Correction of vibration artifacts in DTI using phase-encoding reversal (COVIPER)

Mitigating susceptibility-induced distortions in high-resolution 3DEPI fMRI at 7T

Geometric distortion is a major limiting factor for spatial specificity in high-resolution fMRI using EPI readouts and is exacerbated at higher field strengths due to increased B0 field inhomogeneity. Prominent correction schemes are based on B0 field-mapping or acquiring reverse phase-encoded (reversed-PE) data. However, to date, comparisons of these techniques in the context of fMRI have only been performed on 2DEPI data, either at lower field or lower resolution. In this study, we investigate distortion compensation in the context of sub-millimetre 3DEPI data at 7T. B0 field-mapping and reversed-PE distortion correction techniques were applied to both partial coverage BOLD-weighted and whole brain MT-weighted 3DEPI data with matched distortion. Qualitative assessment showed overall improvement in cortical alignment for both correction techniques in both 3DEPI fMRI and whole-brain MT-3DEPI datasets. The distortion-corrected MT-3DEPI images were quantitatively evaluated by comparing cortical alignment with an anatomical reference using dice coefficient (DC) and correlation ratio (CR) measures. These showed that B0 field-mapping and reversed-PE methods both improved correspondence between the MT-3DEPI and anatomical data, with more substantial improvements consistently obtained using the reversed-PE approach. Regional analyses demonstrated that the largest benefit of distortion correction, and in particular of the reversed-PE approach, occurred in frontal and temporal regions where susceptibility-induced distortions are known to be greatest, but had not led to complete signal dropout. In conclusion, distortion correction based on reversed-PE data has shown the greater capacity for achieving faithful alignment with anatomical data in the context of high-resolution fMRI at 7T using 3DEPI.

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.

Multiband diffusion tensor imaging for presurgical mapping of motor and language pathways in patients with brain tumors

BACKGROUND AND PURPOSE

Assessment of the essential white matter fibers of arcuate fasciculus and corticospinal tract (CST), required for preoperative planning in brain tumor patients, relies on the reliability of diffusion tensor imaging (DTI). The recent development of multiband DTI (mb-DTI) based on simultaneous multislice excitation could maintain the overall quality of tractography while not exceeding standard clinical care time. To address this potential, we performed quantitative analyses to evaluate tractography results of arcuate fasciculus and CST acquired by mb-DTI in brain tumor patients.

METHODS

We retrospectively analyzed 44 patients with brain lesions who underwent presurgical single-shot DTI (s-DTI) and mb-DTI. We measured DTI parameters: fractional anisotropy (FA) and mean diffusivity (MD [mm² s^-1 ]) in whole brain and tumor regions; and the tractography parameters: fiber FA, MD (mm² s^-1 ), volume (mm³ ), and length (mm) in the whole brain, arcuate fasciculus, and CST. Additionally, three neuroradiologists performed a blinded visual assessment comparing s-DTI with mb-DTI.

RESULTS

The mb-DTI showed higher mean FA and lower MD (r > .95, p < .002) in whole brain and tumor regions of interest; slightly higher fiber FA, volume, and length; and slightly lower fiber MD in whole brain, arcuate fasciculus, and CST than in s-DTI. These differences were significant for fiber FA in all tracts; length (mm) in arcuate fasciculus; and fiber MD (mm² s^-1 ) and volume (mm³ ) in all patients with tumor involved in the arcuate fasciculus, CST, and whole brain tracts (p = .001). Visual assessment demonstrated that both techniques produced visually similar tracts.

CONCLUSIONS

This study demonstrated the clinical potential and significant advantages of preoperative mb-DTI in brain tumor patients.

On the signal-to-noise ratio benefit of spiral acquisition in diffusion MRI

PURPOSE

Spiral readouts combine several favorable properties that promise superior net sensitivity for diffusion imaging. The purpose of this study is to verify the signal-to-noise ratio (SNR) benefit of spiral acquisition in comparison with current echo-planar imaging (EPI) schemes.

METHODS

Diffusion-weighted in vivo brain data from three subjects were acquired with a single-shot spiral sequence and several variants of single-shot EPI, including full-Fourier and partial-Fourier readouts as well as different diffusion-encoding schemes. Image reconstruction was based on an expanded signal model including field dynamics obtained by concurrent field monitoring. The effective resolution of each sequence was matched to that of full-Fourier EPI with 1 mm nominal resolution. SNR maps were generated by determining the noise statistics of the raw data and analyzing the propagation of equivalent synthetic noise through image reconstruction. Using the same approach, maps of noise amplification due to parallel imaging (g-factor) were calculated for different acceleration factors.

