Optical tracking with two markers for robust prospective motion correction for brain imaging

Choreography Controlled (ChoCo) brain MRI artifact generation for labeled motion-corrupted datasets

MRI is a non-invasive medical imaging modality that is sensitive to patient motion, which constitutes a major limitation in most clinical applications. Solutions may arise from the reduction of acquisition times or from motion-correction techniques, either prospective or retrospective. Benchmarking the latter methods requires labeled motion-corrupted datasets, which are uncommon. Up to our best knowledge, no protocol for generating labeled datasets of MRI images corrupted by controlled motion has yet been proposed. Hence, we present a methodology allowing the acquisition of reproducible motion-corrupted MRI images as well as validation of the system's performance by motion estimation through rigid-body volume registration of fast 3D echo-planar imaging (EPI) time series. A proof-of-concept is presented, to show how the protocol can be implemented to provide qualitative and quantitative results. An MRI-compatible video system displays a moving target that volunteers equipped with customized plastic glasses must follow to perform predefined head choreographies. Motion estimation using rigid-body EPI time series registration demonstrated that head position can be accurately determined (with an average standard deviation of about 0.39 degrees). A spatio-temporal upsampling and interpolation method to cope with fast motion is also proposed in order to improve motion estimation. The proposed protocol is versatile and straightforward. It is compatible with all MRI systems and may provide insights on the origins of specific motion artifacts. The MRI and artificial intelligence research communities could benefit from this work to build in-vivo labeled datasets of motion-corrupted MRI images suitable for training/testing any retrospective motion correction or machine learning algorithm.

Comparison of prospective and retrospective motion correction in 3D-encoded neuroanatomical MRI

PURPOSE

To compare prospective motion correction (PMC) and retrospective motion correction (RMC) in Cartesian 3D-encoded MPRAGE scans and to investigate the effects of correction frequency and parallel imaging on the performance of RMC.

METHODS

Head motion was estimated using a markerless tracking system and sent to a modified MPRAGE sequence, which can continuously update the imaging FOV to perform PMC. The prospective correction was applied either before each echo train (before-ET) or at every sixth readout within the ET (within-ET). RMC was applied during image reconstruction by adjusting k-space trajectories according to the measured motion. The motion correction frequency was retrospectively increased with RMC or decreased with reverse RMC. Phantom and in vivo experiments were used to compare PMC and RMC, as well as to compare within-ET and before-ET correction frequency during continuous motion. The correction quality was quantitatively evaluated using the structural similarity index measure with a reference image without motion correction and without intentional motion.

RESULTS

PMC resulted in superior image quality compared to RMC both visually and quantitatively. Increasing the correction frequency from before-ET to within-ET reduced the motion artifacts in RMC. A hybrid PMC and RMC correction, that is, retrospectively increasing the correction frequency of before-ET PMC to within-ET, also reduced motion artifacts. Inferior performance of RMC compared to PMC was shown with GRAPPA calibration data without intentional motion and without any GRAPPA acceleration.

CONCLUSION

Reductions in local Nyquist violations with PMC resulted in superior image quality compared to RMC. Increasing the motion correction frequency to within-ET reduced the motion artifacts in both RMC and PMC.

Quantitative evaluation of prospective motion correction in healthy subjects at 7T MRI

PURPOSE

Quantitative assessment of prospective motion correction (PMC) capability at 7T MRI for compliant healthy subjects to improve high-resolution images in the absence of intentional motion.

METHODS

Twenty-one healthy subjects were imaged at 7 T. They were asked not to move, to consider only unintentional motion. An in-bore optical tracking system was used to monitor head motion and consequently update the imaging volume. For all subjects, high-resolution T₁ (3D-MPRAGE), T₂ (2D turbo spin echo), proton density (2D turbo spin echo), and T2∗ (2D gradient echo) weighted images were acquired with and without PMC. The images were evaluated through subjective and objective analysis.

RESULTS

Subjective evaluation overall has shown a statistically significant improvement (5.5%) in terms of image quality with PMC ON. In a separate evaluation of every contrast, three of the four contrasts (T₁ , T₂ , and proton density) have shown a statistically significant improvement (9.62%, 9.85%, and 9.26%), whereas the fourth one ( T2∗ ) has shown improvement, although not statistically significant. In the evaluation with objective metrics, average edge strength has shown an overall improvement of 6% with PMC ON, which was statistically significant; and gradient entropy has shown an overall improvement of 2%, which did not reach statistical significance.

