Motion correction in fMRI via registration of individual slices into an anatomical volume

What's New and What's Next in Diffusion MRI Preprocessing

Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B₁ bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.

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.

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.

Effects of using different software packages for BOLD analysis in planning a neurosurgical treatment in patients with brain tumours

BACKGROUND

The authors of the present thesis carried out a comparative analysis of three different computer software packages - FSL, SPM and FuncTool - for the processing of fMRI scans.

PURPOSE

The aim of the thesis was the analysis of the volume of regions functionally active during the stimulation of the centres evaluated as well as the location of those regions in relation to the tumour boundaries, and then the comparison of the results.

MATERIAL AND METHODS

Thirty eight patients with a diagnosed tumour of the left hemisphere, qualified for a neurosurgical operation, underwent fMRI. The functions of speech, motion and sensation were evaluated. Imaging data were processed for all the acquired scans with the use of each of the three software packages assessed.

RESULTS

For the FuncTool software package the calculated differences in the distances were several times greater than those calculated using FSL and SPM. The differences in the volume of the functionally active regions derived from the calculations with the use of the FSL and SPM software packages were statistically different for four out of the six functions evaluated.

CONCLUSIONS

The conclusions of the analysis in question showed that the FSL and SPM packages could be used interchangeably in the functional mapping of the brain with the purpose of the planning of neurosurgical operations. The FuncTool software package is less precise than FSL and SPM.

The Benefit of Slice Timing Correction in Common fMRI Preprocessing Pipelines

Due to the nature of fMRI acquisition protocols, slices cannot be acquired simultaneously, and as a result, are temporally misaligned from each other. To correct from this misalignment, preprocessing pipelines often incorporate slice timing correction (STC). However, evaluating the benefits of STC is challenging because it (1) is dependent on slice acquisition parameters, (2) interacts with head movement in a non-linear fashion, and (3) significantly changes with other preprocessing steps, fMRI experimental design, and fMRI acquisition parameters. Presently, the interaction of STC with various scan conditions has not been extensively examined. Here, we examine the effect of STC when it is applied with various other preprocessing steps such as motion correction (MC), motion parameter residualization (MPR), and spatial smoothing. Using 180 simulated and 30 real fMRI data, we quantitatively demonstrate that the optimal order in which STC should be applied depends on interleave parameters and motion level. We also demonstrate the benefit STC on sub-second-TR scans and for functional connectivity analysis. We conclude that STC is a critical part of the preprocessing pipeline that can be extremely beneficial for fMRI processing. However, its effectiveness interacts with other preprocessing steps and with other scan parameters and conditions which may obscure its significant importance in the fMRI processing pipeline.

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.

Motion-robust diffusion compartment imaging using simultaneous multi-slice acquisition

PURPOSE

To achieve motion-robust diffusion compartment imaging (DCI) in near continuously moving subjects based on simultaneous multi-slice, diffusion-weighted brain MRI.

METHODS

Simultaneous multi-slice (SMS) acquisition enables fast and dense sampling of k- and q-space. We propose to achieve motion-robust DCI via slice-level motion correction by exploiting the rigid coupling between simultaneously acquired slices. This coupling provides 3D coverage of the anatomy that substantially constraints the slice-to-volume alignment problem. This is incorporated into an explicit model of motion dynamics that handles continuous and large subject motion in robust DCI reconstruction.

RESULTS

We applied the proposed technique, called Motion Tracking based on Simultanous Multislice Registration (MT-SMR) to multi b-value SMS diffusion-weighted brain MRI of healthy volunteers and motion-corrupted scans of 20 pediatric subjects. Quantitative and qualitative evaluation based on fractional anisotropy in unidirectional fiber regions, and DCI in crossing-fiber regions show robust reconstruction in the presence of motion.

CONCLUSION

The proposed approach has the potential to extend routine use of SMS DCI in very challenging populations, such as young children, newborns, and non-cooperative patients.

Integrated fMRI Preprocessing Framework Using Extended Kalman Filter for Estimation of Slice-Wise Motion

Functional MRI acquisition is sensitive to subjects' motion that cannot be fully constrained. Therefore, signal corrections have to be applied a posteriori in order to mitigate the complex interactions between changing tissue localization and magnetic fields, gradients and readouts. To circumvent current preprocessing strategies limitations, we developed an integrated method that correct motion and spatial low-frequency intensity fluctuations at the level of each slice in order to better fit the acquisition processes. The registration of single or multiple simultaneously acquired slices is achieved online by an Iterated Extended Kalman Filter, favoring the robust estimation of continuous motion, while an intensity bias field is non-parametrically fitted. The proposed extraction of gray-matter BOLD activity from the acquisition space to an anatomical group template space, taking into account distortions, better preserves fine-scale patterns of activity. Importantly, the proposed unified framework generalizes to high-resolution multi-slice techniques. When tested on simulated and real data the latter shows a reduction of motion explained variance and signal variability when compared to the conventional preprocessing approach. These improvements provide more stable patterns of activity, facilitating investigation of cerebral information representation in healthy and/or clinical populations where motion is known to impact fine-scale data.

Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders

Recent years have witnessed the proliferation of neuroimaging studies of neurodevelopmental disorders (NDDs), particularly of children with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Tourette's syndrome (TS). Neuroimaging offers immense potential in understanding the biology of these disorders, and how it relates to clinical symptoms. Neuroimaging techniques, in the long run, may help identify neurobiological markers to assist clinical diagnosis and treatment. However, methodological challenges have affected the progress of clinical neuroimaging. This paper reviews the methodological challenges involved in imaging children with NDDs. Specific topics include correcting for head motion, normalization using pediatric brain templates, accounting for psychotropic medication use, delineating complex developmental trajectories, and overcoming smaller sample sizes. The potential of neuroimaging-based biomarkers and the utility of implementing neuroimaging in a clinical setting are also discussed. Data-sharing approaches, technological advances, and an increase in the number of longitudinal, prospective studies are recommended as future directions. Significant advances have been made already, and future decades will continue to see innovative progress in neuroimaging research endeavors of NDDs.

Volumetric reconstruction from printed films: Enabling 30 year longitudinal analysis in MR neuroimaging

Hospitals often hold historical MR image data printed on films without being able to make it accessible to modern image processing techniques. Having the possibility to recover geometrically consistent, volumetric images from scans acquired decades ago will enable more comprehensive, longitudinal studies to understand disease progressions. In this paper, we propose a consistent framework to reconstruct a volumetric representation from printed films holding thick single-slice brain MR image acquisitions dating back to the 1980's. We introduce a flexible framework based on semi-automatic slice extraction, followed by automated slice-to-volume registration with inter-slice transformation regularisation and slice intensity correction. Our algorithm is robust against numerous detrimental effects being present in archaic films. A subsequent, isotropic total variation deconvolution technique revitalises the visual appearance of the obtained volumes. We assess the accuracy and perform the validation of our reconstruction framework on a uniquely long-term MRI dataset where a ground-truth is available. This method will be used to facilitate a robust longitudinal analysis spanning 30 years of MRI scans.

Prospective motion correction in 2D multishot MRI using EPI navigators and multislice-to-volume image registration

PURPOSE

Prospective motion correction reduces artifacts in MRI by correcting for subject motion in real time, but techniques are limited for multishot 2-dimensional (2D) sequences. This study addresses this limitation by using 2D echo-planar imaging (EPI) slice navigator acquisitions together with a multislice-to-volume image registration.

METHODS

The 2D-EPI navigators were integrated into 2D imaging sequences to allow a rapid, real-time motion correction based on the registration of three navigator slices to a reference volume. A dedicated slice-iteration scheme was used to limit mutual spin-saturation effects between navigator and image data. The method was evaluated using T₂ -weighted spin echo and multishot rapid acquisition with relaxation enhancement (RARE) sequences, and its motion-correction capabilities were compared with those of periodically rotated overlapping parallel lines with enhanced reconstruction (PROPELLER). Validation was performed in vivo using a well-defined motion protocol.

RESULTS

Data acquired during subject motion showed residual motion parameters within ±0.5 mm and ±0.5°, and demonstrated a substantial improvement in image quality compared with uncorrected scans. In a comparison to PROPELLER, the proposed technique preserved a higher level of anatomical detail in the presence of subject motion.

CONCLUSIONS

EPI-navigator-based prospective motion correction using multislice-to-volume image registration can substantially reduce image artifacts, while minimizing spin-saturation effects. The method can be adapted for use in other 2D MRI sequences and promises to improve image quality in routine clinical examinations. Magn Reson Med 78:2127-2135, 2017. © 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.

Temporal slice registration and robust diffusion-tensor reconstruction for improved fetal brain structural connectivity analysis

Diffusion weighted magnetic resonance imaging, or DWI, is one of the most promising tools for the analysis of neural microstructure and the structural connectome of the human brain. The application of DWI to map early development of the human connectome in-utero, however, is challenged by intermittent fetal and maternal motion that disrupts the spatial correspondence of data acquired in the relatively long DWI acquisitions. Fetuses move continuously during DWI scans. Reliable and accurate analysis of the fetal brain structural connectome requires careful compensation of motion effects and robust reconstruction to avoid introducing bias based on the degree of fetal motion. In this paper we introduce a novel robust algorithm to reconstruct in-vivo diffusion-tensor MRI (DTI) of the moving fetal brain and show its effect on structural connectivity analysis. The proposed algorithm involves multiple steps of image registration incorporating a dynamic registration-based motion tracking algorithm to restore the spatial correspondence of DWI data at the slice level and reconstruct DTI of the fetal brain in the standard (atlas) coordinate space. A weighted linear least squares approach is adapted to remove the effect of intra-slice motion and reconstruct DTI from motion-corrected data. The proposed algorithm was tested on data obtained from 21 healthy fetuses scanned in-utero at 22-38 weeks gestation. Significantly higher fractional anisotropy values in fiber-rich regions, and the analysis of whole-brain tractography and group structural connectivity, showed the efficacy of the proposed method compared to the analyses based on original data and previously proposed methods. The results of this study show that slice-level motion correction and robust reconstruction is necessary for reliable in-vivo structural connectivity analysis of the fetal brain. Connectivity analysis based on graph theoretic measures show high degree of modularity and clustering, and short average characteristic path lengths indicative of small-worldness property of the fetal brain network. These findings comply with previous findings in newborns and a recent study on fetuses. The proposed algorithm can provide valuable information from DWI of the fetal brain not available in the assessment of the original 2D slices and may be used to more reliably study the developing fetal brain connectome.

