Distortion correction and robust tensor estimation for MR diffusion imaging

Outliers in diffusion-weighted MRI: Exploring detection models and mitigation strategies

Diffusion-weighted MRI (dMRI) is a medical imaging method that can be used to investigate the brain microstructure and structural connections between different brain regions. The method, however, requires relatively complex data processing frameworks and analysis pipelines. Many of these approaches are vulnerable to signal dropout artefacts that can originate from subjects moving their head during the scan. To combat these artefacts and eliminate such outliers, researchers have proposed two approaches: to replace outliers or to downweight outliers during modelling and analysis. With the rising interest in dMRI for clinical research, these types of corrections are increasingly important. Therefore, we set out to investigate the differences between outlier replacement and weighting approaches to help the dMRI community to select the best tool for their data processing pipelines. We evaluated dMRI motion correction registration and single tensor model fit pipelines using Gaussian Process and Spherical Harmonic based replacement approaches and outlier downweighting using highly realistic whole-brain simulations. As a proof of concept, we applied these approaches to dMRI infant data sets that contained varying numbers of dropout artefacts. Based on our results, we concluded that the Gaussian Process based outlier replacement provided similar tensor fit results to Gaussian Process based outlier detection and downweighting. Therefore, if only the least-squares estimate of the single tensor model is of interest, our recommendation is to use outlier replacement. However, outlier downweighting can potentially provide a more accurate estimate of the model precision which could be relevant for applications such as probabilistic tractoraphy.

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.

Structural and functional MRI of altered brain development in a novel adolescent rat model of quinpirole-induced compulsive checking behavior

Obsessive-compulsive disorder (OCD) is increasingly considered to be a neurodevelopmental disorder. However, despite insights in neural substrates of OCD in adults, less is known about mechanisms underlying compulsivity during brain development in children and adolescents. Therefore, we developed an adolescent rat model of compulsive checking behavior and investigated developmental changes in structural and functional measures in the frontostriatal circuitry. Five-weeks old Sprague Dawley rats were subcutaneously injected with quinpirole (n = 21) or saline (n = 20) twice a week for five weeks. Each injection was followed by placement in the middle of an open field table, and compulsive behavior was quantified as repeated checking behavior. Anatomical, resting-state functional and diffusion MRI at 4.7T were conducted before the first and after the last quinpirole/saline injection to measure regional volumes, functional connectivity and structural integrity in the brain, respectively. After consecutive quinpirole injections, adolescent rats demonstrated clear checking behavior and repeated travelling between two open-field zones. MRI measurements revealed an increase of regional volumes within the frontostriatal circuits and an increase in fractional anisotropy (FA) in white matter areas during maturation in both experimental groups. Quinpirole-injected rats showed a larger developmental increase in FA values in the internal capsule and forceps minor compared to control rats. Our study points toward a link between development of compulsive behavior and altered white matter maturation in quinpirole-injected adolescent rats, in line with observations in pediatric patients with compulsive phenotypes. This novel animal model provides opportunities to investigate novel treatments and underlying mechanisms for patients with early-onset OCD specifically.

Longitudinal neuroimaging following combat concussion: sub-acute, 1 year and 5 years post-injury

Questions remain regarding the long-term impact of combat concussive blast exposure. While efforts have begun to highlight the clinical impact, less is known about neuroimaging trajectories that may inform underlying pathophysiological changes post-injury. Through collaborative efforts in combat, following medical evacuation, and at universities in the USA, this study followed service members both with and without blast concussion from the sub-acute to 1-year and 5-year outcomes with quantitative neuroimaging. The following two primary and two exploratory groups were examined: combat-deployed controls without blast exposure history ‘non-blast control’ and concussive blast patients (primary) and combat concussion arising not from blast ‘non-blast concussion’ and combat-deployed controls with blast exposure history ‘blast control’ (exploratory). A total of 575 subjects were prospectively enrolled and imaged; 347 subjects completed further neuroimaging examination at 1 year and 342 subjects completed further neuroimaging examination at 5 years post-injury. At each time point, MRI scans were completed that included high-resolution structural as well as diffusion tensor imaging acquisitions processed for quantitative volumetric and diffusion tensor imaging changes. Longitudinal evaluation of the number of abnormal diffusion tensor imaging and volumetric regions in patients with blast concussion revealed distinct trends by imaging modality. While the presence of abnormal volumetric regions remained quite stable comparing our two primary groups of non-blast control to blast concussion, the diffusion tensor imaging abnormalities were observed to have varying trajectories. Most striking was the fractional anisotropy ‘U-shaped’ curve observed for a proportion of those that, if we had only followed them to 1 year, would look like trajectories of recovery. However, by continuing the follow-up to 5 years in these very same patients, a secondary increase in the number of reduced fractional anisotropy regions was identified. Comparing non-blast controls to blast concussion at each time point revealed significant differences in the number of regions with reduced fractional anisotropy at both the sub-acute and 5-year time points, which held after adjustment for age, education, gender, scanner and subsequent head injury exposure followed by correction for multiple comparisons. The secondary increase identified in patients with blast concussion may be the earliest indications of microstructural changes underlying the ‘accelerated brain aging’ theory recently reported from chronic, cross-sectional studies of veterans following brain injury. These varying trajectories also inform potential prognostic neuroimaging biomarkers of progression and recovery.

Cross-scanner and cross-protocol diffusion MRI data harmonisation: A benchmark database and evaluation of algorithms

Diffusion MRI is being used increasingly in studies of the brain and other parts of the body for its ability to provide quantitative measures that are sensitive to changes in tissue microstructure. However, inter-scanner and inter-protocol differences are known to induce significant measurement variability, which in turn jeopardises the ability to obtain 'truly quantitative measures' and challenges the reliable combination of different datasets. Combining datasets from different scanners and/or acquired at different time points could dramatically increase the statistical power of clinical studies, and facilitate multi-centre research. Even though careful harmonisation of acquisition parameters can reduce variability, inter-protocol differences become almost inevitable with improvements in hardware and sequence design over time, even within a site. In this work, we present a benchmark diffusion MRI database of the same subjects acquired on three distinct scanners with different maximum gradient strength (40, 80, and 300 mT/m), and with 'standard' and 'state-of-the-art' protocols, where the latter have higher spatial and angular resolution. The dataset serves as a useful testbed for method development in cross-scanner/cross-protocol diffusion MRI harmonisation and quality enhancement. Using the database, we compare the performance of five different methods for estimating mappings between the scanners and protocols. The results show that cross-scanner harmonisation of single-shell diffusion data sets can reduce the variability between scanners, and highlight the promises and shortcomings of today's data harmonisation techniques.