CONCLUSION

Based on subjective assessment, PMC improved image quality in high-resolution images of healthy compliant subjects in the absence of intentional motion for all contrasts except T2∗ , in which no significant differences were observed. Quantitative metrics showed an overall trend for an improvement with PMC, but not all differences were significant.

Unified Retrospective EEG Motion Educated Artefact Suppression for EEG-fMRI to Suppress Magnetic Field Gradient Artefacts During Motion

The data quality of simultaneously acquired electroencephalography and functional magnetic resonance imaging (EEG-fMRI) can be strongly affected by motion. Recent work has shown that the quality of fMRI data can be improved by using a Moiré-Phase-Tracker (MPT)-camera system for prospective motion correction. The use of the head position acquired by the MPT-camera-system has also been shown to correct motion-induced voltages, ballistocardiogram (BCG) and gradient artefact residuals separately. In this work we show the concept of an integrated framework based on the general linear model to provide a unified motion informed model of in-MRI artefacts. This model (retrospective EEG motion educated gradient artefact suppression, REEG-MEGAS) is capable of correcting voltage-induced, BCG and gradient artefact residuals of EEG data acquired simultaneously with prospective motion corrected fMRI. In our results, we have verified that applying REEG-MEGAS correction to EEG data acquired during subject motion improves the data quality in terms of motion induced voltages and also GA residuals in comparison to standard Artefact Averaging Subtraction and Retrospective EEG Motion Artefact Suppression. Besides that, we provide preliminary evidence that although adding more regressors to a model may slightly affect the power of physiological signals such as the alpha-rhythm, its application may increase the overall quality of a dataset, particularly when strongly affected by motion. This was verified by analysing the EEG traces, power spectra density and the topographic distribution from two healthy subjects. We also have verified that the correction by REEG-MEGAS improves higher frequency artefact correction by decreasing the power of Gradient Artefact harmonics. Our method showed promising results for decreasing the power of artefacts for frequencies up to 250 Hz. Additionally, REEG-MEGAS is a hybrid framework that can be implemented for real time prospective motion correction of EEG and fMRI data. Among other EEG-fMRI applications, the approach described here may benefit applications such as EEG-fMRI neurofeedback and brain computer interface, which strongly rely on the prospective acquisition and application of motion artefact removal.

Improved prospective frequency correction for macromolecule-suppressed GABA editing with metabolite cycling at 3T

PURPOSE

To combine metabolite cycling with J-difference editing (MC MEGA) to allow for prospective frequency correction at each transient without additional acquisitions and compare it to water-suppressed MEGA-PRESS (WS MEGA) editing with intermittent prospective frequency correction.

METHODS

Macromolecule-suppressed gamma aminobutyric acid (GABA)-edited experiments were performed in a phantom and in the occipital lobe (OCC) (n = 12) and medial prefrontal cortex (mPFC) (n = 8) of the human brain. Water frequency consistency and average offset over acquisition time were compared. GABA multiplet patterns, signal intensities, and choline subtraction artifacts were evaluated. In vivo GABA concentrations were compared and related to frequency offset in the OCC.

RESULTS

MC MEGA was more stable with 21% and 32% smaller water frequency SDs in the OCC and mPFC, respectively. MC MEGA also had 39% and 40% smaller average frequency offsets in the OCC and mPFC, respectively. Phantom GABA multiplet patterns and signal intensities were similar. In vivo GABA concentrations were smaller in MC MEGA than in WS MEGA, with median (interquartile range) of 2.52 (0.27) and 2.29 (0.19) institutional units (i.u.), respectively in the OCC scans without prior DTI, and 0.99 (0.3) and 1.72 (0.5), respectively in the mPFC. OCC WS MEGA GABA concentrations, but not MC MEGA GABA concentrations were moderately correlated with frequency offset. mPFC WS MEGA spectra contained significantly more subtraction artifacts than MC MEGA spectra.

CONCLUSION

MC MEGA is feasible and allows for prospective frequency correction at every transient. MC MEGA GABA concentrations were not biased by frequency offsets and contained less subtraction artifacts compared to WS MEGA.

Combining prospective and retrospective motion correction based on a model for fast continuous motion

PURPOSE

Prospective motion correction (PMC) and retrospective motion correction (RMC) have different advantages and limitations. The present work aims to combine the advantages of both for rigid body motion, aiming at correcting for faster motions than was previously achievable. Additionally, it provides insights into the effects of motion on pulse sequences and MR signals with a goal of further improving motion correction in the future.

