Prospective slice-by-slice motion correction reduces false positive activations in fMRI with task-correlated motion

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired blood oxygenation level dependent (BOLD) signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example, in a chronic stroke cohort with varying stroke location and degree of tissue damage.

Denoising task-correlated head motion from motor-task fMRI data with multi-echo ICA

Motor-task functional magnetic resonance imaging (fMRI) is crucial in the study of several clinical conditions, including stroke and Parkinson's disease. However, motor-task fMRI is complicated by task-correlated head motion, which can be magnified in clinical populations and confounds motor activation results. One method that may mitigate this issue is multi-echo independent component analysis (ME-ICA), which has been shown to separate the effects of head motion from the desired BOLD signal but has not been tested in motor-task datasets with high amounts of motion. In this study, we collected an fMRI dataset from a healthy population who performed a hand grasp task with and without task-correlated amplified head motion to simulate a motor-impaired population. We analyzed these data using three models: single-echo (SE), multi-echo optimally combined (ME-OC), and ME-ICA. We compared the models' performance in mitigating the effects of head motion on the subject level and group level. On the subject level, ME-ICA better dissociated the effects of head motion from the BOLD signal and reduced noise. Both ME models led to increased t-statistics in brain motor regions. In scans with high levels of motion, ME-ICA additionally mitigated artifacts and increased stability of beta coefficient estimates, compared to SE. On the group level, all three models produced activation clusters in expected motor areas in scans with both low and high motion, indicating that group-level averaging may also sufficiently resolve motion artifacts that vary by subject. These findings demonstrate that ME-ICA is a useful tool for subject-level analysis of motor-task data with high levels of task-correlated head motion. The improvements afforded by ME-ICA are critical to improve reliability of subject-level activation maps for clinical populations in which group-level analysis may not be feasible or appropriate, for example in a chronic stroke cohort with varying stroke location and degree of tissue damage.

Simultaneous multislice EPI prospective motion correction by real-time receiver phase correction and coil sensitivity map interpolation

PURPOSE

To improve the image reconstruction for prospective motion correction (PMC) of simultaneous multislice (SMS) EPI of the brain, an update of receiver phase and resampling of coil sensitivities are proposed and evaluated.

METHODS

A camera-based system was used to track head motion (3 translations and 3 rotations) and dynamically update the scan position and orientation. We derived the change in receiver phase associated with a shifted field of view (FOV) and applied it in real-time to each k-space line of the EPI readout trains. Second, for the SMS reconstruction, we adapted resampled coil sensitivity profiles reflecting the movement of slices. Single-shot gradient-echo SMS-EPI scans were performed in phantoms and human subjects for validation.

RESULTS

Brain SMS-EPI scans in the presence of motion with PMC and no phase correction for scan plane shift showed noticeable artifacts. These artifacts were visually and quantitatively attenuated when corrections were enabled. Correcting misaligned coil sensitivity maps improved the temporal SNR (tSNR) of time series by 24% (p = 0.0007) for scans with large movements (up to ˜35 mm and 30°). Correcting the receiver phase improved the tSNR of a scan with minimal head movement by 50% from 50 to 75 for a United Kingdom biobank protocol.

CONCLUSION

Reconstruction-induced motion artifacts in single-shot SMS-EPI scans acquired with PMC can be removed by dynamically adjusting the receiver phase of each line across EPI readout trains and updating coil sensitivity profiles during reconstruction. The method may be a valuable tool for SMS-EPI scans in the presence of subject motion.

Camera-based Prospective Motion Correction in Paediatric Epilepsy Patients Enables EEG-fMRI Localization Even in High-motion States

BACKGROUND

EEG-fMRI is a useful additional test to localize the epileptogenic zone (EZ) particularly in MRI negative cases. However subject motion presents a particular challenge owing to its large effects on both MRI and EEG signal. Traditionally it is assumed that prospective motion correction (PMC) of fMRI precludes EEG artifact correction.