Changes in brain white matter integrity after systemic treatment for breast cancer: a prospective longitudinal study

An increasing number of studies suggest chemotherapy for breast cancer may be neurotoxic. Cross-sectional MRI diffusion tensor imaging (DTI) studies suggest a vulnerability of brain white matter to various chemotherapeutic regimens. Up till now, this was confirmed in one prospective DTI study: Deprez et al. (2012) showed a widespread decline in fractional anisotropy (FA) of breast cancer patients after chemotherapy consisting of 5-fluorouracil (5-FU), epirubicin and cyclophosphamide (FEC) +/- taxanes +/- endocrine treatment. Our aim was to evaluate whether similar detrimental effects on white matter integrity would be observed with the currently widely prescribed anthracycline-based chemotherapy for breast cancer (predominantly doxorubicin and cyclophosphamide +/- taxanes +/- endocrine treatment (=BC + SYST; n = 26) compared to no systemic treatment (BC; n = 23) and no-cancer controls (NC; n = 30). Assessment took place before and six months after chemotherapy, and matched intervals for the unexposed groups. DTI data were analyzed using voxel-based tract-based spatial statistics and region of interest (ROI) analysis. Voxel-based analysis did not show an effect of chemotherapy +/- endocrine treatment on white matter integrity. ROI analysis however indicated subtle detrimental effects of chemotherapy +/- endocrine treatment by showing a larger decline in WM integrity in the superior longitudinal fasciculus and corticospinal tract in BC + SYST than BC. Indications for relatively mild neurotoxicity in our study might be explained by patient characteristics and specific aspects of data analysis. The omission of 5-FU in current treatment regimens or the administration of doxorubicin instead of epirubicin is also discussed as an explanation for the observed effects.

Diffusion-Tensor Tractography of the Auditory Neural Pathway : Clinical Usefulness in Patients with Unilateral Sensorineural Hearing Loss

PURPOSE

The purpose of this study was to evaluate the structural integrity of the auditory neural pathway in patients with unilateral sensorineural hearing loss using quantitative diffusion-tensor tractography.

METHODS

Diffusion-tensor tractography imaging was performed using a 3T magnetic resonance imaging system to evaluate structural alterations in the auditory neural pathway of patients with unilateral sensorineural hearing loss. The two diffusion-tensor tractography parameters, fractional anisotropy and the apparent diffusion coefficient were compared between the ipsilateral side and the contralateral side in patients and controls. Additionally, correlations between the parameter values and the hearing loss level in patients were evaluated.

RESULTS

A total of 24 sensorineural hearing loss patients (14 males; age range, 17-65 years; average age, 45.3 years) and 24 age and sex-matched control subjects were enrolled. Fractional anisotropy values on the ipsilateral and contralateral sides were significantly lower in patients than in the control group (p = 0.004 and 0.001, respectively). The differences in the apparent diffusion coefficient values for the ipsilateral and contralateral sides between the two groups were not significant (p = 0.279 and 0.248, respectively). There was an inverse relationship between fractional anisotropy and the severity of hearing impairment on the ipsilateral and contralateral sides (r = -0.519, p = 0.005 and r = -0.454, p = 0.015, respectively). No significant correlation was found between the apparent diffusion coefficient and hearing loss level on the ipsilateral and contralateral sides (r = 0.172, p = 0.380 and r = 0.131, p = 0.508, respectively).

CONCLUSION

Quantitative diffusion-tensor tractography can be used to detect microstructural alterations in the auditory neural pathway in sensorineural hearing loss patients with normal results in standard imaging studies.

Fast and accurate Slicewise OutLIer Detection (SOLID) with informed model estimation for diffusion MRI data

The accurate characterization of the diffusion process in tissue using diffusion MRI is greatly challenged by the presence of artefacts. Subject motion causes not only spatial misalignments between diffusion weighted images, but often also slicewise signal intensity errors. Voxelwise robust model estimation is commonly used to exclude intensity errors as outliers. Slicewise outliers, however, become distributed over multiple adjacent slices after image registration and transformation. This challenges outlier detection with voxelwise procedures due to partial volume effects. Detecting the outlier slices before any transformations are applied to diffusion weighted images is therefore required. In this work, we present i) an automated tool coined SOLID for slicewise outlier detection prior to geometrical image transformation, and ii) a framework to naturally interpret data uncertainty information from SOLID and include it as such in model estimators. SOLID uses a straightforward intensity metric, is independent of the choice of the diffusion MRI model, and can handle datasets with a few or irregularly distributed gradient directions. The SOLID-informed estimation framework prevents the need to completely reject diffusion weighted images or individual voxel measurements by downweighting measurements with their degree of uncertainty, thereby supporting convergence and well-conditioning of iterative estimation algorithms. In comprehensive simulation experiments, SOLID detects outliers with a high sensitivity and specificity, and can achieve higher or at least similar sensitivity and specificity compared to other tools that are based on more complex and time-consuming procedures for the scenarios investigated. SOLID was further validated on data from 54 neonatal subjects which were visually inspected for outlier slices with the interactive tool developed as part of this study, showing its potential to quickly highlight problematic volumes and slices in large population studies. The informed model estimation framework was evaluated both in simulations and in vivo human data.

Inter-subject Registration of Functional Images: Do We Need Anatomical Images?

In Echo-Planar Imaging (EPI)-based Magnetic Resonance Imaging (MRI), inter-subject registration typically uses the subject's T1-weighted (T1w) anatomical image to learn deformations of the subject's brain onto a template. The estimated deformation fields are then applied to the subject's EPI scans (functional or diffusion-weighted images) to warp the latter to a template space. Historically, such indirect T1w-based registration was motivated by the lack of clear anatomical details in low-resolution EPI images: a direct registration of the EPI scans to template space would be futile. A central prerequisite in such indirect methods is that the anatomical (aka the T1w) image of each subject is well aligned with their EPI images via rigid coregistration. We provide experimental evidence that things have changed: nowadays, there is a decent amount of anatomical contrast in high-resolution EPI data. That notwithstanding, EPI distortions due to B0 inhomogeneities cannot be fully corrected. Residual uncorrected distortions induce non-rigid deformations between the EPI scans and the same subject's anatomical scan. In this manuscript, we contribute a computationally cheap pipeline that leverages the high spatial resolution of modern EPI scans for direct inter-subject matching. Our pipeline is direct and does not rely on the T1w scan to estimate the inter-subject deformation. Results on a large dataset show that this new pipeline outperforms the classical indirect T1w-based registration scheme, across a variety of post-registration quality-assessment metrics including: Normalized Mutual Information, relative variance (variance-to-mean ratio), and to a lesser extent, improved peaks of group-level General Linear Model (GLM) activation maps.