METHODS

The effective encoding trajectory and a global phase offset in a moving object are calculated based on complete gradient waveforms of an arbitrary sequence and a continuous motion model. These data are used to feed the forward signal model, which is then used in iterative image reconstruction to suppress the artifacts still present after the PMC.

RESULTS

Verification experiments with a rotation phantom and in vivo were performed. Predictions of simulated motion artifacts for PMC based on sequence waveforms are very accurate. The performance at combined PMC+RMC is limited by Nyquist violations in the sampled k-space and can be compensated by oversampling.

CONCLUSION

The combined correction results in better images than pure PMC in the presence of fast motion. The predictions of artifacts are very accurate, allowing for comparing sequences or protocols in simulations. The observed artifacts due to Nyquist violations are expected to be corrected by utilizing parallel imaging.

VALIDITY OF AN MRI-COMPATIBLE MOTION CAPTURE SYSTEM FOR USE WITH LOWER EXTREMITY NEUROIMAGING PARADIGMS

Background

Emergent linkages between musculoskeletal injury and the nervous system have increased interest to evaluate brain activity during functional movements associated with injury risk. Functional magnetic resonance imaging (fMRI) is a sophisticated modality that can be used to study brain activity during functional sensorimotor control tasks. However, technical limitations have precluded the precise quantification of lower-extremity joint kinematics during active brain scanning. The purpose of this study was to determine the validity of a new, MRI-compatible motion tracking system relative to a traditional multi-camera 3D motion capture system for measuring lower extremity joint kinematics.

Methods

Fifteen subjects (9 females, 6 males) performed knee flexion-extension and leg press movements against guided resistance while laying supine. Motion tracking data were collected simultaneously using the MRI-compatible and traditional multi-camera 3D motion systems. Participants' sagittal and frontal plane knee angles were calculated from data acquired by both multi-camera systems. Resultant range of angular movement in both measurement planes were compared between both systems. Instrument agreement was assessed using Bland-Altman plots and intraclass correlation coefficients (ICC).

Results

The system demonstrated excellent validity in the sagittal plane (ICCs>0.99) and good to excellent validity in the frontal plane (0.84 < ICCs < 0.92). Mean differences between corresponding range of angular movement measurements ranged from 0.186 ° to 0.295 °.

Conclusions

The present data indicate that this new, MRI-compatible system is valid for measuring lower extremity movements when compared to the gold standard 3D motion analysis system. As there is growing interest regarding the neural substrates of lower extremity movement, particularly in relation to injury and pathology, this system can now be integrated into neuroimaging paradigms to investigate movement biomechanics and its relation to brain activity.

Level of Evidence

3.

Prospective motion correction for diffusion weighted EPI of the brain using an optical markerless tracker

PURPOSE

To enable motion-robust diffusion weighted imaging of the brain using well-established imaging techniques.

METHODS

An optical markerless tracking system was used to estimate and correct for rigid body motion of the head in real time during scanning. The imaging coordinate system was updated before each excitation pulse in a single-shot EPI sequence accelerated by GRAPPA with motion-robust calibration. Full Fourier imaging was used to reduce effects of motion during diffusion encoding. Subjects were imaged while performing prescribed motion patterns, each repeated with prospective motion correction on and off.

RESULTS

Prospective motion correction with dynamic ghost correction enabled high quality DWI in the presence of fast and continuous motion within a 10° range. Images acquired without motion were not degraded by the prospective correction. Calculated diffusion tensors tolerated the motion well, but ADC values were slightly increased.

CONCLUSIONS

Prospective correction by markerless optical tracking minimizes patient interaction and appears to be well suited for EPI-based DWI of patient groups unable to remain still including those who are not compliant with markers.

55 Mn-based fiducial markers for rapid and automated RF coil localization for hyperpolarized 13 C MRI

PURPOSE

To use fiducial markers containing manganese 55 to rapidly localize carbon 13 (¹³ C) RF coils for correcting images for B₁ variation.

METHODS

Hollow high-density polyethylene spheres were filled with 3M sodium permanganate and affixed to a rectangular ¹³ C-tuned RF coil. The relative positions of the markers and coil conductors were mapped using CT. Marker positions were measured by MRI using a series of 1D projections and automated peak detection. Once the coil location was determined, coil sensitivity was estimated using a quasi-static calculation. Simulations were performed to determine the minimum number of projections required for robust localization. Phantom experiments were used to confirm the accuracy of marker localization as well as the calculated coil sensitivity. Finally, in vivo validation was performed using hyperpolarized ¹³ C pyruvate in a rat model.