RESULTS

Relative to full-Fourier EPI at b = 0 s/mm² , spiral acquisition yielded SNR gains of 42-88% and 40-89% in white and gray matter, respectively, depending on the diffusion-encoding scheme. Relative to partial-Fourier EPI, the gains were 36-44% and 34-42%. Spiral g-factor maps exhibited less spatial variation and lower maxima than their EPI counterparts.

CONCLUSION

Spiral readouts achieve significant SNR gains in the order of 40-80% over EPI in diffusion imaging at 3T. Combining systematic effects of shorter echo time, readout efficiency, and favorable g-factor behavior, similar benefits are expected across clinical and neurosciences uses of diffusion imaging.

Phase Image Texture Analysis for Motion Detection in Diffusion MRI (PITA-MDD)

PURPOSE

Pronounced spin phase artifacts appear in diffusion-weighted imaging (DWI) with only minor subject motion. While DWI data corruption is often identified as signal drop out in diffusion-weighted (DW) magnitude images, DW phase images may have higher sensitivity for detecting subtle subject motion.

METHODS

This article describes a novel method to return a metric of subject motion, computed using an image texture analysis of the DW phase image. This Phase Image Texture Analysis for Motion Detection in dMRI (PITA-MDD) method is computationally fast and reliably detects subject motion from diffusion-weighted images. A threshold of the motion metric was identified to remove motion-corrupted slices, and the effect of removing corrupted slices was assessed on the reconstructed FA maps and fiber tracts.

RESULTS

Using a motion-metric threshold to remove the motion-corrupted slices results in superior fiber tracts and fractional anisotropy maps. When further compared to a state-of-the-art magnitude-based motion correction method, PITA-MDD was able to detect comparable corrupted slices in a more computationally efficient manner.

CONCLUSION

In this study, we evaluated the use of DW phase images to detect motion corruption. The proposed method can be a robust and fast alternative for automatic motion detection in the brain with multiple applications to inform prospective motion correction or as real-time feedback for data quality control during scanning, as well as after data is already acquired.

High-Fidelity Measures of Whole-Brain Functional Connectivity and White Matter Integrity Mediate Relationships between Traumatic Brain Injury and Post-Traumatic Stress Disorder Symptoms

Traumatic brain injury (TBI) disrupts brain communication and increases risk for post-traumatic stress disorder (PTSD). However, mechanisms by which TBI-related disruption of brain communication confers PTSD risk have not been successfully elucidated in humans. This may be in part because functional MRI (fMRI), the most common technique for measuring functional brain communication, is unreliable for characterizing individual patients. However, this unreliability can be overcome with sufficient within-individual data. Here, we examined whether relationships could be observed among TBI, structural and functional brain connectivity, and PTSD severity by collecting ∼3.5 hours of resting-state fMRI and diffusion tensor imaging (DTI) data in each of 26 United States military veterans. We observed that a TBI history was associated with decreased whole-brain resting-state functional connectivity (RSFC), while the number of lifetime TBIs was associated with reduced whole-brain fractional anisotropy (FA). Both RSFC and FA explained independent variance in PTSD severity, with RSFC mediating the TBI-PTSD relationship. Finally, we showed that large amounts of per-individual data produced highly reliable RSFC measures, and that relationships among TBI, RSFC/FA, and PTSD could not be observed with typical data quantities. These results demonstrate links among TBI, brain connectivity, and PTSD severity, and illustrate the need for precise characterization of individual patients using high-data fMRI scanning.

Four in vivo g-ratio-weighted imaging methods: Comparability and repeatability at the group level

A recent method, denoted in vivo g-ratio-weighted imaging, has related the microscopic g-ratio, only accessible by ex vivo histology, to noninvasive MRI markers for the fiber volume fraction (FVF) and myelin volume fraction (MVF). Different MRI markers have been proposed for g-ratio weighted imaging, leaving open the question which combination of imaging markers is optimal. To address this question, the repeatability and comparability of four g-ratio methods based on different combinations of, respectively, two imaging markers for FVF (tract-fiber density, TFD, and neurite orientation dispersion and density imaging, NODDI) and two imaging markers for MVF (magnetization transfer saturation rate, MT, and, from proton density maps, macromolecular tissue volume, MTV) were tested in a scan-rescan experiment in two groups. Moreover, it was tested how the repeatability and comparability were affected by two key processing steps, namely the masking of unreliable voxels (e.g., due to partial volume effects) at the group level and the calibration value used to link MRI markers to MVF (and FVF). Our data showed that repeatability and comparability depend largely on the marker for the FVF (NODDI outperformed TFD), and that they were improved by masking. Overall, the g-ratio method based on NODDI and MT showed the highest repeatability (90%) and lowest variability between groups (3.5%). Finally, our results indicate that the calibration procedure is crucial, for example, calibration to a lower g-ratio value (g = 0.6) than the commonly used one (g = 0.7) can change not only repeatability and comparability but also the reported dependency on the FVF imaging marker. Hum Brain Mapp 39:24-41, 2018. © 2017 Wiley Periodicals, Inc.

Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress

Diffusion magnetic resonance imaging (dMRI) continues to grow in popularity as a useful neuroimaging method to study brain development, and longitudinal studies that track the same individuals over time are emerging. Over the last decade, seminal work using dMRI has provided new insights into the development of brain white matter (WM) microstructure, connections and networks throughout childhood and adolescence. This review provides an introduction to dMRI, both diffusion tensor imaging (DTI) and other dMRI models, as well as common acquisition and analysis approaches. We highlight the difficulties associated with ascribing these imaging measurements and their changes over time to specific underlying cellular and molecular events. We also discuss selected methodological challenges that are of particular relevance for studies of development, including critical choices related to image acquisition, image analysis, quality control assessment, and the within-subject and longitudinal reliability of dMRI measurements. Next, we review the exciting progress in the characterization and understanding of brain development that has resulted from dMRI studies in childhood and adolescence, including brief overviews and discussions of studies focusing on sex and individual differences. Finally, we outline future directions that will be beneficial to the field.

The efficiency of retrospective artifact correction methods in improving the statistical power of between-group differences in spinal cord DTI

Diffusion tensor imaging (DTI) is a promising approach for investigating the white matter microstructure of the spinal cord. However, it suffers from severe susceptibility, physiological, and instrumental artifacts present in the cord. Retrospective correction techniques are popular approaches to reduce these artifacts, because they are widely applicable and do not increase scan time.

In this paper, we present a novel outlier rejection approach (reliability masking) which is designed to supplement existing correction approaches by excluding irreversibly corrupted and thus unreliable data points from the DTI index maps. Then, we investigate how chains of retrospective correction techniques including (i) registration, (ii) registration and robust fitting, and (iii) registration, robust fitting, and reliability masking affect the statistical power of a previously reported finding of lower fractional anisotropy values in the posterior column and lateral corticospinal tracts in cervical spondylotic myelopathy (CSM) patients.

While established post-processing steps had small effect on the statistical power of the clinical finding (slice-wise registration: −0.5%, robust fitting: +0.6%), adding reliability masking to the post-processing chain increased it by 4.7%. Interestingly, reliability masking and registration affected the t-score metric differently: while the gain in statistical power due to reliability masking was mainly driven by decreased variability in both groups, registration slightly increased variability. In conclusion, reliability masking is particularly attractive for neuroscience and clinical research studies, as it increases statistical power by reducing group variability and thus provides a cost-efficient alternative to increasing the group size.

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A novel outlier rejection technique (reliability masking) is introduced.

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Standard artifact correction has little effect on the statistical power of between-group differences.

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Reliability masking improves the statistical power of between-group differences.

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This improvement is driven by decreased group-level variability.

Diffusion tensor spectroscopic imaging of the human brain in children and adults

PURPOSE

We developed diffusion tensor spectroscopic imaging (DTSI), based on proton-echo-planar-spectroscopic imaging (PEPSI), and evaluated the feasibility of mapping brain metabolite diffusion in adults and children.

METHODS

PRESS prelocalized DTSI at 3 Tesla (T) was performed using navigator-based correction of movement-related phase errors and cardiac gating with compensation for repetition time (TR) related variability in T₁ saturation. Mean diffusivity (MD) and fractional anisotropy (FA) of total N-acetyl-aspartate (tNAA), total creatine (tCr), and total choline (tCho) were measured in eight adults (17-60 years) and 10 children (3-24 months) using b_max  = 1734 s/mm² , 1 cc and 4.5 cc voxel sizes, with nominal scan times of 17 min and 8:24 min. Residual movement-related phase encoding ghosting (PEG) was used as a regressor across scans to correct overestimation of MD.