METHODS

Children undergoing presurgical assessment at Great Ormond Street Hospital were included into the study. PMC of fMRI was done using a commercial system with a Moiré Phase Tracking marker and MR-compatible camera. For retrospective EEG correction both a standard and a motion educated EEG artefact correction (REEGMAS) were compared to each other.

RESULTS

Ten children underwent simultaneous EEG-fMRI. Overall head movement was high (mean RMS velocity < 1.5 mm/s) and showed high inter- and intra-individual variability. Comparing motion measured by the PMC camera and the (uncorrected residual) motion detected by realignment of fMRI images, there was a five-fold reduction in motion from its prospective correction. Retrospective EEG correction using both standard approaches and REEGMAS allowed the visualization and identification of physiological noise and epileptiform discharges. Seven of 10 children had significant maps, which were concordant with the clinical EZ hypothesis in 6 of these 7.

CONCLUSION

To our knowledge this is the first application of camera-based PMC for MRI in a pediatric clinical setting. Despite large amount of movement PMC in combination with retrospective EEG correction recovered data and obtained clinically meaningful results during high levels of subject motion. Practical limitations may currently limit the widespread use of this technology.

Attenuation of motion artifacts in fMRI using discrete reconstruction of irregular fMRI trajectories (DRIFT)

PURPOSE

Numerous studies report motion as the most detrimental source of noise and artifacts in fMRI. Current motion correction methods fail to completely address the motion problem. Retrospective techniques such as spatial realignment can correct for between-volume misalignment but fail to address within volume contamination and spin-history artifacts. Prospective motion correction can prevent spin-history artifacts but currently cannot update the gradients fast enough to remove k-space filling artifacts, calling for a hybrid approach to fully address these problems.

THEORY AND METHODS

Motion can be mathematically formulated into the MR signal equation to describe the motion artifacts at their origin in k-space. From these equations, it is demonstrated that different motions have different effects on the signal. A novel motion correction algorithm is designed from these equations to remove motion-induced artifacts directly in k-space, discrete reconstruction of irregular fMRI trajectory (DRIFT). This method is evaluated rigorously using fMRI simulations and data from a rotating phantom inside the scanner.

RESULTS

The results indicate that although some motion types have negligible effects on the MR signal, others produce catastrophic and lasting artifacts even after motion cessation. In simulation, DRIFT is able to remove motion artifacts in the absence of spin history. In a phantom scan, DRIFT significantly attenuates the motion artifacts in the fMRI data.

CONCLUSION

Neither prospective nor retrospective motion correction methods could completely remove the motion artifacts from the fMRI data. However, DRIFT, as a retrospective technique, when combined with prospective motion correction, can eliminate a significant portion of motion artifacts.

ICA-based denoising strategies in breath-hold induced cerebrovascular reactivity mapping with multi echo BOLD fMRI

Performing a BOLD functional MRI (fMRI) acquisition during breath-hold (BH) tasks is a non-invasive, robust method to estimate cerebrovascular reactivity (CVR). However, movement and breathing-related artefacts caused by the BH can substantially hinder CVR estimates due to their high temporal collinearity with the effect of interest, and attention has to be paid when choosing which analysis model should be applied to the data. In this study, we evaluate the performance of multiple analysis strategies based on lagged general linear models applied on multi-echo BOLD fMRI data, acquired in ten subjects performing a BH task during ten sessions, to obtain subject-specific CVR and haemodynamic lag estimates. The evaluated approaches range from conventional regression models, i.e. including drifts and motion timecourses as nuisance regressors, applied on single-echo or optimally-combined data, to more complex models including regressors obtained from multi-echo independent component analysis with different grades of orthogonalization in order to preserve the effect of interest, i.e. the CVR. We compare these models in terms of their ability to make signal intensity changes independent from motion, as well as the reliability as measured by voxelwise intraclass correlation coefficients of both CVR and lag maps over time. Our results reveal that a conservative independent component analysis model applied on the optimally-combined multi-echo fMRI signal offers the largest reduction of motion-related effects in the signal, while yielding reliable CVR amplitude and lag estimates, although a conventional regression model applied on the optimally-combined data results in similar estimates. This work demonstrates the usefulness of multi-echo based fMRI acquisitions and independent component analysis denoising for precision mapping of CVR in single subjects based on BH paradigms, fostering its potential as a clinically-viable neuroimaging tool for individual patients. It also proves that the way in which data-driven regressors should be incorporated in the analysis model is not straight-forward due to their complex interaction with the BH-induced BOLD response.