Mutations in the netrin-1 gene cause congenital mirror movements

Netrin-1 is a secreted protein that was first identified 20 years ago as an axon guidance molecule that regulates midline crossing in the CNS. It plays critical roles in various tissues throughout development and is implicated in tumorigenesis and inflammation in adulthood. Despite extensive studies, no inherited human disease has been directly associated with mutations in NTN1, the gene coding for netrin-1. Here, we have identified 3 mutations in exon 7 of NTN1 in 2 unrelated families and 1 sporadic case with isolated congenital mirror movements (CMM), a disorder characterized by involuntary movements of one hand that mirror intentional movements of the opposite hand. Given the diverse roles of netrin-1, the absence of manifestations other than CMM in NTN1 mutation carriers was unexpected. Using multimodal approaches, we discovered that the anatomy of the corticospinal tract (CST) is abnormal in patients with NTN1-mutant CMM. When expressed in HEK293 or stable HeLa cells, the 3 mutated netrin-1 proteins were almost exclusively detected in the intracellular compartment, contrary to WT netrin-1, which is detected in both intracellular and extracellular compartments. Since netrin-1 is a diffusible extracellular cue, the pathophysiology likely involves its loss of function and subsequent disruption of axon guidance, resulting in abnormal decussation of the CST.

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.

Non cell-autonomous role of DCC in the guidance of the corticospinal tract at the midline

DCC, a NETRIN-1 receptor, is considered as a cell-autonomous regulator for midline guidance of many commissural populations in the central nervous system. The corticospinal tract (CST), the principal motor pathway for voluntary movements, crosses the anatomic midline at the pyramidal decussation. CST fails to cross the midline in Kanga mice expressing a truncated DCC protein. Humans with heterozygous DCC mutations have congenital mirror movements (CMM). As CMM has been associated, in some cases, with malformations of the pyramidal decussation, DCC might also be involved in this process in human. Here, we investigated the role of DCC in CST midline crossing both in human and mice. First, we demonstrate by multimodal approaches, that patients with CMM due to DCC mutations have an increased proportion of ipsilateral CST projections. Second, we show that in contrast to Kanga mice, the anatomy of the CST is not altered in mice with a deletion of DCC in the CST. Altogether, these results indicate that DCC controls CST midline crossing in both humans and mice, and that this process is non cell-autonomous in mice. Our data unravel a new level of complexity in the role of DCC in CST guidance at the midline.

5-Year imaging sequelae of concussive blast injury and relation to early clinical outcome

Current imaging diagnostic techniques are often insensitive to the underlying pathological changes following mild traumatic brain injury (TBI) or concussion so much so that the explicit definition of these uncomplicated mild brain injuries includes the absence of radiological findings. In the US military, this is complicated by the natural tendency of service members to down play symptoms for fear of removal from their unit particularly in combat making it challenging for clinicians to definitively diagnose and determine course of treatment. Questions remain regarding the long-term impact of these war-time brain injuries. The objective of the current study was to evaluate the long-term imaging sequelae of blast concussion in active-duty US military and leverage previous longitudinal data collected in these same patients to identify predictors of sustained DTI signal change indicative of chronic neurodegeneration. In total, 50 blast TBI and 44 combat-deployed controls were evaluated at this 5-year follow up by advanced neuroimaging techniques including diffusion tensor imaging and quantitative volumetry. While cross-sectional analysis of regions of white matter on DTI images did not reveal significant differences across groups after statistical correction, an approach flexible to the heterogeneity of brain injury at the single-subject level identified 74% of the concussive blast TBI cohort to have reductions in fractional anisotropy indicative of chronic brain injury. Logistic regression leveraging clinical and demographic data collected in the acute/sub-acute and 1-year follow up to determine predictors of these long-term imaging changes determined that brain injury diagnosis, older age, verbal memory and verbal fluency best predicted the presence of DTI abnormalities 5 years post injury with an AUC of 0.78 indicating good prediction strength. These results provide supporting evidence for the evolution not resolution of this brain injury pathology, adding to the growing body of literature describing imaging signatures of chronic neurodegeneration even after mild TBI and concussion.

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Design: prospective, observational, longitudinal research study

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Patients: concussive blast (n = 50), combat-deployed control (n = 44)

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Diffusion tensor imaging analyzed 5 yr post-injury, highly predicted by 1 yr outcomes.

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Imaging abnormalities appear to evolve from sub-acute, to 1-year, to 5-year scan.

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Findings indicate chronic neurodegeneration in majority of blast concussion patients.

Effects of Orientation and Anisometry of Magnetic Resonance Imaging Acquisitions on Diffusion Tensor Imaging and Structural Connectomes

Diffusion-weighted imaging (DWI) quantifies water molecule diffusion within tissues and is becoming an increasingly used technique. However, it is very challenging as correct quantification depends on many different factors, ranging from acquisition parameters to a long pipeline of image processing. In this work, we investigated the influence of voxel geometry on diffusion analysis, comparing different acquisition orientations as well as isometric and anisometric voxels. Diffusion-weighted images of one rat brain were acquired with four different voxel geometries (one isometric and three anisometric in different directions) and three different encoding orientations (coronal, axial and sagittal). Diffusion tensor scalar measurements, tractography and the brain structural connectome were analyzed for each of the 12 acquisitions. The acquisition direction with respect to the main magnetic field orientation affected the diffusion results. When the acquisition slice-encoding direction was not aligned with the main magnetic field, there were more artifacts and a lower signal-to-noise ratio that led to less anisotropic tensors (lower fractional anisotropic values), producing poorer quality results. The use of anisometric voxels generated statistically significant differences in the values of diffusion metrics in specific regions. It also elicited differences in tract reconstruction and in different graph metric values describing the brain networks. Our results highlight the importance of taking into account the geometric aspects of acquisitions, especially when comparing diffusion data acquired using different geometries.