RESULTS

In simulations, our algorithm was accurate in determining marker positions when at least 6 projections were used (RMSE 1.4 ± 0.9 mm). These estimates were verified in phantom experiments, where markers locations were determined with an RMS accuracy of 1.3 mm. A minimum SNR of 4 was required for automated detection to perform accurately. Computed coil sensitivity had a median error of 17% when taken over the entire measured area and 5.7% over a central region. In a rat, correction for nonuniform reception and flip angle was able to normalize the signals arising from asymmetrically positioned kidneys.

CONCLUSION

Manganese 55 fiducial markers are an inexpensive and reliable method for rapidly localizing ¹³ C RF coils and correcting ¹³ C images for B₁ variation without user intervention.

Prospective motion correction of fMRI: Improving the quality of resting state data affected by large head motion

The quality of functional MRI (fMRI) data is affected by head motion. It has been shown that fMRI data quality can be improved by prospectively updating the gradients and radio-frequency pulses in response to head motion during image acquisition by using an MR-compatible optical tracking system (prospective motion correction, or PMC). Recent studies showed that PMC improves the temporal Signal to Noise Ratio (tSNR) of resting state fMRI data (rs-fMRI) acquired from subjects not moving intentionally. Besides that, the time courses of Independent Components (ICs), resulting from Independent Component Analysis (ICA), were found to present significant temporal correlation with the motion parameters recorded by the camera. However, the benefits of applying PMC for improving the quality of rs-fMRI acquired under large head movements and its effects on resting state networks (RSN) and connectivity matrices are still unknown. In this study, subjects were instructed to cross their legs at will while rs-fMRI data with and without PMC were acquired, which generated head motion velocities ranging from 4 to 30 ​mm/s. We also acquired fMRI data without intentional motion. Independent component analysis of rs-fMRI was performed to evaluate IC maps and time courses of RSNs. We also calculated the temporal correlation among different brain regions and generated connectivity matrices for the different motion and PMC conditions. In our results we verified that the crossing leg movements reduced the tSNR of sessions without and with PMC by 45 and 20%, respectively, when compared to sessions without intentional movements. We have verified an interaction between head motion speed and PMC status, showing stronger attenuation of tSNR for acquisitions without PMC than for those with PMC. Additionally, the spatial definition of major RSNs, such as default mode, visual, left and right central executive networks, was improved when PMC was enabled. Furthermore, motion altered IC-time courses by decreasing power at low frequencies and increasing power at higher frequencies (typically associated with artefacts). PMC partially reversed these alterations of the power spectra. Finally, we showed that PMC provides temporal correlation matrices for data acquired under motion conditions more comparable to those obtained by fMRI sessions where subjects were instructed not to move.

Resting State fMRI: Going Through the Motions

Resting state functional magnetic resonance imaging (rs-fMRI) has become an indispensable tool in neuroscience research. Despite this, rs-fMRI signals are easily contaminated by artifacts arising from movement of the head during data collection. The artifacts can be problematic even for motions on the millimeter scale, with complex spatiotemporal properties that can lead to substantial errors in functional connectivity estimates. Effective correction methods must be employed, therefore, to distinguish true functional networks from motion-related noise. Research over the last three decades has produced numerous correction methods, many of which must be applied in combination to achieve satisfactory data quality. Subject instruction, training, and mild restraints are helpful at the outset, but usually insufficient. Improvements come from applying multiple motion correction algorithms retrospectively after rs-fMRI data are collected, although residual artifacts can still remain in cases of elevated motion, which are especially prevalent in patient populations. Although not commonly adopted at present, “real-time” correction methods are emerging that can be combined with retrospective methods and that promise better correction and increased rs-fMRI signal sensitivity. While the search for the ideal motion correction protocol continues, rs-fMRI research will benefit from good disclosure practices, such as: (1) reporting motion-related quality control metrics to provide better comparison between studies; and (2) including motion covariates in group-level analyses to limit the extent of motion-related confounds when studying group differences.