SLIMM: Slice localization integrated MRI monitoring

Functional MRI (fMRI) is extremely challenging to perform in subjects who move because subject motion disrupts blood oxygenation level dependent (BOLD) signal measurement. It has become common to use retrospective framewise motion detection and censoring in fMRI studies to eliminate artifacts arising from motion. Data censoring results in significant loss of data and statistical power unless the data acquisition is extended to acquire more data not corrupted by motion. Acquiring more data than is necessary leads to longer than necessary scan duration, which is more expensive and may lead to additional subject non-compliance. Therefore, it is well established that real-time prospective motion monitoring is crucial to ensure data quality and reduce imaging costs. In addition, real-time monitoring of motion allows for feedback to the operator and the subject during the acquisition, to enable intervention to reduce the subject motion. The most widely used form of motion monitoring for fMRI is based on volume-to-volume registration (VVR), which quantifies motion as the misalignment between subsequent volumes. However, motion is not constrained to occur only at the boundaries of volume acquisition, but instead may occur at any time. Consequently, each slice of an fMRI acquisition may be displaced by motion, and assessment of whole volume to volume motion may be insensitive to both intra-volume and inter-volume motion that is revealed by displacement of the slices. We developed the first slice-by-slice self-navigated motion monitoring system for fMRI by developing a real-time slice-to-volume registration (SVR) algorithm. Our real-time SVR algorithm, which is the core of the system, uses a local image patch-based matching criterion along with a Levenberg-Marquardt optimizer, all accelerated via symmetric multi-processing, with interleaved and simultaneous multi-slice acquisition schemes. Extensive experimental results on real motion data demonstrated that our fast motion monitoring system, named Slice Localization Integrated MRI Monitoring (SLIMM), provides more accurate motion measurements than a VVR based approach. Therefore, SLIMM offers improved online motion monitoring which is particularly important in fMRI for challenging patient populations. Real-time motion monitoring is crucial for online data quality control and assurance, for enabling feedback to the subject and the operator to act to mitigate motion, and in adaptive acquisition strategies that aim to ensure enough data of sufficient quality is acquired without acquiring excess data.

Improved motion correction of submillimetre 7T fMRI time series with Boundary-Based Registration (BBR)

Ultra-high field functional magnetic resonance imaging (fMRI) has allowed us to acquire images with submillimetre voxels. However, in order to interpret the data clearly, we need to accurately correct head motion and the resultant distortions. Here, we present a novel application of Boundary Based Registration (BBR) to realign functional Magnetic Resonance Imaging (fMRI) data and evaluate its effectiveness on a set of 7T submillimetre data, as well as millimetre 3T data for comparison. BBR utilizes the boundary information from high contrast present in structural data to drive registration of functional data to the structural data. In our application, we realign each functional volume individually to the structural data, effectively realigning them to each other. In addition, this realignment method removes the need for a secondary aligning of functional data to structural data for purposes such as laminar segmentation or registration to data from other scanners. We demonstrate that BBR realignment outperforms standard realignment methods across a variety of data analysis methods. For instance, the method results in a 15% increase in linear discriminant contrast, a cross-validated estimate of multivariate discriminability. Further analysis shows that this benefit is an inherent property of the BBR cost function and not due to the difference in target volume. Our results show that BBR realignment is able to accurately correct head motion in 7T data and can be utilized in preprocessing pipelines to improve the quality of 7T data.