RESULTS

After correction for PEG, metabolite slice-averaged MD estimated at 20% PEG were lower (P < 0.042) for adults (0.17/0.20/0.18 × 10^-3 mm² /s) than for children (0.26/0.27/0.24 × 10^-3 mm² /s). Extrapolated to 0% PEG, the MD estimates decreased further (0.09/0.11/0.11 × 10^-3 mm² /s versus 0.15/0.16/0.15 × 10^-3 mm² /s). Slice-averaged FA of tNAA (P = 0.049), tCr (P = 0.067), and tCho (P = 0.003) were higher in children.

CONCLUSION

This high-speed DTSI approach with PEG regression allows for estimation of metabolite MD and FA with improved tolerance to movement. Our preliminary data suggesting age-related changes support DTSI as a sensitive technique for investigating intracellular markers of biological processes. Magn Reson Med 78:1246-1256, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Artifact correction in diffusion MRI of non-human primate brains on a clinical 3T scanner

BACKGROUND

Smearing artifacts were observed and investigated in diffusion tensor imaging (DTI) studies of macaque monkeys on a clinical whole-body 3T scanner.

METHODS

Four adult macaques were utilized to evaluate DTI artifacts. DTI images were acquired with a single-shot echo-planar imaging (EPI) sequence using a parallel imaging technique.

RESULTS

The smearing artifacts observed on the diffusion-weighted images and fractional anisotropy maps were caused by the incomplete fat suppression due to the irregular macaque frontal skull geometry and anatomy. The artifact can be reduced substantially using a novel three-dimensional (3D) shimming procedure.

CONCLUSION

The smearing artifacts observed on diffusion weighted images and fractional anisotropy (FA) maps of macaque brains can be reduced substantially using a robust 3D shimming approach. The DTI protocol combined with the shimming procedure could be a robust approach to examine brain connectivity and white matter integrity of non-human primates using a conventional clinical setting.

Whole-Brain In-vivo Measurements of the Axonal G-Ratio in a Group of 37 Healthy Volunteers

The g-ratio, quantifying the ratio between the inner and outer diameters of a fiber, is an important microstructural characteristic of fiber pathways and is functionally related to conduction velocity. We introduce a novel method for estimating the MR g-ratio non-invasively across the whole brain using high-fidelity magnetization transfer (MT) imaging and single-shell diffusion MRI. These methods enabled us to map the MR g-ratio in vivo across the brain's prominent fiber pathways in a group of 37 healthy volunteers and to estimate the inter-subject variability. Effective correction of susceptibility-related distortion artifacts was essential before combining the MT and diffusion data, in order to reduce partial volume and edge artifacts. The MR g-ratio is in good qualitative agreement with histological findings despite the different resolution and spatial coverage of MRI and histology. The MR g-ratio holds promise as an important non-invasive biomarker due to its microstructural and functional relevance in neurodegeneration.

Local but not long-range microstructural differences of the ventral temporal cortex in developmental prosopagnosia

Individuals with developmental prosopagnosia (DP) experience face recognition impairments despite normal intellect and low-level vision and no history of brain damage. Prior studies using diffusion tensor imaging in small samples of subjects with DP (n=6 or n=8) offer conflicting views on the neurobiological bases for DP, with one suggesting white matter differences in two major long-range tracts running through the temporal cortex, and another suggesting white matter differences confined to fibers local to ventral temporal face-specific functional regions of interest (fROIs) in the fusiform gyrus. Here, we address these inconsistent findings using a comprehensive set of analyzes in a sample of DP subjects larger than both prior studies combined (n=16). While we found no microstructural differences in long-range tracts between DP and age-matched control participants, we found differences local to face-specific fROIs, and relationships between these microstructural measures with face recognition ability. We conclude that subtle differences in local rather than long-range tracts in the ventral temporal lobe are more likely associated with developmental prosopagnosia.

POAS4SPM: a toolbox for SPM to denoise diffusion MRI data

We present an implementation of a recently developed noise reduction algorithm for dMRI data, called multi-shell position orientation adaptive smoothing (msPOAS), as a toolbox for SPM. The method intrinsically adapts to the structures of different size and shape in dMRI and hence avoids blurring typically observed in non-adaptive smoothing. We give examples for the usage of the toolbox and explain the determination of experiment-dependent parameters for an optimal performance of msPOAS.