Quantitative Magnetic Resonance Imaging of Skeletal Muscle Disease

Quantitative magnetic resonance imaging (qMRI) describes the development and use of MRI to quantify physical, chemical, and/or biological properties of living systems. Neuromuscular diseases often exhibit a temporally varying, spatially heterogeneous, and multi-faceted pathology. The goal of this protocol is to characterize this pathology using qMRI methods. The MRI acquisition protocol begins with localizer images (used to locate the position of the body and tissue of interest within the MRI system), quality control measurements of relevant magnetic field distributions, and structural imaging for general anatomical characterization. The qMRI portion of the protocol includes measurements of the longitudinal and transverse relaxation time constants (T1 and T2, respectively). Also acquired are diffusion-tensor MRI data, in which water diffusivity is measured and used to infer pathological processes such as edema. Quantitative magnetization transfer imaging is used to characterize the relative tissue content of macromolecular and free water protons. Lastly, fat-water MRI methods are used to characterize fibro-adipose tissue replacement of muscle. In addition to describing the data acquisition and analysis procedures, this paper also discusses the potential problems associated with these methods, the analysis and interpretation of the data, MRI safety, and strategies for artifact reduction and protocol optimization.

Robust MR-based approaches to quantifying white matter structure and structure/function alterations in Huntington's disease

BACKGROUND

Huge advances have been made in understanding and addressing confounds in diffusion MRI data to quantify white matter microstructure. However, there has been a lag in applying these advances in clinical research. Some confounds are more pronounced in HD which impedes data quality and interpretability of patient-control differences. This study presents an optimised analysis pipeline and addresses specific confounds in a HD patient cohort.

METHOD

15 HD gene-positive and 13 matched control participants were scanned on a 3T MRI system with two diffusion MRI sequences. An optimised post processing pipeline included motion, eddy current and EPI correction, rotation of the B matrix, free water elimination (FWE) and tractography analysis using an algorithm capable of reconstructing crossing fibres. The corpus callosum was examined using both a region-of-interest and a deterministic tractography approach, using both conventional diffusion tensor imaging (DTI)-based and spherical deconvolution analyses.

RESULTS

Correcting for CSF contamination significantly altered microstructural metrics and the detection of group differences. Reconstructing the corpus callosum using spherical deconvolution produced a more complete reconstruction with greater sensitivity to group differences, compared to DTI-based tractography. Tissue volume fraction (TVF) was reduced in HD participants and was more sensitive to disease burden compared to DTI metrics.

CONCLUSION

Addressing confounds in diffusion MR data results in more valid, anatomically faithful white matter tract reconstructions with reduced within-group variance. TVF is recommended as a complementary metric, providing insight into the relationship with clinical symptoms in HD not fully captured by conventional DTI metrics.

Neurotoxicity in breast cancer survivors ≥10 years post-treatment is dependent on treatment type

Adjuvant chemotherapy (CT) for breast cancer (BC) is associated with very late side-effects on brain function and structure. However, little is known about neurotoxicity of specific treatment regimens. To compare neurotoxicity profiles after different treatment strategies, we used neurocognitive testing and multimodality MRI in BC survivors randomized to high-dose (HI), conventional-dose (CON-) CT or radiotherapy (RT) only and a healthy control (HC) group. BC survivors who received CON-CT (n = 20) and HC (n = 20) were assessed using a neurocognitive test battery and multimodality MRI including 3D-T1, Diffusion Tensor Imaging (DTI) and 1H-MR spectroscopy (1H-MRS) to measure various aspects of cerebral white (WM) and gray matter (GM). Data were compared to previously assessed groups of BC survivors who received HI-CT (n = 17) and RT-only (n = 15). Testing took place on average 11.5 years post-CT. 3D-T1 showed focal GM volume reductions both for HI-CT and CON-CT compared to RT-only (p < .004). DTI-derived mean diffusivity and 1H-MRS derived N-acetyl aspartate showed WM injury specific to HI-CT but not CON-CT (p < .05). Residual effects were revealed in the RT-only group compared to HC on MRI and neurocognitive measurements (p < .05). Ten years after adjuvant CT for BC lower cerebral GM volume was found in HI as well as CON-CT BC survivors whereas injury to WM is restricted to HI-CT. This might indicate that WM brain changes after BC treatment may show more pronounced (partial) recovery than GM. Furthermore, our results suggest residual neurotoxicity in the RT-only group, which warrants further investigation.

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.

PCA-based groupwise image registration for quantitative MRI

Quantitative magnetic resonance imaging (qMRI) is a technique for estimating quantitative tissue properties, such as the T1 and T2 relaxation times, apparent diffusion coefficient (ADC), and various perfusion measures. This estimation is achieved by acquiring multiple images with different acquisition parameters (or at multiple time points after injection of a contrast agent) and by fitting a qMRI signal model to the image intensities. Image registration is often necessary to compensate for misalignments due to subject motion and/or geometric distortions caused by the acquisition. However, large differences in image appearance make accurate image registration challenging. In this work, we propose a groupwise image registration method for compensating misalignment in qMRI. The groupwise formulation of the method eliminates the requirement of choosing a reference image, thus avoiding a registration bias. The method minimizes a cost function that is based on principal component analysis (PCA), exploiting the fact that intensity changes in qMRI can be described by a low-dimensional signal model, but not requiring knowledge on the specific acquisition model. The method was evaluated on 4D CT data of the lungs, and both real and synthetic images of five different qMRI applications: T1 mapping in a porcine heart, combined T1 and T2 mapping in carotid arteries, ADC mapping in the abdomen, diffusion tensor mapping in the brain, and dynamic contrast-enhanced mapping in the abdomen. Each application is based on a different acquisition model. The method is compared to a mutual information-based pairwise registration method and four other state-of-the-art groupwise registration methods. Registration accuracy is evaluated in terms of the precision of the estimated qMRI parameters, overlap of segmented structures, distance between corresponding landmarks, and smoothness of the deformation. In all qMRI applications the proposed method performed better than or equally well as competing methods, while avoiding the need to choose a reference image. It is also shown that the results of the conventional pairwise approach do depend on the choice of this reference image. We therefore conclude that our groupwise registration method with a similarity measure based on PCA is the preferred technique for compensating misalignments in qMRI.

Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI): A framework for single-subject analysis in diffusion tensor imaging

The purpose of this work is to develop a framework for single-subject analysis of diffusion tensor imaging (DTI) data. This framework is termed Tract Orientation and Angular Dispersion Deviation Indicator (TOADDI) because it is capable of testing whether an individual tract as represented by the major eigenvector of the diffusion tensor and its corresponding angular dispersion are significantly different from a group of tracts on a voxel-by-voxel basis. This work develops two complementary statistical tests based on the elliptical cone of uncertainty, which is a model of uncertainty or dispersion of the major eigenvector of the diffusion tensor. The orientation deviation test examines whether the major eigenvector from a single subject is within the average elliptical cone of uncertainty formed by a collection of elliptical cones of uncertainty. The shape deviation test is based on the two-tailed Wilcoxon-Mann-Whitney two-sample test between the normalized shape measures (area and circumference) of the elliptical cones of uncertainty of the single subject against a group of controls. The False Discovery Rate (FDR) and False Non-discovery Rate (FNR) were incorporated in the orientation deviation test. The shape deviation test uses FDR only. TOADDI was found to be numerically accurate and statistically effective. Clinical data from two Traumatic Brain Injury (TBI) patients and one non-TBI subject were tested against the data obtained from a group of 45 non-TBI controls to illustrate the application of the proposed framework in single-subject analysis. The frontal portion of the superior longitudinal fasciculus seemed to be implicated in both tests (orientation and shape) as significantly different from that of the control group. The TBI patients and the single non-TBI subject were well separated under the shape deviation test at the chosen FDR level of 0.0005. TOADDI is a simple but novel geometrically based statistical framework for analyzing DTI data. TOADDI may be found useful in single-subject, graph-theoretic and group analyses of DTI data or DTI-based tractography techniques.

Realistic simulation of artefacts in diffusion MRI for validating post-processing correction techniques

In this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. In the literature, improved alignment is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We apply this framework in three ways. Firstly we assess the most commonly used method for artefact correction, FSL's eddy_correct, and compare it to a recently proposed alternative, eddy. We demonstrate quantitatively that using eddy_correct leads to significant errors in the corrected data, whilst eddy is able to provide much improved correction. Secondly we investigate the datasets required to achieve good correction with eddy, by looking at the minimum number of directions required and comparing the recommended full-sphere acquisitions to equivalent half-sphere protocols. Finally, we investigate the impact of correction quality by examining the fits from microstructure models to real and simulated data.

A Simulation Framework for Quantitative Validation of Artefact Correction in Diffusion MRI

In this paper we demonstrate a simulation framework that enables the direct and quantitative comparison of post-processing methods for diffusion weighted magnetic resonance (DW-MR) images. DW-MR datasets are employed in a range of techniques that enable estimates of local microstructure and global connectivity in the brain. These techniques require full alignment of images across the dataset, but this is rarely the case. Artefacts such as eddy-current (EC) distortion and motion lead to misalignment between images, which compromise the quality of the microstructural measures obtained from them. Numerous methods and software packages exist to correct these artefacts, some of which have become de-facto standards, but none have been subject to rigorous validation. The ultimate aim of these techniques is improved image alignment, yet in the literature this is assessed using either qualitative visual measures or quantitative surrogate metrics. Here we introduce a simulation framework that allows for the direct, quantitative assessment of techniques, enabling objective comparisons of existing and future methods. DW-MR datasets are generated using a process that is based on the physics of MRI acquisition, which allows for the salient features of the images and their artefacts to be reproduced. We demonstrate the application of this framework by testing one of the most commonly used methods for EC correction, registration of DWIs to b = 0, and reveal the systematic bias this introduces into corrected datasets.

Diffusion tensor tractography of normal facial and vestibulocochlear nerves

PURPOSE

Diffusion tensor tractography (DTT) is not adequately reliable for prediction of facial and vestibulocochlear (VII-VIII) nerve locations, especially relative to a vestibular schwannoma (VS). Furthermore, it is often not possible to visualize normal VII-VIII nerves by DTT (visualization rates were 12.5-63.6%). Therefore, DTT post-processing was optimized for normal VII-VIII nerve visualization with and without manual noise elimination.

METHODS

DTT examinations of ten patients were evaluated to assess the improvement in performance by modifying seed region of interest (ROI) and fractional anisotropy (FA) threshold. Seed ROI was placed at the porus of the internal auditory meatus, and FA threshold values were either fixed or variable for each patient. DTT visualization of cranial nerves VII-VIII was evaluated and the noise effect was measured.

RESULTS

Cranial nerves VII-VIII were visualized in 90% of patients without using manual noise elimination by modifying the seed ROI and FA threshold. The visualization rate with FA threshold of the upper limit in each patient (100%) was significantly higher than that with FA threshold of 0.1 (75%) (p = 0.02). The incidence rate of noise with FA threshold of the upper limit (10%) was not significantly different than the FA threshold of 0.1 (20%) (p = 0.66).

CONCLUSION

Seed ROI modification and FA thresholding can improve the visualization of cranial nerve VII-VIII locations in DTT. This technique is promising for its potential to determine the relationship of cranial nerves VII-VIII to VS.

Efficacy of distortion correction on diffusion imaging: comparison of FSL eddy and eddy_correct using 30 and 60 directions diffusion encoding

Diffusion imaging is a unique noninvasive tool to detect brain white matter trajectory and integrity in vivo. However, this technique suffers from spatial distortion and signal pileup or dropout originating from local susceptibility gradients and eddy currents. Although there are several methods to mitigate these problems, most techniques can be applicable either to susceptibility or eddy-current induced distortion alone with a few exceptions. The present study compared the correction efficiency of FSL tools, "eddy_correct" and the combination of "eddy" and "topup" in terms of diffusion-derived fractional anisotropy (FA). The brain diffusion images were acquired from 10 healthy subjects using 30 and 60 directions encoding schemes based on the electrostatic repulsive forces. For the 30 directions encoding, 2 sets of diffusion images were acquired with the same parameters, except for the phase-encode blips which had opposing polarities along the anteroposterior direction. For the 60 directions encoding, non-diffusion-weighted and diffusion-weighted images were obtained with forward phase-encoding blips and non-diffusion-weighted images with the same parameter, except for the phase-encode blips, which had opposing polarities. FA images without and with distortion correction were compared in a voxel-wise manner with tract-based spatial statistics. We showed that images corrected with eddy and topup possessed higher FA values than images uncorrected and corrected with eddy_correct with trilinear (FSL default setting) or spline interpolation in most white matter skeletons, using both encoding schemes. Furthermore, the 60 directions encoding scheme was superior as measured by increased FA values to the 30 directions encoding scheme, despite comparable acquisition time. This study supports the combination of eddy and topup as a superior correction tool in diffusion imaging rather than the eddy_correct tool, especially with trilinear interpolation, using 60 directions encoding scheme.