Prospective motion correction improves the sensitivity of fMRI pattern decoding

We evaluated the effectiveness of prospective motion correction (PMC) on a simple visual task when no deliberate subject motion was present. The PMC system utilizes an in‐bore optical camera to track an external marker attached to the participant via a custom‐molded mouthpiece. The study was conducted at two resolutions (1.5 mm vs 3 mm) and under three conditions (PMC On and Mouthpiece On vs PMC Off and Mouthpiece On vs PMC Off and Mouthpiece Off). Multiple data analysis methods were conducted, including univariate and multivariate approaches, and we demonstrated that the benefit of PMC is most apparent for multi‐voxel pattern decoding at higher resolutions. Additional testing on two participants showed that our inexpensive, commercially available mouthpiece solution produced comparable results to a dentist‐molded mouthpiece. Our results showed that PMC is increasingly important at higher resolutions for analyses that require accurate voxel registration across time.

Prospective motion correction using coil-mounted cameras: Cross-calibration considerations

PURPOSE

Optical prospective motion correction substantially reduces sensitivity to motion in neuroimaging of human subjects. However, a major barrier to clinical deployment has been the time-consuming cross-calibration between the camera and MRI scanner reference frames. This work addresses this challenge.

METHODS

A single camera was mounted onto the head coil for tracking head motion. Two new methods were developed: (1) a rapid calibration method for camera-to-scanner cross-calibration using a custom-made tool incorporating wireless active markers, and (2) a calibration adjustment method to compensate for table motion between scans. Both methods were tested at 1.5T and 3T in vivo. Simulations were performed to determine the required mechanical tolerance for repositioning of the camera.

RESULTS

The rapid calibration method is completed in a short (<30 s) scan, which is carried out only once per installation. The calibration adjustment method requires no extra scan time and runs automatically whenever the system is used. The mechanical tolerance analysis indicates that most motion (90% reduction in voxel displacement) could be corrected even with far larger camera repositioning errors than are observed in practice.

CONCLUSION

The methods presented here allow calibration of sufficient quality to be carried out and maintained with no additional technologist workload. Magn Reson Med 79:1911-1921, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Methods for cleaning the BOLD fMRI signal

Blood oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI) has rapidly become a popular technique for the investigation of brain function in healthy individuals, patients as well as in animal studies. However, the BOLD signal arises from a complex mixture of neuronal, metabolic and vascular processes, being therefore an indirect measure of neuronal activity, which is further severely corrupted by multiple non-neuronal fluctuations of instrumental, physiological or subject-specific origin. This review aims to provide a comprehensive summary of existing methods for cleaning the BOLD fMRI signal. The description is given from a methodological point of view, focusing on the operation of the different techniques in addition to pointing out the advantages and limitations in their application. Since motion-related and physiological noise fluctuations are two of the main noise components of the signal, techniques targeting their removal are primarily addressed, including both data-driven approaches and using external recordings. Data-driven approaches, which are less specific in the assumed model and can simultaneously reduce multiple noise fluctuations, are mainly based on data decomposition techniques such as principal and independent component analysis. Importantly, the usefulness of strategies that benefit from the information available in the phase component of the signal, or in multiple signal echoes is also highlighted. The use of global signal regression for denoising is also addressed. Finally, practical recommendations regarding the optimization of the preprocessing pipeline for the purpose of denoising and future venues of research are indicated. Through the review, we summarize the importance of signal denoising as an essential step in the analysis pipeline of task-based and resting state fMRI studies.

Prospective motion correction in functional MRI

Due to the intrinsic low sensitivity of BOLD-fMRI long scanning is required. Subject motion during fMRI scans reduces statistical significance of the activation maps and increases the prevalence of false activations. Motion correction is therefore an essential tool for a successful fMRI data analysis. Retrospective motion correction techniques are now commonplace and are incorporated into a wide range of fMRI analysis toolboxes. These techniques are advantageous due to robustness, sequence independence and have minimal impact on the fMRI study setup. Retrospective techniques however, do not provide an accurate intra-volume correction, nor can these techniques correct for the spin-history effects. The application of prospective motion correction in fMRI appears to be effective in reducing false positives and increasing sensitivity when compared to retrospective techniques, particularly in the cases of substantial motion. Especially advantageous in this regard is the combination of prospective motion correction with dynamic distortion correction. Nevertheless, none of the recent methods are able to recover activations in presence of motion that are comparable to no-motion conditions, which motivates further research in the area of adaptive dynamic imaging.