Investigation of vibration-induced artifact in clinical diffusion-weighted imaging of pediatric subjects

It has been reported that mechanical vibrations of the magnetic resonance imaging scanner could produce spurious signal dropouts in diffusion-weighted images resulting in artifactual anisotropy in certain regions of the brain with red appearance in the Directionally Encoded Color maps. We performed a review of the frequency of this artifact across pediatric studies, noting differences by scanner manufacturer, acquisition protocol, as well as weight and position of the subject. We also evaluated the ability of automated and quantitative methods to detect this artifact. We found that the artifact may be present in over 50% of data in certain protocols and is not limited to one scanner manufacturer. While a specific scanner had the highest incidence, low body weight and positioning were also associated with appearance of the artifact for both scanner types evaluated, making children potentially more susceptible than adults. Visual inspection remains the best method for artifact identification. Software for automated detection showed very low sensitivity (10%). The artifact may present inconsistently in longitudinal studies. We discuss a published case report that has been widely cited and used as evidence to set policy about diagnostic criteria for determining vegetative state. That report attributed longitudinal changes in anisotropy to white matter plasticity without considering the possibility that the changes were caused by this artifact. Our study underscores the need to check for the presence of this artifact in clinical studies, analyzes circumstances for when it may be more likely to occur, and suggests simple strategies to identify and potentially avoid its effects.

Diffusion MRI and its Role in Neuropsychology

Diffusion Magnetic Resonance Imaging (dMRI) is a popular method used by neuroscientists to uncover unique information about the structural connections within the brain. dMRI is a non-invasive imaging methodology in which image contrast is based on the diffusion of water molecules in tissue. While applicable to many tissues in the body, this review focuses exclusively on the use of dMRI to examine white matter in the brain. In this review, we begin with a definition of diffusion and how diffusion is measured with MRI. Next we introduce the diffusion tensor model, the predominant model used in dMRI. We then describe acquisition issues related to acquisition parameters and scanner hardware and software. Sources of artifacts are then discussed, followed by a brief review of analysis approaches. We provide an overview of the limitations of the traditional diffusion tensor model, and highlight several more sophisticated non-tensor models that better describe the complex architecture of the brain's white matter. We then touch on reliability and validity issues of diffusion measurements. Finally, we describe examples of ways in which dMRI has been applied to studies of brain disorders and how identified alterations relate to symptomatology and cognition.

Diffusion imaging in the rat cervical spinal cord

Magnetic resonance imaging (MRI) is the state of the art approach for assessing the status of the spinal cord noninvasively, and can be used as a diagnostic and prognostic tool in cases of disease or injury. Diffusion weighted imaging (DWI), is sensitive to the thermal motion of water molecules and allows for inferences of tissue microstructure. This report describes a protocol to acquire and analyze DWI of the rat cervical spinal cord on a small-bore animal system. It demonstrates an imaging setup for the live anesthetized animal and recommends a DWI acquisition protocol for high-quality imaging, which includes stabilization of the cord and control of respiratory motion. Measurements with diffusion weighting along different directions and magnitudes (b-values) are used. Finally, several mathematical models of the resulting signal are used to derive maps of the diffusion processes within the spinal cord tissue that provide insight into the normal cord and can be used to monitor injury or disease processes noninvasively.

Distortion correction in EPI using an extended PSF method with a reversed phase gradient approach

In echo-planar imaging (EPI), such as commonly used for functional MRI (fMRI) and diffusion-tensor imaging (DTI), compressed distortion is a more difficult challenge than local stretching as spatial information can be lost in strongly compressed areas. In addition, the effects are more severe at ultra-high field (UHF) such as 7T due to increased field inhomogeneity. To resolve this problem, two EPIs with opposite phase-encoding (PE) polarity were acquired and combined after distortion correction. For distortion correction, a point spread function (PSF) mapping method was chosen due to its high correction accuracy and extended to perform distortion correction of both EPIs with opposite PE polarity thus reducing the PSF reference scan time. Because the amount of spatial information differs between the opposite PE datasets, the method was further extended to incorporate a weighted combination of the two distortion-corrected images to maximize the spatial information content of a final corrected image. The correction accuracy of the proposed method was evaluated in distortion-corrected data using both forward and reverse phase-encoded PSF reference data and compared with the reversed gradient approaches suggested previously. Further we demonstrate that the extended PSF method with an improved weighted combination can recover local distortions and spatial information loss and be applied successfully not only to spin-echo EPI, but also to gradient-echo EPIs acquired with both PE directions to perform geometrically accurate image reconstruction.