Midlife managerial experience is linked to late life hippocampal morphology and function

An active cognitive lifestyle has been suggested to have a protective role in the long-term maintenance of cognition. Amongst healthy older adults, more managerial or supervisory experiences in midlife are linked to a slower hippocampal atrophy rate in late life. Yet whether similar links exist in individuals with Mild Cognitive Impairment (MCI) is not known, nor whether these differences have any functional implications. 68 volunteers from the Sydney SMART Trial, diagnosed with non-amnestic MCI, were divided into high and low managerial experience (HME/LME) during their working life. All participants underwent neuropsychological testing, structural and resting-state functional MRI. Group comparisons were performed on hippocampal volume, morphology, hippocampal seed-based functional connectivity, memory and executive function and self-ratings of memory proficiency. HME was linked to better memory function (p = 0.024), mediated by larger hippocampal volume (p = 0.025). More specifically, deformation analysis found HME had relatively more volume in the CA1 sub-region of the hippocampus (p < 0.05). Paradoxically, this group rated their memory proficiency worse (p = 0.004), a result correlated with diminished functional connectivity between the right hippocampus and right prefrontal cortex (p < 0.001). Finally, hierarchical regression modelling substantiated this double dissociation.

Towards a comprehensive framework for movement and distortion correction of diffusion MR images: Within volume movement

Most motion correction methods work by aligning a set of volumes together, or to a volume that represents a reference location. These are based on an implicit assumption that the subject remains motionless during the several seconds it takes to acquire all slices in a volume, and that any movement occurs in the brief moment between acquiring the last slice of one volume and the first slice of the next. This is clearly an approximation that can be more or less good depending on how long it takes to acquire one volume and in how rapidly the subject moves. In this paper we present a method that increases the temporal resolution of the motion correction by modelling movement as a piecewise continous function over time. This intra-volume movement correction is implemented within a previously presented framework that simultaneously estimates distortions, movement and movement-induced signal dropout. We validate the method on highly realistic simulated data containing all of these effects. It is demonstrated that we can estimate the true movement with high accuracy, and that scalar parameters derived from the data, such as fractional anisotropy, are estimated with greater fidelity when data has been corrected for intra-volume movement. Importantly, we also show that the difference in fidelity between data affected by different amounts of movement is much reduced when taking intra-volume movement into account. Additional validation was performed on data from a healthy volunteer scanned when lying still and when performing deliberate movements. We show an increased correspondence between the “still” and the “movement” data when the latter is corrected for intra-volume movement. Finally we demonstrate a big reduction in the telltale signs of intra-volume movement in data acquired on elderly subjects.

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We introduce a method to correct for intra-volume movement into an existing framework for movement and distortion correction.

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It has been validated on realistic simulated data and on experimental data with deliberate movement.

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The results indicate that one can reliably reverse the adverse effects of intra-volume movement.

Optimal slice timing correction and its interaction with fMRI parameters and artifacts

Due to the nature of fMRI acquisition protocols, slices in the plane of acquisition are not acquired simultaneously or sequentially, and therefore are temporally misaligned with each other. Slice timing correction (STC) is a critical preprocessing step that corrects for this temporal misalignment. Interpolation-based STC is implemented in all major fMRI processing software packages. To date, little effort has gone towards assessing the optimal method of STC. Delineating the benefits of STC can be challenging because of its slice-dependent gain as well as its interaction with other fMRI artifacts. In this study, we propose a new optimal method (Filter-Shift) based on the fundamental properties of sampling theory in digital signal processing. We then evaluate our method by comparing it to two other methods of STC from the most popular statistical software packages, SPM and FSL. STC methods were evaluated using 338 simulated and 30 real fMRI data and demonstrate the effectiveness of STC in general as well as the superiority of the proposed method in comparison to existing ones. All methods were evaluated under various scan conditions such as motion level, interleave sequence, scanner sampling rate, and the duration of the scan itself.

Evaluating the Influence of Spatial Resampling for Motion Correction in Resting-State Functional MRI

Head motion is one of major concerns in current resting-state functional MRI studies. Image realignment including motion estimation and spatial resampling is often applied to achieve rigid-body motion correction. While the accurate estimation of motion parameters has been addressed in most studies, spatial resampling could also produce spurious variance, and lead to unexpected errors on the amplitude of BOLD signal. In this study, two simulation experiments were designed to characterize these variance related with spatial resampling. The fluctuation amplitude of spurious variance was first investigated using a set of simulated images with estimated motion parameters from a real dataset, and regions more likely to be affected by spatial resampling were found around the peripheral regions of the cortex. The other simulation was designed with three typical types of motion parameters to represent different extents of motion. It was found that areas with significant correlation between spurious variance and head motion scattered all over the brain and varied greatly from one motion type to another. In the last part of this study, four popular motion regression approaches were applied respectively and their performance in reducing spurious variance was compared. Among them, Friston 24 and Voxel-specific 12 model (Friston et al., 1996), were found to have the best outcomes. By separating related effects during fMRI analysis, this study provides a better understanding of the characteristics of spatial resampling and the interpretation of motion-BOLD relationship.