Direct modelling of gradient artifacts for EEG-fMRI denoising and motion tracking

OBJECTIVE

Simultaneous electroencephalography and functional magnetic resonance imaging recording (EEG-fMRI) has been widely used in neuroscientific and clinical research. The artifacts in the recorded EEG resulting from rapidly switching magnetic field gradients are usually corrected by average-artifact subtraction (AAS) due to their repetitive nature. But the performance of AAS is often disrupted by altered artifact waveforms across epochs, notably due to head motion.

APPROACH

Here, a method is proposed to make use of the known MR sequence gradient waveforms for a direct modelling of gradient artifacts. After accounting for filtering effects on the gradient artifacts, a continuous modulation of the gradient waveforms superimposed on the EEG signal is obtained.

MAIN RESULTS

Although a moving AAS template can adjust to slow drifts in gradient artifact variation, it fails to adapt to abrupt motion, resulting in residual noise. We demonstrate how this modelling approach can reduce motion-affected gradient artifacts without distorting the underlying neuronal signals. Moreover, the method provides useful head motion information highly correlated with motion tracked by an optical camera.

SIGNIFICANCE

Our work provides a novel way to improve gradient artifact removal in EEG-fMRI, and shows a potential to detect head motion without requiring additional hardware.

Markerless high-frequency prospective motion correction for neuroanatomical MRI

PURPOSE

To integrate markerless head motion tracking with prospectively corrected neuroanatomical MRI sequences and to investigate high-frequency motion correction during imaging echo trains.

METHODS

A commercial 3D surface tracking system, which estimates head motion by registering point cloud reconstructions of the face, was used to adapt the imaging FOV based on head movement during MPRAGE and T₂ SPACE (3D variable flip-angle turbo spin-echo) sequences. The FOV position and orientation were updated every 6 lines of k-space (< 50 ms) to enable "within-echo-train" prospective motion correction (PMC). Comparisons were made with scans using "before-echo-train" PMC, in which the FOV was updated only once per TR, before the start of each echo train (ET). Continuous-motion experiments with phantoms and in vivo were used to compare these high-frequency and low-frequency correction strategies. MPRAGE images were processed with FreeSurfer to compare estimates of brain structure volumes and cortical thickness in scans with different PMC.

RESULTS

The median absolute pose differences between markerless tracking and MR image registration were 0.07/0.26/0.15 mm for x/y/z translation and 0.06º/0.02º/0.12° for rotation about x/y/z. The PMC with markerless tracking substantially reduced motion artifacts. The continuous-motion experiments showed that within-ET PMC, which minimizes FOV encoding errors during ETs that last over 1 second, reduces artifacts compared with before-ET PMC. T₂ SPACE was found to be more sensitive to motion during ETs than MPRAGE. FreeSurfer morphometry estimates from within-ET PMC MPRAGE images were the most accurate.

CONCLUSION

Markerless head tracking can be used for PMC, and high-frequency within-ET PMC can reduce sensitivity to motion during long imaging ETs.

Baby brain atlases

The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.

Exploring the origins of EEG motion artefacts during simultaneous fMRI acquisition: Implications for motion artefact correction

Motion artefacts (MAs) are induced within EEG data collected simultaneously with fMRI when the subject's head rotates relative to the magnetic field. The effects of these artefacts have generally been ameliorated by removing periods of data during which large artefact voltages appear in the EEG traces. However, even when combined with other standard post-processing methods, this strategy does not remove smaller MAs which can dominate the neuronal signals of interest. A number of methods are therefore being developed to characterise the MA by measuring reference signals and then using these in artefact correction. These methods generally assume that the head and EEG cap, plus any attached sensors, form a rigid body which can be characterised by a standard set of six motion parameters. Here we investigate the motion of the head/EEG cap system to provide a better understanding of MAs. We focus on the reference layer artefact subtraction (RLAS) approach, as this allows measurement of a separate reference signal for each electrode that is being used to measure brain activity.

Through a series of experiments on phantoms and subjects, we find that movement of the EEG cap relative to the phantom and skin on the forehead is relatively small and that this non-rigid body movement does not appear to cause considerable discrepancy in artefacts between the scalp and reference signals. However, differences in the amplitude of these signals is observed which may be due to differences in geometry of the system from which the reference signals are measured compared with the brain signals. In addition, we find that there is non-rigid body movement of the skull and skin which produces an additional MA component for a head shake, which is not present for a head nod. This results in a large discrepancy in the amplitude and temporal profile of the MA measured on the scalp and reference layer, reducing the efficacy of MA correction based on the reference signals.