Estimating the apparent transverse relaxation time (R2(*)) from images with different contrasts (ESTATICS) reduces motion artifacts

Relaxation rates provide important information about tissue microstructure. Multi-parameter mapping (MPM) estimates multiple relaxation parameters from multi-echo FLASH acquisitions with different basic contrasts, i.e., proton density (PD), T1 or magnetization transfer (MT) weighting. Motion can particularly affect maps of the apparent transverse relaxation rate R2^*, which are derived from the signal of PD-weighted images acquired at different echo times. To address the motion artifacts, we introduce ESTATICS, which robustly estimates R2^* from images even when acquired with different basic contrasts. ESTATICS extends the fitted signal model to account for inherent contrast differences in the PDw, T1w and MTw images. The fit was implemented as a conventional ordinary least squares optimization and as a robust fit with a small or large confidence interval. These three different implementations of ESTATICS were tested on data affected by severe motion artifacts and data with no prominent motion artifacts as determined by visual assessment or fast optical motion tracking. ESTATICS improved the quality of the R2^* maps and reduced the coefficient of variation for both types of data—with average reductions of 30% when severe motion artifacts were present. ESTATICS can be applied to any protocol comprised of multiple 2D/3D multi-echo FLASH acquisitions as used in the general research and clinical setting.

Diffusion tensor MRI of chemotherapy-induced cognitive impairment in non-CNS cancer patients: a review

Patients with non-central nervous system cancers often experience subtle cognitive deficits after treatment with cytotoxic agents. Therapy-induced structural changes to the brain could be one of the possible causes underlying these reported cognitive deficits. In this review, we evaluate the use of diffusion tensor imaging (DTI) for assessing possible therapy-induced changes in the microstructure of the cerebral white matter (WM) and provide a critical overview of the published DTI research on therapy-induced cognitive impairment. Both cross-sectional and longitudinal DTI studies have demonstrated abnormal microstructural properties in WM regions involved in cognition. These findings correlated with cognitive performance, suggesting that there is a link between reduced "WM integrity" and chemotherapy-induced impaired cognition. In this paper, we will also introduce the basics of diffusion tensor imaging and how it can be applied to evaluate effects of therapy on structural changes in cerebral WM. The review concludes with considerations and discussion regarding DTI data interpretation and possible future directions for investigating therapy-induced WM changes in cancer patients. This review article is part of a Special Issue entitled: Neuroimaging Studies of Cancer and Cancer Treatment.

Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading

Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas. Fourteen patients with low-grade gliomas and 19 with high-grade gliomas underwent diffusion tensor imaging and three-dimensional T1-weighted magnetic resonance imaging before tumour resection. Seven features including diffusion-weighted imaging, fractional anisotropy, first eigenvalue, second eigenvalue, third eigenvalue, mean diffusivity and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. We developed a two-level clustering approach for a self-organizing map followed by the K-means algorithm to enable unsupervised clustering of a large number of input vectors with the seven features for the whole brain. The vectors were grouped by the self-organizing map as protoclusters, which were classified into the smaller number of clusters by K-means to make a voxel-based diffusion tensor-based clustered image. Furthermore, we also determined if the diffusion tensor-based clustered image was really helpful for predicting pre-operative glioma grade in a supervised manner. The ratio of each class in the diffusion tensor-based clustered images was calculated from the regions of interest manually traced on the diffusion tensor imaging space, and the common logarithmic ratio scales were calculated. We then applied support vector machine as a classifier for distinguishing between low- and high-grade gliomas. Consequently, the sensitivity, specificity, accuracy and area under the curve of receiver operating characteristic curves from the 16-class diffusion tensor-based clustered images that showed the best performance for differentiating high- and low-grade gliomas were 0.848, 0.745, 0.804 and 0.912, respectively. Furthermore, the log-ratio value of each class of the 16-class diffusion tensor-based clustered images was compared between low- and high-grade gliomas, and the log-ratio values of classes 14, 15 and 16 in the high-grade gliomas were significantly higher than those in the low-grade gliomas (p < 0.005, p < 0.001 and p < 0.001, respectively). These classes comprised different patterns of the seven diffusion tensor imaging-based parameters. The results suggest that the multiple diffusion tensor imaging-based parameters from the voxel-based diffusion tensor-based clustered images can help differentiate between low- and high-grade gliomas.

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We have developed a novel unsupervised method for voxel-based clustered imaging.

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Each class ratio in clustered images differentiated high from low-grade gliomas.

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The 16-class clustered images showed the best performance for the differentiation.

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Each class comprised different patterns of the seven diffusion tensor-based features.

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Multiple parameters from diffusion tensor images are useful for glioma grading.

Iterative reweighted linear least squares for accurate, fast, and robust estimation of diffusion magnetic resonance parameters

PURPOSE

Diffusion-weighted magnetic resonance imaging suffers from physiological noise, such as artifacts caused by motion or system instabilities. Therefore, there is a need for robust diffusion parameter estimation techniques. In the past, several techniques have been proposed, including RESTORE and iRESTORE (Chang et al. Magn Reson Med 2005; 53:1088-1095; Chang et al. Magn Reson Med 2012; 68:1654-1663). However, these techniques are based on nonlinear estimators and are consequently computationally intensive.

METHOD

In this work, we present a new, robust, iteratively reweighted linear least squares (IRLLS) estimator. IRLLS performs a voxel-wise identification of outliers in diffusion-weighted magnetic resonance images, where it exploits the natural skewness of the data distribution to become more sensitive to both signal hyperintensities and signal dropouts.

RESULTS

Both simulations and real data experiments were conducted to compare IRLLS with other state-of-the-art techniques. While IRLLS showed no significant loss in accuracy or precision, it proved to be substantially faster than both RESTORE and iRESTORE. In addition, IRLLS proved to be even more robust when considering the overestimation of the noise level or when the signal-to-noise ratio is low.