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.

An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging

In this paper we describe a method for retrospective estimation and correction of eddy current (EC)-induced distortions and subject movement in diffusion imaging. In addition a susceptibility-induced field can be supplied and will be incorporated into the calculations in a way that accurately reflects that the two fields (susceptibility- and EC-induced) behave differently in the presence of subject movement. The method is based on registering the individual volumes to a model free prediction of what each volume should look like, thereby enabling its use on high b-value data where the contrast is vastly different in different volumes. In addition we show that the linear EC-model commonly used is insufficient for the data used in the present paper (high spatial and angular resolution data acquired with Stejskal-Tanner gradients on a 3T Siemens Verio, a 3T Siemens Connectome Skyra or a 7T Siemens Magnetome scanner) and that a higher order model performs significantly better. The method is already in extensive practical use and is used by four major projects (the WU-UMinn HCP, the MGH HCP, the UK Biobank and the Whitehall studies) to correct for distortions and subject movement.

Head Motion and Correction Methods in Resting-state Functional MRI

Resting-state functional magnetic resonance imaging (RS-fMRI) is used to investigate brain functional connectivity at rest. However, noise from human physiological motion is an unresolved problem associated with this technique. Following the unexpected previous result that group differences in head motion between control and patient groups caused group differences in the resting-state network with RS-fMRI, we reviewed the effects of human physiological noise caused by subject motion, especially motion of the head, on functional connectivity at rest detected with RS-fMRI. The aim of the present study was to review head motion artifact with RS-fMRI, individual and patient population differences in head motion, and correction methods for head motion artifact with RS-fMRI. Numerous reports have described new methods [e.g., scrubbing, regional displacement interaction (RDI)] for motion correction on RS-fMRI, many of which have been successful in reducing this negative influence. However, the influence of head motion could not be entirely excluded by any of these published techniques. Therefore, in performing RS-fMRI studies, head motion of the participants should be quantified with measurement technique (e.g., framewise displacement). Development of a more effective correction method would improve the accuracy of RS-fMRI analysis.

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

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

Voxel-wise motion artifacts in population-level whole-brain connectivity analysis of resting-state FMRI

Functional Magnetic Resonance Imaging (fMRI) based brain connectivity analysis maps the functional networks of the brain by estimating the degree of synchronous neuronal activity between brain regions. Recent studies have demonstrated that "resting-state" fMRI-based brain connectivity conclusions may be erroneous when motion artifacts have a differential effect on fMRI BOLD signals for between group comparisons. A potential explanation could be that in-scanner displacement, due to rotational components, is not spatially constant in the whole brain. However, this localized nature of motion artifacts is poorly understood and is rarely considered in brain connectivity studies. In this study, we initially demonstrate the local correspondence between head displacement and the changes in the resting-state fMRI BOLD signal. Than, we investigate how connectivity strength is affected by the population-level variation in the spatial pattern of regional displacement. We introduce Regional Displacement Interaction (RDI), a new covariate parameter set for second-level connectivity analysis and demonstrate its effectiveness in reducing motion related confounds in comparisons of groups with different voxel-vise displacement pattern and preprocessed using various nuisance regression methods. The effect of using RDI as second-level covariate is than demonstrated in autism-related group comparisons. The relationship between the proposed method and some of the prevailing subject-level nuisance regression techniques is evaluated. Our results show that, depending on experimental design, treating in-scanner head motion as a global confound may not be appropriate. The degree of displacement is highly variable among various brain regions, both within and between subjects. These regional differences bias correlation-based measures of brain connectivity. The inclusion of the proposed second-level covariate into the analysis successfully reduces artifactual motion-related group differences and preserves real neuronal differences, as demonstrated by the autism-related comparisons.

Resting State fMRI in the moving fetus: a robust framework for motion, bias field and spin history correction

There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5 T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies.

SimPACE: generating simulated motion corrupted BOLD data with synthetic-navigated acquisition for the development and evaluation of SLOMOCO: a new, highly effective slicewise motion correction

Head motion in functional MRI and resting-state MRI is a major problem. Existing methods do not robustly reflect the true level of motion artifact for in vivo fMRI data. The primary issue is that current methods assume that motion is synchronized to the volume acquisition and thus ignore intra-volume motion. This manuscript covers three sections in the use of gold-standard motion-corrupted data to pursue an intra-volume motion correction. First, we present a way to get motion corrupted data with accurately known motion at the slice acquisition level. This technique simulates important data acquisition-related motion artifacts while acquiring real BOLD MRI data. It is based on a novel motion-injection pulse sequence that introduces known motion independently for every slice: Simulated Prospective Acquisition CorrEction (SimPACE). Secondly, with data acquired using SimPACE, we evaluate several motion correction and characterization techniques, including several commonly used BOLD signal- and motion parameter-based metrics. Finally, we introduce and evaluate a novel, slice-based motion correction technique. Our novel method, SLice-Oriented MOtion COrrection (SLOMOCO) performs better than the volumetric methods and, moreover, accurately detects the motion of independent slices, in this case equivalent to the known injected motion. We demonstrate that SLOMOCO can model and correct for nearly all effects of motion in BOLD data. Also, none of the commonly used motion metrics was observed to robustly identify motion corrupted events, especially in the most realistic scenario of sudden head movement. For some popular metrics, performance was poor even when using the ideal known slice motion instead of volumetric parameters. This has negative implications for methods relying on these metrics, such as recently proposed motion correction methods such as data censoring and global signal regression.