Together our data suggest that the efficacy of the correction of MA using any reference-based system is likely to differ for different types of head movement with head shake being the hardest to correct. This provides new information to inform the development of hardware and post-processing methods for removing MAs from EEG data acquired simultaneously with fMRI data.

Prospective motion correction for 3D pseudo-continuous arterial spin labeling using an external optical tracking system

Head motion is an unsolved problem in magnetic resonance imaging (MRI) studies of the brain. Real-time tracking using a camera has recently been proposed as a way to prevent head motion artifacts. As compared to navigator-based approaches that use MRI data to detect and correct motion, optical motion correction works independently of the MRI scanner, thus providing low-latency real-time motion updates without requiring any modifications to the pulse sequence. The purpose of this study was two-fold: 1) to demonstrate that prospective optical motion correction using an optical camera mitigates artifacts from head motion in three-dimensional pseudo-continuous arterial spin labeling (3D PCASL) acquisitions and 2) to assess the effect of latency differences between real-time optical motion tracking and navigator-style approaches (such as PROMO). An optical motion correction system comprising a single camera and a marker attached to the patient's forehead was used to track motion at a rate of 60fps. In the presence of motion, continuous tracking data from the optical system was used to update the scan plane in real-time during the 3D-PCASL acquisition. Navigator-style correction was simulated by using the tracking data from the optical system and performing updates only once per repetition time. Three normal volunteers and a patient were instructed to perform continuous and discrete head motion throughout the scan. Optical motion correction yielded superior image quality compared to uncorrected images or images using navigator-style correction. The standard deviations of pixel-wise CBF differences between reference and non-corrected, navigator-style-corrected and optical-corrected data were 14.28, 14.35 and 11.09mL/100g/min for continuous motion, and 12.42, 12.04 and 9.60mL/100g/min for discrete motion. Data obtained from the patient revealed that motion can obscure pathology and that application of optical prospective correction can successfully reveal the underlying pathology in the presence of head motion.

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.

Reverse retrospective motion correction

PURPOSE

One potential barrier for using prospective motion correction (PMC) in the clinic is the unpredictable nature of a scan because of the direct interference with the imaging sequence. We demonstrate that a second set of "de-corrected" images can be reconstructed from a scan with PMC that show how images would have appeared without PMC enabled.

THEORY AND METHODS

For three-dimensional scans, the effects of PMC can be undone by performing a retrospective reconstruction based on the inverse of the transformation matrix used for real time gradient feedback. Retrospective reconstruction is performed using a generalized SENSE approach with continuous head motion monitored using a single-marker optical camera system.

RESULTS

Reverse retrospective reconstruction is demonstrated for phantom and in vivo scans using an magnetization-prepared rapid gradient echo (MPRAGE) sequence including parallel and Partial Fourier acceleration.

CONCLUSION

Reverse retrospective reconstruction can almost perfectly undo the effects of prospective feedback, and thereby provide a second image data set with the effects of motion correction removed. In case of correct feedback, one can directly compare the quality of the corrected with that of the uncorrected scan. Additionally, because erroneous feedback during PMC may introduce artifacts, it is possible to eliminate artifacts in a corrupted scan by reversing the false gradient updates. Magn Reson Med 75:2341-2349, 2016. © 2015 Wiley Periodicals, Inc.

Quantitative framework for prospective motion correction evaluation

PURPOSE

Establishing a framework to evaluate performances of prospective motion correction (PMC) MRI considering motion variability between MRI scans.

METHODS

A framework was developed to obtain quantitative comparisons between different motion correction setups, considering that varying intrinsic motion patterns between acquisitions can induce bias. Intrinsic motion was considered by replaying in a phantom experiment the recorded motion trajectories from subjects. T1-weighted MRI on five volunteers and two different marker fixations (mouth guard and nose bridge fixations) were used to test the framework. Two metrics were investigated to quantify the improvement of the image quality with PMC.

RESULTS

Motion patterns vary between subjects as well as between repeated scans within a subject. This variability can be approximated by replaying the motion in a distinct phantom experiment and used as a covariate in models comparing motion corrections. We show that considering the intrinsic motion alters the statistical significance in comparing marker fixations. As an example, two marker fixations, a mouth guard and a nose bridge, were evaluated in terms of their effectiveness for PMC. A mouth guard achieved better PMC performance.

CONCLUSION

Intrinsic motion patterns can bias comparisons between PMC configurations and must be considered for robust evaluations. A framework for evaluating intrinsic motion patterns in PMC is presented.