Reduction of Motion Artifacts and Noise Using Independent Component Analysis in Task-Based Functional MRI for Preoperative Planning in Patients with Brain Tumor

BACKGROUND AND PURPOSE

Although it is a potentially powerful presurgical tool, fMRI can be fraught with artifacts, leading to interpretive errors, many of which are not fully accounted for in routinely applied correction methods. The purpose of this investigation was to evaluate the effects of data denoising by independent component analysis in patients undergoing preoperative evaluation for glioma resection compared with more routinely applied correction methods such as realignment or motion scrubbing.

MATERIALS AND METHODS

Thirty-five functional runs (both motor and language) in 12 consecutive patients with glioma were analyzed retrospectively by double-blind review. Data were processed and compared with the following: 1) realignment alone, 2) motion scrubbing, 3) independent component analysis denoising, and 4) both independent component analysis denoising and motion scrubbing. Primary outcome measures included a change in false-positives, false-negatives, z score, and diagnostic rating.

RESULTS

Independent component analysis denoising reduced false-positives in 63% of studies versus realignment alone. There was also an increase in the z score in areas of true activation in 71.4% of studies. Areas of new expected activation (previous false-negatives) were revealed in 34.4% of cases with independent component analysis denoising versus motion scrubbing or realignment alone. Of studies deemed nondiagnostic with realignment or motion scrubbing alone, 65% were considered diagnostic after independent component analysis denoising.

CONCLUSIONS

The addition of independent component analysis denoising of fMRI data in preoperative patients with glioma has a significant impact on data quality, resulting in reduced false-positives and an increase in true-positives compared with more commonly applied motion scrubbing or simple realignment methods.

Combining EPI and motion correction for fMRI human brain images with big motion

Motion correction is an important component in fMRI brain image analysis. Linear registration technique is mostly used in the process based on the assumption that there is not any shape changes of human brain during imaging process. Echo planar imaging (EPI) technique has been widely adapted in fMRI imaging to shorten encoding duration and increase temporal resolution. However, due to the magnetic field inhomogeneity caused by tissues, shape distortion and signal intensity lose are brought into fMRI images by the technique. On the other hand, subject's pose in scanner has a effect on magnetic field inhomogeneity, so the EPI distortions are subject to head movement, especially when the movement is big. As a result, most current motion correction techniques, which are based on rigid registration, cannot handle the problem. In this paper, a technique that combines EPI distortion correction and motion correction to handle the above-mentioned problem is proposed. Since it is almost impossible to obtain ground truth at present, a task-related fMRI BOLD time course image with big motion is selected as experimental material to test its performance. The image is pre-processed with the proposed EPI-motion correction scheme then analyzed by FSL feat tool. Compared with another process with only motion correction and FSL feat analysis, the experimental result using the proposed method has no false activation detection. It is suggested the proposed EPI-motion correction scheme has the ability to handle the fMRI human brain images with big motion.

A robust method for suppressing motion-induced coil sensitivity variations during prospective correction of head motion in fMRI