Quantitative in vivo imaging of embryonic development: opportunities and challenges

Animal models are critically important for a mechanistic understanding of embryonic morphogenesis. For decades, visualizing these rapid and complex multidimensional events has relied on projection images and thin section reconstructions. While much insight has been gained, fixed tissue specimens offer limited information on dynamic processes that are essential for tissue assembly and organ patterning. Quantitative imaging is required to unlock the important basic science and clinically relevant secrets that remain hidden. Recent advances in live imaging technology have enabled quantitative longitudinal analysis of embryonic morphogenesis at multiple length and time scales. Four different imaging modalities are currently being used to monitor embryonic morphogenesis: optical, ultrasound, magnetic resonance imaging (MRI), and micro-computed tomography (micro-CT). Each has its advantages and limitations with respect to spatial resolution, depth of field, scanning speed, and tissue contrast. In addition, new processing tools have been developed to enhance live imaging capabilities. In this review, we analyze each type of imaging source and its use in quantitative study of embryonic morphogenesis in small animal models. We describe the physics behind their function, identify some examples in which the modality has revealed new quantitative insights, and then conclude with a discussion of new research directions with live imaging.

Subject movement during multislice interleaved MR acquisitions: prevalence and potential effect on MRI-derived brain pathology measurements and multicenter clinical trials of therapeutics for multiple sclerosis

PURPOSE

To show the prevalence of inter-packet motion in clinical trial magnetic resonance imaging (MRI) data and the effect of inter-packet motion on MRI-derived brain pathology measurements.

MATERIALS AND METHODS

We present a method to detect and quantify inter-packet motion, apply it to 2384 MRIs to determine the prevalence of inter-packet motion in clinical trial data, and show the effect of inter-packet motion on measuring multiple sclerosis (MS) lesion volumes.

RESULTS

Experiments with simulated data showed that the detection procedure was accurate at measuring the amount of movement between packets and quantifying the amount of missing data. Application to clinical trial data demonstrated that a large number of MRIs had missing data due to inter-packet motion; 20% of the images had greater than 10% of the data missing and 10% of the images had greater than 15% of the data missing. These levels corresponded to thresholds where lesions were difficult to visually identify or disappeared completely. Lesion volume measurement errors ranged from 1.3 ± 0.5% to 9.9 ± 6.3%.

CONCLUSION

Inter-packet motion can introduce substantial errors to MRI-derived brain pathology measurements. The prevalence of inter-packet motion is substantial in MS clinical trial data. Automated detection procedures should be implemented to increase the fidelity of MRI-derived measurements.

Analysis of fMRI time series with mutual information

Neuroimaging plays a fundamental role in the study of human cognitive neuroscience. Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain. The problem of identifying regionally specific effects in neuroimaging data is usually solved by applying Statistical Parametric Mapping (SPM). Here, a mutual information (MI) criterion is used to identify regionally specific effects produced by a task. In particular, two MI estimators are presented for its use in fMRI data. The first one uses a Parzen probability density estimation, and the second one is based on a K Nearest Neighbours (KNN) estimation. Additionally, a statistical measure has been introduced to automatically detect the voxels which are relevant to the fMRI task. Experiments demonstrate the advantages of MI estimators over SPM maps; firstly, providing more significant differences between relevant and irrelevant voxels; secondly, presenting more focalized activation; and, thirdly, detecting small areas related to the task. These findings, and the improved performance of KNN MI estimator in multisubject and multistimuli studies, make the proposed methods a good alternative to SPM.

Mapping fetal brain development in utero using magnetic resonance imaging: the Big Bang of brain mapping

The development of tools to construct and investigate probabilistic maps of the adult human brain from magnetic resonance imaging (MRI) has led to advances in both basic neuroscience and clinical diagnosis. These tools are increasingly being applied to brain development in adolescence and childhood, and even to neonatal and premature neonatal imaging. Even earlier in development, parallel advances in clinical fetal MRI have led to its growing use as a tool in challenging medical conditions. This has motivated new engineering developments encompassing optimal fast MRI scans and techniques derived from computer vision, the combination of which allows full 3D imaging of the moving fetal brain in utero without sedation. These promise to provide a new and unprecedented window into early human brain growth. This article reviews the developments that have led us to this point, examines the current state of the art in the fields of fast fetal imaging and motion correction, and describes the tools to analyze dynamically changing fetal brain structure. New methods to deal with developmental tissue segmentation and the construction of spatiotemporal atlases are examined, together with techniques to map fetal brain growth patterns.