CONCLUSION

The substantially shortened calculation time in combination with the increased robustness and accuracy, make IRLLS a practical and reliable alternative to current state-of-the-art techniques for the robust estimation of diffusion-weighted magnetic resonance parameters.

A robust post-processing workflow for datasets with motion artifacts in diffusion kurtosis imaging

PURPOSE

The aim of this study was to develop a robust post-processing workflow for motion-corrupted datasets in diffusion kurtosis imaging (DKI).

MATERIALS AND METHODS

The proposed workflow consisted of brain extraction, rigid registration, distortion correction, artifacts rejection, spatial smoothing and tensor estimation. Rigid registration was utilized to correct misalignments. Motion artifacts were rejected by using local Pearson correlation coefficient (LPCC). The performance of LPCC in characterizing relative differences between artifacts and artifact-free images was compared with that of the conventional correlation coefficient in 10 randomly selected DKI datasets. The influence of rejected artifacts with information of gradient directions and b values for the parameter estimation was investigated by using mean square error (MSE). The variance of noise was used as the criterion for MSEs. The clinical practicality of the proposed workflow was evaluated by the image quality and measurements in regions of interest on 36 DKI datasets, including 18 artifact-free (18 pediatric subjects) and 18 motion-corrupted datasets (15 pediatric subjects and 3 essential tremor patients).

RESULTS

The relative difference between artifacts and artifact-free images calculated by LPCC was larger than that of the conventional correlation coefficient (p<0.05). It indicated that LPCC was more sensitive in detecting motion artifacts. MSEs of all derived parameters from the reserved data after the artifacts rejection were smaller than the variance of the noise. It suggested that influence of rejected artifacts was less than influence of noise on the precision of derived parameters. The proposed workflow improved the image quality and reduced the measurement biases significantly on motion-corrupted datasets (p<0.05).

CONCLUSION

The proposed post-processing workflow was reliable to improve the image quality and the measurement precision of the derived parameters on motion-corrupted DKI datasets. The workflow provided an effective post-processing method for clinical applications of DKI in subjects with involuntary movements.

Automated assessment of the quality of diffusion tensor imaging data using color cast of color-encoded fractional anisotropy images

Diffusion tensor imaging (DTI) data often suffer from artifacts caused by motion. These artifacts are especially severe in DTI data from infants, and implementing tight quality controls is therefore imperative for DTI studies of infants. Currently, routine procedures for quality assurance of DTI data involve the slice-wise visual inspection of color-encoded, fractional anisotropy (CFA) images. Such procedures often yield inconsistent results across different data sets, across different operators who are examining those data sets, and sometimes even across time when the same operator inspects the same data set on two different occasions. We propose a more consistent, reliable, and effective method to evaluate the quality of CFA images automatically using their color cast, which is calculated on the distribution statistics of the 2D histogram in the color space as defined by the International Commission on Illumination (CIE) on lightness and a and b (LAB) for the color-opponent dimensions (also known as the CIELAB color space) of the images. Experimental results using DTI data acquired from neonates verified that this proposed method is rapid and accurate. The method thus provides a new tool for real-time quality assurance for DTI data.

Brain white matter microstructure alterations in adolescent rhesus monkeys exposed to early life stress: associations with high cortisol during infancy

BACKGROUND

Early adverse experiences, especially those involving disruption of the mother-infant relationship, are detrimental for proper socioemotional development in primates. Humans with histories of childhood maltreatment are at high risk for developing psychopathologies including depression, anxiety, substance abuse, and behavioral disorders. However, the underlying neurodevelopmental alterations are not well understood. Here we used a nonhuman primate animal model of infant maltreatment to study the long-term effects of this early life stress on brain white matter integrity during adolescence, its behavioral correlates, and the relationship with early levels of stress hormones.

METHODS

Diffusion tensor imaging and tract based spatial statistics were used to investigate white matter integrity in 9 maltreated and 10 control animals during adolescence. Basal plasma cortisol levels collected at one month of age (when abuse rates were highest) were correlated with white matter integrity in regions with group differences. Total aggression was also measured and correlated with white matter integrity.

RESULTS

We found significant reductions in white matter structural integrity (measured as fractional anisotropy) in the corpus callosum, occipital white matter, external medullary lamina, as well as in the brainstem of adolescent rhesus monkeys that experienced maternal infant maltreatment. In most regions showing fractional anisotropy reductions, opposite effects were detected in radial diffusivity, without changes in axial diffusivity, suggesting that the alterations in tract integrity likely involve reduced myelin. Moreover, in most regions showing reduced white matter integrity, this was associated with elevated plasma cortisol levels early in life, which was significantly higher in maltreated than in control infants. Reduced fractional anisotropy in occipital white matter was also associated with increased social aggression.

CONCLUSIONS

These findings highlight the long-term impact of infant maltreatment on brain white matter structural integrity, particularly in tracts involved in visual processing, emotional regulation, and somatosensory and motor integration. They also suggest a relationship between elevations in stress hormones detected in maltreated animals during infancy and long-term brain white matter structural effects.

Image corruption detection in diffusion tensor imaging for post-processing and real-time monitoring

Due to the high sensitivity of diffusion tensor imaging (DTI) to physiological motion, clinical DTI scans often suffer a significant amount of artifacts. Tensor-fitting-based, post-processing outlier rejection is often used to reduce the influence of motion artifacts. Although it is an effective approach, when there are multiple corrupted data, this method may no longer correctly identify and reject the corrupted data. In this paper, we introduce a new criterion called "corrected Inter-Slice Intensity Discontinuity" (cISID) to detect motion-induced artifacts. We compared the performance of algorithms using cISID and other existing methods with regard to artifact detection. The experimental results show that the integration of cISID into fitting-based methods significantly improves the retrospective detection performance at post-processing analysis. The performance of the cISID criterion, if used alone, was inferior to the fitting-based methods, but cISID could effectively identify severely corrupted images with a rapid calculation time. In the second part of this paper, an outlier rejection scheme was implemented on a scanner for real-time monitoring of image quality and reacquisition of the corrupted data. The real-time monitoring, based on cISID and followed by post-processing, fitting-based outlier rejection, could provide a robust environment for routine DTI studies.