Prospective motion correction is a promising candidate solution to suppress the effects of head motion during fMRI, ideally allowing the imaging plane to remain fixed with respect to the moving head. Residual signal artifacts may remain, however, because head motion in relation to a fixed multi-channel receiver coil (with non-uniform sensitivity maps) can potentially introduce unwanted signal variations comparable to the weak fMRI BOLD signal (~1%-4% at 1.5-3.0T). The present work aimed to investigate the magnitude of these residual artifacts, and characterize the regime over which prospective motion correction benefits from adjusting sensitivity maps to reflect relative positional change between the head and the coil. Numerical simulations were used to inform human fMRI experiments. The simulations indicated that for axial imaging within a commonly used 12-channel head coil, 5° of head rotation in-plane produced artifact signal changes of ~3%. Subsequently, six young adults were imaged with and without overt head motions of approximately this extent, with and without prospective motion correction using the Prospective Acquisition CorrEction (PACE) method, and with and without sensitivity map adjustments. Sensitivity map adjustments combined with PACE strongly protected against the artifacts of interest, as indicated by comparing three metrics of data quality (number of activated voxels, Dice coefficient of activation overlap, temporal standard deviation of baseline fMRI timeseries data) across the different experimental conditions. It is concluded that head motion in relation to a fixed multi-channel coil can adversely affect fMRI with prospective motion correction, and that sensitivity map adjustment can mitigate this effect at 3.0T.

Quantitative Assessment of Head Motion toward Functional Magnetic Resonance Imaging during Stepping

Purpose:

Stepping motions have been often used as gait-like patterns in functional magnetic resonance imaging (fMRI) to understand gait control. However, it is still very difficult to stabilize the task-related head motion. Our main purpose is to provide characteristics of the task-related head motion during stepping to develop robust restraints toward fMRI.

Methods:

Multidirectional head and knee position during stepping were acquired using a motion capture system outside MRI room in 13 healthy participants. Six phases in a stepping motion were defined by reference to the left knee angles and the mean of superior-inferior head velocity (V_mean) in each phase was investigated. Furthermore, the correlation between the standard deviation of the knee angle (θ_sd) and the maximum of the head velocity (V_max) was evaluated.

Results:

The standard deviation of each superior-inferior head position and pitch were significantly larger than the other measurements. V_mean showed a characteristic repeating pattern associated with the knee angle. Additionally, there were significant correlations between θ_sd and V_max.

Conclusions:

This is the first report to reveal the characteristics of the task-related head motion during stepping. Our findings are an essential step in the development of robust restraint toward fMRI during stepping task.

Interactions between head motion and coil sensitivity in accelerated fMRI

BACKGROUND

Parallel imaging is widely adopted to accelerate functional MRI (fMRI) data acquisition, through various strategies that involve multi-channel receiver coils. However, the non-uniform spatial sensitivity of multi-channel receiver coils may introduce unwanted artifacts when head motion occurs during the few-minute long fMRI scans. Although prospective correction provides a promising solution for alleviating the head motion artifacts in fMRI, the relative position of the fixed multi-channel receiver coils moves in the moving reference frame, potentially resulting in artifactual signal.

NEW METHOD

We used numerical simulations to investigate this effect on fMRI using two parallel imaging schemes: sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA) with acceleration factors 2 and 4, towards characterizing the regime over which parallel-imaging fMRI with prospective motion correction will benefit from updating coil sensitivities to reflect relative positional change between the head and the receiver coil. Moreover, six subjects were scanned with acceleration factors 2 and 4 while performing a simple finger-tapping task with and without overt head motion.

RESULTS

Updating coil sensitivities showed significant positive impact on standard deviation and activation maps in presence of overt head motion compared to that obtained with no overt head motion.

COMPARISON WITH EXISTING METHODS

The parallel imaging fMRI with updated coil sensitivity maps were compared to that with the coil sensitivity maps acquired at the reference position.

CONCLUSIONS

Head motion in relation to a fixed multi-channel coil can adversely affect the quality of parallel imaging fMRI data; and updating coil sensitivity map can mitigate this effect.

Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts

Functional magnetic resonance imaging (fMRI) studies that require high-resolution whole-brain coverage have long scan times that are primarily driven by the large number of thin slices acquired. Two-dimensional multiband echo-planar imaging (EPI) sequences accelerate the data acquisition along the slice direction and therefore represent an attractive approach to such studies by improving the temporal resolution without sacrificing spatial resolution. In this work, a 2D multiband EPI sequence was optimized for 1.5 mm isotropic whole-brain acquisitions at 3 T with 10 healthy volunteers imaged while performing simultaneous visual and motor tasks. The performance of the sequence was evaluated in terms of BOLD sensitivity and false-positive activation at multiband (MB) factors of 1, 2, 4, and 6, combined with in-plane GRAPPA acceleration of 2 × (GRAPPA 2), and the two reconstruction approaches of Slice-GRAPPA and Split Slice-GRAPPA. Sensitivity results demonstrate significant gains in temporal signal-to-noise ratio (tSNR) and t-score statistics for MB 2, 4, and 6 compared to MB 1. The MB factor for optimal sensitivity varied depending on anatomical location and reconstruction method. When using Slice-GRAPPA reconstruction, evidence of false-positive activation due to signal leakage between simultaneously excited slices was seen in one instance, 35 instances, and 70 instances over the ten volunteers for the respective accelerations of MB 2 × GRAPPA 2, MB 4 × GRAPPA 2, and MB 6 × GRAPPA 2. The use of Split Slice-GRAPPA reconstruction suppressed the prevalence of false positives significantly, to 1 instance, 5 instances, and 5 instances for the same respective acceleration factors. Imaging protocols using an acceleration factor of MB 2 × GRAPPA 2 can be confidently used for high-resolution whole-brain imaging to improve BOLD sensitivity with very low probability for false-positive activation due to slice leakage. Imaging protocols using higher acceleration factors (MB 3 or MB 4 × GRAPPA 2) can likely provide even greater gains in sensitivity but should be carefully optimized to minimize the possibility of false activations.

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MB factors 1, 2, 4, and 6 and two reconstructions were evaluated for fMRI performance.

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MB accelerations 2, 4, and 6 improved BOLD sensitivity metrics over MB 1.

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False-positive activation due to signal leakage was seen at high accelerations.

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Use of Split Slice-GRAPPA reconstruction significantly reduces false positives.

The Travelling-Wave Primate System: A New Solution for Magnetic Resonance Imaging of Macaque Monkeys at 7 Tesla Ultra-High Field

Introduction

Neuroimaging of macaques at ultra-high field (UHF) is usually conducted by combining a volume coil for transmit (Tx) and a phased array coil for receive (Rx) tightly enclosing the monkey’s head. Good results have been achieved using vertical or horizontal magnets with implanted or near-surface coils. An alternative and less costly approach, the travelling-wave (TW) excitation concept, may offer more flexible experimental setups on human whole-body UHF magnetic resonance imaging (MRI) systems, which are now more widely available. Goal of the study was developing and validating the TW concept for in vivo primate MRI.

Methods

The TW Primate System (TWPS) uses the radio frequency shield of the gradient system of a human whole-body 7 T MRI system as a waveguide to propagate a circularly polarized B1 field represented by the TE11 mode. This mode is excited by a specifically designed 2-port patch antenna. For receive, a customized neuroimaging monkey head receive-only coil was designed. Field simulation was used for development and evaluation. Signal-to-noise ratio (SNR) was compared with data acquired with a conventional monkey volume head coil consisting of a homogeneous transmit coil and a 12-element receive coil.

Results

The TWPS offered good image homogeneity in the volume-of-interest Turbo spin echo images exhibited a high contrast, allowing a clear depiction of the cerebral anatomy. As a prerequisite for functional MRI, whole brain ultrafast echo planar images were successfully acquired.

Conclusion

The TWPS presents a promising new approach to fMRI of macaques for research groups with access to a horizontal UHF MRI system.