Self-encoded marker for optical prospective head motion correction in MRI

The tracking and compensation of patient motion during a magnetic resonance imaging (MRI) acquisition is an unsolved problem. For brain MRI, a promising approach recently suggested is to track the patient using an in-bore camera and a checkerboard marker attached to the patient's forehead. However, the possible tracking range of the head pose is limited by the fact that the locally attached marker must be entirely visible inside the camera's narrow field of view (FOV). To overcome this shortcoming, we developed a novel self-encoded marker where each feature on the pattern is augmented with a 2-D barcode. Hence, the marker can be tracked even if it is not completely visible in the camera image. Furthermore, it offers considerable advantages over the checkerboard marker in terms of processing speed, since it makes the correspondence search of feature points and marker-model coordinates, which are required for the pose estimation, redundant. The motion correction with the novel self-encoded marker recovered a rotation of 18° around the principal axis of the cylindrical phantom in-between two scans. After rigid registration of the resulting volumes, we measured a maximal error of 0.39 mm and 0.15° in translation and rotation, respectively. In in vivo experiments, the motion compensated images in scans with large motion during data acquisition indicate a correlation of 0.982 compared to a corresponding motion-free reference.

Optimizing preprocessing and analysis pipelines for single-subject fMRI. I. Standard temporal motion and physiological noise correction methods

Subject-specific artifacts caused by head motion and physiological noise are major confounds in BOLD fMRI analyses. However, there is little consensus on the optimal choice of data preprocessing steps to minimize these effects. To evaluate the effects of various preprocessing strategies, we present a framework which comprises a combination of (1) nonparametric testing including reproducibility and prediction metrics of the data-driven NPAIRS framework (Strother et al. [2002]: NeuroImage 15:747-771), and (2) intersubject comparison of SPM effects, using DISTATIS (a three-way version of metric multidimensional scaling (Abdi et al. [2009]: NeuroImage 45:89-95). It is shown that the quality of brain activation maps may be significantly limited by sub-optimal choices of data preprocessing steps (or "pipeline") in a clinical task-design, an fMRI adaptation of the widely used Trail-Making Test. The relative importance of motion correction, physiological noise correction, motion parameter regression, and temporal detrending were examined for fMRI data acquired in young, healthy adults. Analysis performance and the quality of activation maps were evaluated based on Penalized Discriminant Analysis (PDA). The relative importance of different preprocessing steps was assessed by (1) a nonparametric Friedman rank test for fixed sets of preprocessing steps, applied to all subjects; and (2) evaluating pipelines chosen specifically for each subject. Results demonstrate that preprocessing choices have significant, but subject-dependant effects, and that individually-optimized pipelines may significantly improve the reproducibility of fMRI results over fixed pipelines. This was demonstrated by the detection of a significant interaction with motion parameter regression and physiological noise correction, even though the range of subject head motion was small across the group (≪ 1 voxel). Optimizing pipelines on an individual-subject basis also revealed brain activation patterns either weak or absent under fixed pipelines, which has implications for the overall interpretation of fMRI data, and the relative importance of preprocessing methods.

Preterm neonatal diffusion processing using detection and replacement of outliers prior to resampling

In diffusion weighted MRI, subject motion and brain pulsation lead both to signal drop-outs and image misalignment. Unsedated neonates, with their higher heart rate and propensity for motion are particularly prone to degraded scan quality that impairs diffusion tensor estimation. Retrospective registration and robust estimators are two methods that have previously been demonstrated to address motion and intensity outliers, respectively, in diffusion data. However, when taken together, the resampling of images to correct for misalignment can have the effect of averaging outlier voxels with uncorrupted voxels, thereby making outliers more difficult to detect. This article presents a method to remove outliers prior to resampling while taking misalignment into account so that this averaging of outliers with good data can be avoided. The proposed method is compared to other processing pipelines using simulations and data from unsedated preterm neonates. These results demonstrate advantages to the proposed method, particularly in subjects with high motion.

Model-based attenuation of movement artifacts in fMRI

Behavioral analysis of multi-joint arm reaching has allowed important advances in understanding the control of voluntary movements. Complementing this analysis with functional magnetic resonance imaging (fMRI) would give insight into the neural mechanisms behind this control. However, fMRI is very sensitive to artifacts created by head motion and magnetic field deformation caused by the moving limbs. It is thus necessary to attenuate these motion artifacts in order to obtain correct activation patterns. Most algorithms in literature were designed for slow changes of head position over several brain scans and are not very effective on data when the movement is of duration below the resolution of a brain scan. This paper introduces a simple model-based method to remove motion artifacts during short duration movements. The proposed algorithm can account for head movement and field deformations due to movements within and outside of the scanner's field of view. It uses information from the experimental design and subject kinematics to focus the artifact attenuation in time and space and minimize the loss of uncorrupted data. Applications of the algorithm on arm reaching experimental data obtained with blocked and event-related designs demonstrate attenuation of motion artifacts with minimal effect on the brain activations.

Change in diffusion weighted MRI during liver cancer radiotherapy: preliminary observations

PURPOSE

To evaluate diffusion weighted magnetic resonance imaging (DWI) in liver and liver cancers during and following conformal radiotherapy (RT). To determine the feasibility of using changes in apparent diffusion coefficients (ADC) as a potential surrogate for tumour control or normal tissue injury.

