met. These include practical implementation issues, such as obtaining tracking data with minimal delay, and more fundamental problems, such as the magnetic field distortions caused by a moving object. This review discusses these challenges and summarizes the state of the art. We hope that this work will motivate further developments in prospective motion correction and help the technique to reach its full potential.

Retrospective image correction in the presence of nonlinear temporal magnetic field changes using multichannel navigator echoes

Spatio-temporal magnetic field changes in the brain caused by breathing or body movements can lead to image artifacts. This is especially a problem in T(2)(*)-weighted sequences. With the acquisition of an extra echo (navigator), it is possible to measure the magnetic field change induced frequency offset for a given slice during image acquisition. However, substantial local variation across a slice can occur. This work describes an extension of the conventional navigator technique that improves the estimation of the magnetic field distribution in the brain during strong field fluctuations. This is done using the combination of signals from multiple coil elements, the coil sensitivity profiles, and frequency encoding: termed sensitivity-encoded navigator echoes. In vivo validation was performed in subjects who performed normal breathing, nose touching, and deep breathing during scanning. The sensitivity-encoded navigator technique leads to an error reduction in estimating the field distribution in the brain of 73% ± 16% compared with 56% ± 14% for conventional estimation. Image quality can be improved via incorporating this navigator information appropriately into the image reconstruction. When the sensitivity-encoded navigator technique was applied to a T(2)(*)-weighted sequence at 7 T, a ghosting reduction of 47% ± 13% was measured during nose touching experiments compared with no correction.

Prospective motion correction with continuous gradient updates in diffusion weighted imaging

Despite the existence of numerous motion correction methods, head motion during MRI continues to be a major source of artifacts and can greatly reduce image quality. This applies particularly to diffusion weighted imaging, where strong gradients are applied during long encoding periods. These are necessary to encode microscopic movements. However, they also make the technique highly sensitive to bulk motion. In this work, we present a prospective motion correction method where all applied gradients are adjusted continuously to compensate for changes of the object position and ensure the desired phase evolution in the image coordinate frame. Additionally, in phantom experiments this new technique is used to reproduce motion artifacts with high accuracy by changing the position of the imaging frame relative to the measured object. In vivo measurements demonstrate the validity of the new correction method.

Prospective and retrospective motion correction in diffusion magnetic resonance imaging of the human brain

Diffusion-weighting in magnetic resonance imaging (MRI) increases the sensitivity to molecular Brownian motion, providing insight in the micro-environment of the underlying tissue types and structures. At the same time, the diffusion weighting renders the scans sensitive to other motion, including bulk patient motion. Typically, several image volumes are needed to extract diffusion information, inducing also inter-volume motion susceptibility. Bulk motion is more likely during long acquisitions, as they appear in diffusion tensor, diffusion spectrum and q-ball imaging. Image registration methods are successfully used to correct for bulk motion in other MRI time series, but their performance in diffusion-weighted MRI is limited since diffusion weighting introduces strong signal and contrast changes between serial image volumes. In this work, we combine the capability of free induction decay (FID) navigators, providing information on object motion, with image registration methodology to prospectively--or optionally retrospectively--correct for motion in diffusion imaging of the human brain. Eight healthy subjects were instructed to perform small-scale voluntary head motion during clinical diffusion tensor imaging acquisitions. The implemented motion detection based on FID navigator signals is processed in real-time and provided an excellent detection performance of voluntary motion patterns even at a sub-millimetre scale (sensitivity≥92%, specificity>98%). Motion detection triggered an additional image volume acquisition with b=0 s/mm2 which was subsequently co-registered to a reference volume. In the prospective correction scenario, the calculated motion-parameters were applied to perform a real-time update of the gradient coordinate system to correct for the head movement. Quantitative analysis revealed that the motion correction implementation is capable to correct head motion in diffusion-weighted MRI to a level comparable to scans without voluntary head motion. The results indicate the potential of this method to improve image quality in diffusion-weighted MRI, a concept that can also be applied when highest diffusion weightings are performed.