DR-BUDDI (Diffeomorphic Registration for Blip-Up blip-Down Diffusion Imaging) method for correcting echo planar imaging distortions

We propose an echo planar imaging (EPI) distortion correction method (DR-BUDDI), specialized for diffusion MRI, which uses data acquired twice with reversed phase encoding directions, often referred to as blip-up blip-down acquisitions. DR-BUDDI can incorporate information from an undistorted structural MRI and also use diffusion-weighted images (DWI) to guide the registration, improving the quality of the registration in the presence of large deformations and in white matter regions. DR-BUDDI does not require the transformations for correcting blip-up and blip-down images to be the exact inverse of each other. Imposing the theoretical "blip-up blip-down distortion symmetry" may not be appropriate in the presence of common clinical scanning artifacts such as motion, ghosting, Gibbs ringing, vibrations, and low signal-to-noise. The performance of DR-BUDDI is evaluated with several data sets and compared to other existing blip-up blip-down correction approaches. The proposed method is robust and generally outperforms existing approaches. The inclusion of the DWIs in the correction process proves to be important to obtain a reliable correction of distortions in the brain stem. Methods that do not use DWIs may produce a visually appealing correction of the non-diffusion weighted images, but the directionally encoded color maps computed from the tensor reveal an abnormal anatomy of the white matter pathways.

The role of dopamine and dopaminergic pathways in dystonia: insights from neuroimaging

Background

Dystonia constitutes a heterogeneous group of movement abnormalities, characterized by sustained or intermittent muscle contractions causing abnormal postures. Overwhelming data suggest involvement of basal ganglia and dopaminergic pathways in dystonia. In this review, we critically evaluate recent neuroimaging studies that investigate dopamine receptors, endogenous dopamine release, morphology of striatum, and structural or functional connectivity in cortico-basal ganglia-thalamo-cortical and related cerebellar circuits in dystonia.

Method

A PubMed search was conducted in August 2014.

Results

Positron emission tomography (PET) imaging offers strong evidence for altered D2/D3 receptor binding and dopaminergic release in many forms of idiopathic dystonia. Functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) data reveal likely involvement of related cerebello-thalamo-cortical and sensory-motor networks in addition to basal ganglia.

Discussion

PET imaging of dopamine receptors or transmitter release remains an effective means to investigate dopaminergic pathways, yet may miss factors affecting dopamine homeostasis and related subcellular signaling cascades that could alter the function of these pathways. fMRI and DTI methods may reveal functional or anatomical changes associated with dysfunction of dopamine-mediated pathways. Each of these methods can be used to monitor target engagement for potential new treatments. PET imaging of striatal phosphodiesterase and development of new selective PET radiotracers for dopamine D3-specific receptors and Mechanistic target of rampamycin (mTOR) are crucial to further investigate dopaminergic pathways. A multimodal approach may have the greatest potential, using PET to identify the sites of molecular pathology and magnetic resonance methods to determine their downstream effects.

High-resolution diffusion kurtosis imaging at 3T enabled by advanced post-processing

Diffusion Kurtosis Imaging (DKI) is more sensitive to microstructural differences and can be related to more specific micro-scale metrics (e.g., intra-axonal volume fraction) than diffusion tensor imaging (DTI), offering exceptional potential for clinical diagnosis and research into the white and gray matter. Currently DKI is acquired only at low spatial resolution (2–3 mm isotropic), because of the lower signal-to-noise ratio (SNR) and higher artifact level associated with the technically more demanding DKI. Higher spatial resolution of about 1 mm is required for the characterization of fine white matter pathways or cortical microstructure. We used restricted-field-of-view (rFoV) imaging in combination with advanced post-processing methods to enable unprecedented high-quality, high-resolution DKI (1.2 mm isotropic) on a clinical 3T scanner. Post-processing was advanced by developing a novel method for Retrospective Eddy current and Motion ArtifacT Correction in High-resolution, multi-shell diffusion data (REMATCH). Furthermore, we applied a powerful edge preserving denoising method, denoted as multi-shell orientation-position-adaptive smoothing (msPOAS). We demonstrated the feasibility of high-quality, high-resolution DKI and its potential for delineating highly myelinated fiber pathways in the motor cortex. REMATCH performs robustly even at the low SNR level of high-resolution DKI, where standard EC and motion correction failed (i.e., produced incorrectly aligned images) and thus biased the diffusion model fit. We showed that the combination of REMATCH and msPOAS increased the contrast between gray and white matter in mean kurtosis (MK) maps by about 35% and at the same time preserves the original distribution of MK values, whereas standard Gaussian smoothing strongly biases the distribution.