MATERIALS AND METHODS

Patients on a six-fraction conformal liver RT protocol underwent DW-MRI at the time of treatment planning, during RT (week one and two) and one month following RT. Diffusion weighted MR images were acquired in exhale breath hold, using b-values of 0 and 600. Regions of interest (ROIs) corresponding to maximal tumour dose, high-dose peri-tumour liver, irradiated normal liver, non-irradiated liver, and spleen were analyzed on ADC maps.

RESULTS

Eleven patients (four hepatocellular carcinoma, five liver metastases, two cholangiocarcinoma) were evaluated. The baseline median tumour ADC of 1.56 x 10(-3)mm(2)/sec increased to 1.89 x 10(-3)mm(2)/sec at RT week one, to 1.91 x 10(-3)mm(2)/sec during week two and to 2.01 x 10(-3)mm(2)/sec at one month following treatment (p < 0.0001). Early increases in mean ADC were correlated with higher dose and sustained tumour response, whereas RECIST and volume changes on T2 images were not. Peri-tumour mean ADC also increased, from 1.40 x 10(-3)mm(2)/sec (baseline) to 1.55 x 10(-3)mm(2)/sec (RT week 2) and 1.64 x 10(-3)mm(2)/sec (follow-up). Small ADC changes were seen in the irradiated liver, and no significant changes were seen in the un-irradiated liver.

CONCLUSIONS

Changes in tumour ADC were seen during RT. Larger increases were correlated with higher doses and increased likelihood of response.

Three-dimensional, in vivo MRI with self-gating and image coregistration in the mouse

Motion during magnetic resonance imaging (MRI) scans routinely results in undesirable image artifact or blurring. Since high-resolution, three-dimensional (3D) imaging of the mouse requires long scan times for satisfactory signal-to-noise ratio (SNR) and image quality, motion-related artifacts are likely over much of the body and limit applications of mouse MRI. In this investigation, we explored the use of self-gated imaging methods and image coregistration for improving image quality in the presence of motion. Self-gated signal results from a modified 3D gradient-echo sequence showed detection of periodic respiratory and cardiac motion in the adult mouse-with excellent comparison to traditional measurements, sensitivity to respiration-induced tissue changes in the brain, and even detection of embryonic cardiac motion in utero. Serial image coregistration with rapidly-acquired, low-SNR volumes further enabled detection and correction of bulk changes in embryo location during in utero imaging sessions and subsequent reconstruction of high-quality images. These methods, in combination, are shown to expand the range of applications for 3D mouse MRI, enabling late-stage embryonic heart imaging and introducing the possibility of longitudinal developmental studies from embryonic stages through adulthood.

Formulation of current density weighted indices for correspondence between functional MRI and electrocortical stimulation maps

OBJECTIVE

Accurate localization of functionally significant brain regions reduces risks of post-operative neurological deficits. The gold standard for presurgical brain mapping is subdural electrocortical stimulation (ECS), which is an open-cranium surgical procedure. Functional MRI (fMRI) may be a noninvasive alternative if it can be shown that fMRI and ECS maps are spatially consistent. We formulate new 3D current density weighted ECS-fMRI correspondence indices and illustrate their use on human data.

METHODS

Current density maps were computed for simulated and human datasets by solving the electrostatic Laplace equation. The proposed indices were characterized and compared with fixed radii and Euclidean distance indices.

RESULTS

Results from simulated datasets showed that the proposed indices quantify correspondence between fMRI and the ECS truth predictably, and provide conspicuous sensitivity increase from fixed radii indices, whereas Euclidean distances may not be suitable measures of the correspondence.

CONCLUSIONS

The proposed indices reflect contextual information from surrounding electrodes and may be physiologically more meaningful in evaluating ECS-fMRI correspondence.

SIGNIFICANCE

To identify safe limits of resection, an ECS map requires placement of electrodes on a patient's brain. Our proposed indices accurately quantify ECS-fMRI correspondence and may be used to evaluate fMRI as a noninvasive alternative for defining resection limits.

Integration of motion correction and physiological noise regression in fMRI

Physiological fluctuations resulting from the heart beat and respiration are a dominant source of noise in fMRI, particularly at high field strengths. Commonly used physiological noise correction techniques, such as RETROspective Image CORrection (RETROICOR), rely critically on the timing of the image acquisition relative to the heart beat, but do not account for the effects of subject motion. Such motion affects the fluctuation amplitude, yet volume registration can distort the timing information. In this study, we aimed to systematically determine the optimal order of volume registration, slice-time correction and RETROICOR in their traditional forms. In addition, we evaluate the sensitivity of RETROICOR to timing errors introduced by the slice acquisition, and we develop a new method of accounting for timing errors introduced by volume registration into physiological correction (motion-modified RETROICOR). Both simulation and resting data indicate that the temporal standard deviation is reduced most by performing volume registration before RETROICOR and slice-time correction after RETROCIOR. While simulations indicate that physiological noise correction with regressors constructed on a slice-by-slice basis more accurately modeled physiological noise compared to using the same regressors for the entire volume, the difference between these regression techniques in subject data was minimal. The motion-modified RETROICOR showed marked improvement in simulations with varying amounts of subject motion, reducing the temporal standard deviation by up to 36% over the traditional RETROICOR. Though to a lesser degree than in simulation, the motion-modified RETROICOR performed better in nearly every voxel in the brain in both high- and low-resolution subject data.