Decreased cerebellar fiber density in cortical myoclonic tremor but not in essential tremor

Pathophysiology of tremor generation remains uncertain in 'familial cortical myoclonic tremor with epilepsy' (FCMTE) and essential tremor (ET). In both disorders, imaging and pathological studies suggest involvement of the cerebellum and its projection areas. MR diffusion tensor imaging allows estimation of white matter tissue composition, and therefore is well suited to quantify structural changes in vivo. This study aimed to compare cerebellar fiber density between FCMTE and ET patients and healthy controls. Seven FCMTE patients, eight ET patients, and five healthy controls were studied. Cerebellum was annotated based on fractional anisotropy (FA) and mean diffusivity volumes. Mean cerebellar FA values were computed as well as mean cerebellar volume. Group statistics included one-way ANOVAs and post hoc independent t tests. Mean FA of the cerebellar region for FCMTE was 0.242 (SD = 0.012), for ET 0.259 (SD = 0.0115), and for controls 0.262 (SD = 0.0146). There was a significant group effect for FA (F(2) = 4.9, p = 0.02). No difference in mean cerebellar volume was found. Post hoc independent t tests revealed significantly decreased mean FA in FCMTE patients compared to controls (t[10] = 2.5, p = 0.03) and ET patients (t[13] = 2.9, p = 0.01), while there was no difference in mean FA between ET patients and controls (t[11] < 1.0). This study indicates for the first time microstructural damage of the cerebellar white matter in FCMTE in vivo. These results ascertain a role of the cerebellum in 'cortical tremor'.

Optimal setting of image bounding box can improve registration accuracy of diffusion tensor tractography

PURPOSE

When we register diffusion tensor tractography (DTT) to anatomical images such as fast imaging employing steady-state acquisition (FIESTA), we register the B0 image to FIESTA. Precise registration of the DTT B0 image to FIESTA is possible with non-rigid registration compared to rigid registration, although the non-rigid methods lack convenience. We report the effect of image data bounding box settings on registration accuracy using a normalized mutual information (NMI) method METHODS: MRI scans of 10 patients were used in this study. Registration was performed without modification of the bounding box in the control group, and the results were compared with groups re-registered using multiple bounding boxes limited to the region of interest (ROI). The distance of misalignment after registration at 3 anatomical characteristic points that are common to both FIESTA and B0 images was used as an index of accuracy.

RESULTS

Mean ([Formula: see text]SD) misalignment at the 3 anatomical points decreased significantly from [Formula: see text] to [Formula: see text] mm, [Formula: see text]), [Formula: see text] to [Formula: see text] mm, ([Formula: see text], and [Formula: see text] to [Formula: see text] mm, ([Formula: see text], each showing improvement compared to the control group CONCLUSION: Narrowing the image data bounding box to the ROI improves the accuracy of registering B0 images to FIESTA by NMI method. With our proposed methodology, accuracy can be improved in extremely easy steps, and this methodology may prove useful for DTT registration to anatomical image.

Collaborative patch-based super-resolution for diffusion-weighted images

In this paper, a new single image acquisition super-resolution method is proposed to increase image resolution of diffusion weighted (DW) images. Based on a nonlocal patch-based strategy, the proposed method uses a non-diffusion image (b0) to constrain the reconstruction of DW images. An extensive validation is presented with a gold standard built on averaging 10 high-resolution DW acquisitions. A comparison with classical interpolation methods such as trilinear and B-spline demonstrates the competitive results of our proposed approach in terms of improvements on image reconstruction, fractional anisotropy (FA) estimation, generalized FA and angular reconstruction for tensor and high angular resolution diffusion imaging (HARDI) models. Besides, first results of reconstructed ultra high resolution DW images are presented at 0.6×0.6×0.6 mm3 and 0.4×0.4×0.4 mm3 using our gold standard based on the average of 10 acquisitions, and on a single acquisition. Finally, fiber tracking results show the potential of the proposed super-resolution approach to accurately analyze white matter brain architecture.

In vivo 3D neuroanatomical evaluation of periprostatic nerve plexus with 3T-MR Diffusion Tensor Imaging

OBJECTIVES

To evaluate if Diffusion Tensor Imaging technique (DTI) can improve the visualization of periprostatic nerve fibers describing the location and distribution of entire neurovascular plexus around the prostate in patients who are candidates for prostatectomy.

MATERIALS AND METHODS

Magnetic Resonance Imaging (MRI), including a 2D T2-weighted FSE sequence in 3 planes, 3D T2-weighted and DTI using 16 gradient directions and b=0 and 1000, was performed on 36 patients. Three out of 36 patients were excluded from the analysis due to poor image quality (blurring N=2, artifact N=1). The study was approved by local ethics committee and all patients gave an informed consent. Images were evaluated by two radiologists with different experience in MRI. DTI images were analyzed qualitatively using dedicated software. Also 2D and 3D T2 images were independently considered.

RESULTS

3D-DTI allowed description of the entire plexus of the periprostatic nerve fibers in all directions, while 2D and 3D T2 morphological sequences depicted part of the fibers, in a plane by plane analysis of fiber courses. DTI demonstrated in all patients the dispersion of nerve fibers around the prostate on both sides including the significant percentage present in the anterior and anterolateral sectors.

CONCLUSIONS

DTI offers optimal representation of the widely distributed periprostatic plexus. If validated, it may help guide nerve-sparing radical prostatectomy.

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.

Local Tests for Identifying Anisotropic Diffusion Areas in Human Brain with DTI

Diffusion tensor imaging (DTI) plays a key role in analyzing the physical structures of biological tissues, particularly in reconstructing fiber tracts of the human brain in vivo. On the one hand, eigenvalues of diffusion tensors (DTs) estimated from diffusion weighted imaging (DWI) data usually contain systematic bias, which subsequently biases the diffusivity measurements popularly adopted in fiber tracking algorithms. On the other hand, correctly accounting for the spatial information is important in the construction of these diffusivity measurements since the fiber tracts are typically spatially structured. This paper aims to establish test-based approaches to identify anisotropic water diffusion areas in the human brain. These areas in turn indicate the areas passed by fiber tracts. Our proposed test statistic not only takes into account the bias components in eigenvalue estimates, but also incorporates the spatial information of neighboring voxels. Under mild regularity conditions, we demonstrate that the proposed test statistic asymptotically follows a χ² distribution under the null hypothesis. Simulation and real DTI data examples are provided to illustrate the efficacy of our proposed methods.