Diffusion imaging quality control via entropy of principal direction distribution

Diffusion MR imaging has received increasing attention in the neuroimaging community, as it yields new insights into the microstructural organization of white matter that are not available with conventional MRI techniques. While the technology has enormous potential, diffusion MRI suffers from a unique and complex set of image quality problems, limiting the sensitivity of studies and reducing the accuracy of findings. Furthermore, the acquisition time for diffusion MRI is longer than conventional MRI due to the need for multiple acquisitions to obtain directionally encoded Diffusion Weighted Images (DWI). This leads to increased motion artifacts, reduced signal-to-noise ratio (SNR), and increased proneness to a wide variety of artifacts, including eddy-current and motion artifacts, "venetian blind" artifacts, as well as slice-wise and gradient-wise inconsistencies. Such artifacts mandate stringent Quality Control (QC) schemes in the processing of diffusion MRI data. Most existing QC procedures are conducted in the DWI domain and/or on a voxel level, but our own experiments show that these methods often do not fully detect and eliminate certain types of artifacts, often only visible when investigating groups of DWI's or a derived diffusion model, such as the most-employed diffusion tensor imaging (DTI). Here, we propose a novel regional QC measure in the DTI domain that employs the entropy of the regional distribution of the principal directions (PD). The PD entropy quantifies the scattering and spread of the principal diffusion directions and is invariant to the patient's position in the scanner. High entropy value indicates that the PDs are distributed relatively uniformly, while low entropy value indicates the presence of clusters in the PD distribution. The novel QC measure is intended to complement the existing set of QC procedures by detecting and correcting residual artifacts. Such residual artifacts cause directional bias in the measured PD and here called dominant direction artifacts. Experiments show that our automatic method can reliably detect and potentially correct such artifacts, especially the ones caused by the vibrations of the scanner table during the scan. The results further indicate the usefulness of this method for general quality assessment in DTI studies.

The impact of post-processing on spinal cord diffusion tensor imaging

Diffusion tensor imaging (DTI) provides information about the microstructure in the brain and spinal cord. While new neuroimaging techniques have significantly advanced the accuracy and sensitivity of DTI of the brain, the quality of spinal cord DTI data has improved less. This is in part due to the small size of the spinal cord (ca. 1cm diameter) and more severe instrumental (e.g. eddy current) and physiological (e.g. cardiac pulsation) artefacts present in spinal cord DTI. So far, the improvements in image quality and resolution have resulted from cardiac gating and new acquisition approaches (e.g. reduced field-of-view techniques). The use of retrospective correction methods is not well established for spinal cord DTI. The aim of this paper is to develop an improved post-processing pipeline tailored for DTI data of the spinal cord with increased quality. For this purpose, we compared two eddy current and motion correction approaches using three-dimensional affine (3D-affine) and slice-wise registrations. We also introduced a new robust-tensor-fitting method that controls for whole-volume outliers. Although in general 3D-affine registration improves data quality, occasionally it can lead to misregistrations and biassed tensor estimates. The proposed robust tensor fitting reduced misregistration-related bias and yielded more reliable tensor estimates. Overall, the combination of slice-wise motion correction, eddy current correction, and robust tensor fitting yielded the best results. It increased the contrast-to-noise ratio (CNR) in FA maps by about 30% and reduced intra-subject variation in fractional anisotropy (FA) maps by 18%. The higher quality of FA maps allows for a better distinction between grey and white matter without increasing scan time and is compatible with any multi-directional DTI acquisition scheme.

A hitchhiker's guide to diffusion tensor imaging

Diffusion Tensor Imaging (DTI) studies are increasingly popular among clinicians and researchers as they provide unique insights into brain network connectivity. However, in order to optimize the use of DTI, several technical and methodological aspects must be factored in. These include decisions on: acquisition protocol, artifact handling, data quality control, reconstruction algorithm, and visualization approaches, and quantitative analysis methodology. Furthermore, the researcher and/or clinician also needs to take into account and decide on the most suited software tool(s) for each stage of the DTI analysis pipeline. Herein, we provide a straightforward hitchhiker's guide, covering all of the workflow's major stages. Ultimately, this guide will help newcomers navigate the most critical roadblocks in the analysis and further encourage the use of DTI.

Retrospective correction of physiological noise in DTI using an extended tensor model and peripheral measurements

Diffusion tensor imaging is widely used in research and clinical applications, but this modality is highly sensitive to artefacts. We developed an easy-to-implement extension of the original diffusion tensor model to account for physiological noise in diffusion tensor imaging using measures of peripheral physiology (pulse and respiration), the so-called extended tensor model. Within the framework of the extended tensor model two types of regressors, which respectively modeled small (linear) and strong (nonlinear) variations in the diffusion signal, were derived from peripheral measures. We tested the performance of four extended tensor models with different physiological noise regressors on nongated and gated diffusion tensor imaging data, and compared it to an established data-driven robust fitting method. In the brainstem and cerebellum the extended tensor models reduced the noise in the tensor-fit by up to 23% in accordance with previous studies on physiological noise. The extended tensor model addresses both large-amplitude outliers and small-amplitude signal-changes. The framework of the extended tensor model also facilitates further investigation into physiological noise in diffusion tensor imaging. The proposed extended tensor model can be readily combined with other artefact correction methods such as robust fitting and eddy current correction.