Prospective motion correction of 3D echo-planar imaging data for functional MRI using optical tracking

We evaluated the performance of an optical camera based prospective motion correction (PMC) system in improving the quality of 3D echo-planar imaging functional MRI data. An optical camera and external marker were used to dynamically track the head movement of subjects during fMRI scanning. PMC was performed by using the motion information to dynamically update the sequence's RF excitation and gradient waveforms such that the field-of-view was realigned to match the subject's head movement. Task-free fMRI experiments on five healthy volunteers followed a 2 × 2 × 3 factorial design with the following factors: PMC on or off; 3.0 mm or 1.5 mm isotropic resolution; and no, slow, or fast head movements. Visual and motor fMRI experiments were additionally performed on one of the volunteers at 1.5 mm resolution comparing PMC on vs PMC off for no and slow head movements. Metrics were developed to quantify the amount of motion as it occurred relative to k-space data acquisition. The motion quantification metric collapsed the very rich camera tracking data into one scalar value for each image volume that was strongly predictive of motion-induced artifacts. The PMC system did not introduce extraneous artifacts for the no motion conditions and improved the time series temporal signal-to-noise by 30% to 40% for all combinations of low/high resolution and slow/fast head movement relative to the standard acquisition with no prospective correction. The numbers of activated voxels (p < 0.001, uncorrected) in both task-based experiments were comparable for the no motion cases and increased by 78% and 330%, respectively, for PMC on versus PMC off in the slow motion cases. The PMC system is a robust solution to decrease the motion sensitivity of multi-shot 3D EPI sequences and thereby overcome one of the main roadblocks to their widespread use in fMRI studies.

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Data quality was assessed in terms of tSNR, RMSE, and task fMRI t-statistics.

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Correction improved tSNR by 30% to 40%.

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Percent reduction in RMSE as a function of motion level was characterized.

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Number of active voxels for motor and visual tasks increased significantly.

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.

Isolated assessment of translation or rotation severely underestimates the effects of subject motion in fMRI data

Subject motion has long since been known to be a major confound in functional MRI studies of the human brain. For resting-state functional MRI in particular, data corruption due to motion artefacts has been shown to be most relevant. However, despite 6 parameters (3 for translations and 3 for rotations) being required to fully describe the head's motion trajectory between timepoints, not all are routinely used to assess subject motion. Using structural (n = 964) as well as functional MRI (n = 200) data from public repositories, a series of experiments was performed to assess the impact of using a reduced parameter set (translation_only and rotation_only) versus using the complete parameter set. It could be shown that the usage of 65 mm as an indicator of the average cortical distance is a valid approximation in adults, although care must be taken when comparing children and adults using the same measure. The effect of using slightly smaller or larger values is minimal. Further, both translation_only and rotation_only severely underestimate the full extent of subject motion; consequently, both translation_only and rotation_only discard substantially fewer datapoints when used for quality control purposes (“motion scrubbing”). Finally, both translation_only and rotation_only severely underperform in predicting the full extent of the signal changes and the overall variance explained by motion in functional MRI data. These results suggest that a comprehensive measure, taking into account all available parameters, should be used to characterize subject motion in fMRI.

Real-time motion correction using gradient tones and head-mounted NMR field probes

PURPOSE

Sinusoidal gradient oscillations in the kilohertz range are proposed for position tracking of NMR probes and prospective motion correction for arbitrary imaging sequences without any alteration of sequence timing. The method is combined with concurrent field monitoring to robustly perform image reconstruction in the presence of potential dynamic field deviations.

METHODS

Benchmarking experiments were done to assess the accuracy and precision of the method and to compare it with theoretical predictions based on the field probe's time-dependent signal-to-noise ratio. An array of four field probes was used to perform real-time prospective motion correction in vivo. Images were reconstructed based on both predetermined and concurrently measured k-space trajectories.

RESULTS

For observation windows of 4.8 ms, the precision of probe position determination was found to be 35 to 62 µm, and the maximal measurement error was 595 µm root-mean-square on a single axis. Sequence update per repetition time on this basis yielded images free of conspicuous artifacts despite substantial head motion. Predetermined and concurrently observed k-space trajectories yielded equivalent image quality.

CONCLUSION

NMR field probes in conjunction with gradient tones permit the tracking and prospective correction of rigid-body motion. Relying on gradient oscillations in the kilohertz range, the method allows for concurrent motion detection and image encoding.