Spin saturation artifact correction using slice-to-volume registration motion estimates for fMRI time series

Evaluation of functional magnetic resonance imaging (fMRI) as a reliable clinical imaging tool requires accurate assessment and correction of head motion artifacts. As the correction of bulk head motion is vital, the loss of signal strength from the confounding effect of head motion on spin magnetization may be an additional factor in activation analysis error. This study focuses on the evaluation and correction of the spin saturation artifact that occurs when parts of adjacent slices are selected due to changing head positions in single-shot multislice acquisitions. As a consequence of head movement, the acquired slices constituting a fMRI volume are no longer parallel to each other and the spin magnetization in fMRI voxels becomes dependent on head motion history. Motion corrections applying the same rigid motion estimates to all the slices in a volume may not be a reasonable approximation in cases where the magnitude of head motion exceeds a subvoxel range. For realistic ranges of motion in fMRI, an accurate estimate of rigid motion parameters for each echo planar imaging (EPI) slice is essential to correctly register voxel intensities. Previously we have implemented the map-slice-to-volume (MSV) motion correction method that maps each slice in a time series onto a reference anatomical volume, which proved to be effective in improving activation detection. To correctly evaluate the motion dependence of spin magnetization, each voxel is tracked with movement history that is available from MSV motion estimates. Relatively low in resolution, EPI voxels are composed of varying mixtures of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) and variations in the tissue composition give rise to voxel intensities that are functions of tissue T1 properties. We have developed a weighted-average spin saturation (WASS) correction method that can handle full rigid motion and account for the melange of different brain tissue isochromats at each EPI voxel location. We evaluated the effect of spin saturation artifacts and the performance of the WASS correction using simulated fMRI time series synthesized with known true activation, motion, and the associated spin saturation artifact. Two different ranges of head rotations, [-5,5] and [-2,2] deg, were introduced and the effect of the spin saturation artifact was quantified to show 18% and 13% reduction in activation detection rate, respectively. Following the MSV motion and WASS correction, results indicate that WASS correction can improve activation detection by 17% relative to MSV only correction.

Correction of misaligned slices in multi-slice cardiovascular magnetic resonance using slice-to-volume registration

A popular technique to reduce respiratory motion for cardiovascular magnetic resonance is to perform a multi-slice acquisition in which a patient holds their breath multiple times during the scan. The feasibility of rigid slice-to-volume registration to correct for misalignments of slice stacks in such images due to differing breath-hold positions is explored. Experimental results indicate that slice-to-volume registration can compensate for the typical misalignments expected. Correction of slice misalignment results in anatomically more correct images, as well as improved left ventricular volume measurements. The interstudy reproducibility has also been improved reducing the number of samples needed for cardiac MR studies.

Concurrent correction of geometric distortion and motion using the map-slice-to-volume method in echo-planar imaging

The accuracy of measuring voxel intensity changes between stimulus and rest images in fMRI echo-planar imaging (EPI) data is severely degraded in the presence of head motion. In addition, EPI is sensitive to susceptibility-induced geometric distortions. Head motion causes image shifts and associated field map changes that induce different geometric distortion at different time points. Conventionally, geometric distortion is "corrected" with a static field map independently of image registration. That approach ignores all field map changes induced by head motion. This work evaluates the improved motion correction capability of mapping slice to volume with concurrent iterative field corrected reconstruction using updated field maps derived from an initial static field map that has been spatially transformed and resampled. It accounts for motion-induced field map changes for translational and in-plane rotation motion. The results from simulated EPI time series data, in which motion, image intensity and activation ground truths are available, show improved accuracy in image registration, field corrected image reconstruction and activation detection.

Fast feature based multi slice to volume registration using phase congruency

Slice to volume registration is very useful in many medical imaging applications, for example, fusing static high resolution three dimensional (3D) image volumes to dynamic two dimensional (2D) slice data for deriving motion information in 3D. Though information theoretic registration methods such as Mutual Information are usually robust, they are time intensive and typically require a high level of field-of-view correspondence between the source and target images. In single slice to volume registration scenarios, where such correspondence is limited, registration accuracy and robustness often deteriorate. In this paper, we present a novel registration method that maintains robustness and accuracy while significantly increasing registration speed. Our approach employs multiple slice (as opposed to single slice) to volume registration, which increases the amount of potential matching information while maintaining a small number of slices and hence facilitates the often necessary high speed dynamic image acquisition. Our proposed registration approach first extracts phase congruency information from the slices/volume using oriented 2D Gabor wavelets. Using local non maximum suppression, we then automatically obtain a robust and accurate set of feature points that are subsequently matched using an Iterative Closest Point (ICP) approach. Validation on BrainWeb simulated magnetic resonance imaging (MRI) data showed significant gains in speed ( approximately 40-fold increase) when compared to conventional Mutual Information based volumetric registration while maintaining comparable robustness and accuracy levels.