Tracking changes following spinal cord injury: insights from neuroimaging

Traumatic spinal cord injury is often disabling and recovery of function is limited. As a consequence of damage, both spinal cord and brain undergo anatomical and functional changes. Besides clinical measures of recovery, biomarkers that can detect early anatomical and functional changes might be useful in determining clinical outcome—during the course of rehabilitation and recovery—as well as furnishing a tool to evaluate novel treatment interventions and their mechanisms of action. Recent evidence suggests an interesting three-way relationship between neurological deficit and changes in the spinal cord and of the brain and that, importantly, noninvasive magnetic resonance imaging techniques, both structural and functional, provide a sensitive tool to lay out these interactions. This review describes recent findings from multimodal imaging studies of remote anatomical changes (i.e., beyond the lesion site), cortical reorganization, and their relationship to clinical disability. These developments in this field may improve our understanding of effects on the nervous system that are attributable to the injury itself and will allow their distinction from changes that result from rehabilitation (i.e., functional retraining) and from interventions affecting the nervous system directly (i.e., neuroprotection or regeneration).

The influence of spatial registration on detection of cerebral asymmetries using voxel-based statistics of fractional anisotropy images and TBSS

The sensitivity of diffusion tensor imaging (DTI) for detecting microstructural white matter alterations has motivated the application of voxel-based statistics (VBS) to fractional anisotropy (FA) images (FA-VBS). However, detected group differences may depend on the spatial registration method used. The objective of this study was to investigate the influence of spatial registration on detecting cerebral asymmetries in FA-VBS analyses with reference to data obtained using Tract-Based Spatial Statistics (TBSS). In the first part of this study we performed FA-VBS analyses using three single-contrast and one multi-contrast registration: (i) whole-brain registration based on T2 contrast, (ii) whole-brain registration based on FA contrast, (iii) individual-hemisphere registration based on FA contrast, and (iv) a combination of (i) and (iii). We then compared the FA-VBS results with those obtained from TBSS. We found that the FA-VBS results depended strongly on the employed registration approach, with the best correspondence between FA-VBS and TBSS results when approach (iv), the “multi-contrast individual-hemisphere” method was employed. In the second part of the study, we investigated the spatial distribution of residual misregistration for each registration approach and the effect on FA-VBS results. For the FA-VBS analyses using the three single-contrast registration methods, we identified FA asymmetries that were (a) located in regions prone to misregistrations, (b) not detected by TBSS, and (c) specific to the applied registration approach. These asymmetries were considered candidates for apparent FA asymmetries due to systematic misregistrations associated with the FA-VBS approach. Finally, we demonstrated that the “multi-contrast individual-hemisphere” approach showed the least residual spatial misregistrations and thus might be most appropriate for cerebral FA-VBS analyses.

The effect of local perturbation fields on human DTI: characterisation, measurement and correction

Indices derived from diffusion tensor imaging (DTI) data, including the mean diffusivity (MD) and fractional anisotropy (FA), are often used to better understand the microstructure of the brain. DTI, however, is susceptible to imaging artefacts, which can bias these indices. The most important sources of artefacts in DTI include eddy currents, nonuniformity and mis-calibration of gradients. We modelled these and other artefacts using a local perturbation field (LPF) approach. LPFs during the diffusion-weighting period describe the local mismatches between the effective and the expected diffusion gradients resulting in a spatially varying error in the diffusion weighting B matrix and diffusion tensor estimation. We introduced a model that makes use of phantom measurements to provide a robust estimation of the LPF in DTI without requiring any scanner-hardware-specific information or special MRI sequences. We derived an approximation of the perturbed diffusion tensor in the isotropic-diffusion limit that can be used to identify regions in any DTI index map that are affected by LPFs. Using these models, we simulated and measured LPFs and characterised their effect on human DTI for three different clinical scanners. The small FA values found in grey matter were biased towards greater anisotropy leading to lower grey-to-white matter contrast (up to 10%). Differences in head position due to e.g. repositioning produced errors of up to 10% in the MD, reducing comparability in multi-centre or longitudinal studies. We demonstrate the importance of the proposed correction by showing improved consistency across scanners, different head positions and an increased FA contrast between grey and white matter.