Comparative validation of automated presurgical tractography based on constrained spherical deconvolution and diffusion tensor imaging with direct electrical stimulation

OBJECTIVES

Accurate presurgical brain mapping enables preoperative risk assessment and intraoperative guidance. This cross-sectional study investigated whether constrained spherical deconvolution (CSD) methods were more accurate than diffusion tensor imaging (DTI)-based methods for presurgical white matter mapping using intraoperative direct electrical stimulation (DES) as the ground truth.

METHODS

Five different tractography methods were compared (three DTI-based and two CSD-based) in 22 preoperative neurosurgical patients undergoing surgery with DES mapping. The corticospinal tract (CST, N = 20) and arcuate fasciculus (AF, N = 7) bundles were reconstructed, then minimum distances between tractograms and DES coordinates were compared between tractography methods. Receiver-operating characteristic (ROC) curves were used for both bundles. For the CST, binary agreement, linear modeling, and posthoc testing were used to compare tractography methods while correcting for relative lesion and bundle volumes.

RESULTS

Distance measures between 154 positive (functional response, pDES) and negative (no response, nDES) coordinates, and 134 tractograms resulted in 860 data points. Higher agreement was found between pDES coordinates and CSD-based compared to DTI-based tractograms. ROC curves showed overall higher sensitivity at shorter distance cutoffs for CSD (8.5 mm) compared to DTI (14.5 mm). CSD-based CST tractograms showed significantly higher agreement with pDES, which was confirmed by linear modeling and posthoc tests (PFWE < .05).

CONCLUSIONS

CSD-based CST tractograms were more accurate than DTI-based ones when validated using DES-based assessment of motor and sensory function. This demonstrates the potential benefits of structural mapping using CSD in clinical practice.

Correlations between DKI and DWI with Ki-67 in gastric adenocarcinoma

BACKGROUND

Diffusion kurtosis imaging (DKI) has been applied for gastric adenocarcinoma. Correlations between its parameters and Ki-67 are unclear.

PURPOSE

To investigate the correlation between DKI and diffusion-weighted imaging (DWI) parameters with the Ki-67 index in gastric adenocarcinoma.

MATERIAL AND METHODS

A total of 54 patients with gastric adenocarcinoma were enrolled in the study and underwent DWI and DKI at 3.0-T MRI before surgery. Based on the settings of the regions of interest, the DWI and DKI parameters (including apparent diffusion coefficient [ADC], diffusion kurtosis [K], and diffusion coefficient [DK]) of each patient's gastric adenocarcinoma were measured and calculated. The participants were divided into two groups (low Ki-67 group and high Ki-67 groups). The intraclass correlation coefficient (ICC) and independent-sample t-test were used to compare differences in each parameter between two groups. Spearman's correlation coefficient was calculated to determine the correlation between Ki-67 and the parameters. Each parameter was compared using the area under the receiver operating characteristic curve. All parameters were included in the multivariate logistic regression analysis to explore the relationship between each parameter and high Ki-67 index.

RESULTS

ADC and DK were negatively relevant with Ki-67 and K was positively relevant with Ki-67 in gastric adenocarcinoma. ADC, DK, and K had diagnostic efficiency in differentiating the low Ki-67 group from the high Ki-67 group. A higher K value independently predicted a high Ki-67 status.

CONCLUSION

DWI and DKI reflected the proliferative characteristics of gastric adenocarcinoma. K was the strongest independent factor for predicting high Ki-67 status.

ENIGMA-DTI: Translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research

The ENIGMA-DTI (diffusion tensor imaging) workgroup supports analyses that examine the effects of psychiatric, neurological, and developmental disorders on the white matter pathways of the human brain, as well as the effects of normal variation and its genetic associations. The seven ENIGMA disorder-oriented working groups used the ENIGMA-DTI workflow to derive patterns of deficits using coherent and coordinated analyses that model the disease effects across cohorts worldwide. This yielded the largest studies detailing patterns of white matter deficits in schizophrenia spectrum disorder (SSD), bipolar disorder (BD), major depressive disorder (MDD), obsessive-compulsive disorder (OCD), posttraumatic stress disorder (PTSD), traumatic brain injury (TBI), and 22q11 deletion syndrome. These deficit patterns are informative of the underlying neurobiology and reproducible in independent cohorts. We reviewed these findings, demonstrated their reproducibility in independent cohorts, and compared the deficit patterns across illnesses. We discussed translating ENIGMA-defined deficit patterns on the level of individual subjects using a metric called the regional vulnerability index (RVI), a correlation of an individual's brain metrics with the expected pattern for a disorder. We discussed the similarity in white matter deficit patterns among SSD, BD, MDD, and OCD and provided a rationale for using this index in cross-diagnostic neuropsychiatric research. We also discussed the difference in deficit patterns between idiopathic schizophrenia and 22q11 deletion syndrome, which is used as a developmental and genetic model of schizophrenia. Together, these findings highlight the importance of collaborative large-scale research to provide robust and reproducible effects that offer insights into individual vulnerability and cross-diagnosis features.

Improving the predictive potential of diffusion MRI in schizophrenia using normative models-Towards subject-level classification

Diffusion MRI studies consistently report group differences in white matter between individuals diagnosed with schizophrenia and healthy controls. Nevertheless, the abnormalities found at the group-level are often not observed at the individual level. Among the different approaches aiming to study white matter abnormalities at the subject level, normative modeling analysis takes a step towards subject-level predictions by identifying affected brain locations in individual subjects based on extreme deviations from a normative range. Here, we leveraged a large harmonized diffusion MRI dataset from 512 healthy controls and 601 individuals diagnosed with schizophrenia, to study whether normative modeling can improve subject-level predictions from a binary classifier. To this aim, individual deviations from a normative model of standard (fractional anisotropy) and advanced (free-water) dMRI measures, were calculated by means of age and sex-adjusted z-scores relative to control data, in 18 white matter regions. Even though larger effect sizes are found when testing for group differences in z-scores than are found with raw values (p < .001), predictions based on summary z-score measures achieved low predictive power (AUC < 0.63). Instead, we find that combining information from the different white matter tracts, while using multiple imaging measures simultaneously, improves prediction performance (the best predictor achieved AUC = 0.726). Our findings suggest that extreme deviations from a normative model are not optimal features for prediction. However, including the complete distribution of deviations across multiple imaging measures improves prediction, and could aid in subject-level classification.

Strengths and challenges of longitudinal non-human primate neuroimaging

Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.

Tractography Processing with the Sparse Closest Point Transform

We propose a novel approach for processing diffusion MRI tractography datasets using the sparse closest point transform (SCPT). Tractography enables the 3D geometry of white matter pathways to be reconstructed; however, algorithms for processing them are often highly customized, and thus, do not leverage the existing wealth of machine learning (ML) algorithms. We investigated a vector-space tractography representation that aims to bridge this gap by using the SCPT, which consists of two steps: first, extracting sparse and representative landmarks from a tractography dataset, and second transforming curves relative to these landmarks with a closest point transform. We explore its use in three typical tasks: fiber bundle clustering, simplification, and selection across a population. The clustering algorithm groups fibers from single whole-brain datasets using a non-parametric k-means clustering algorithm, with performance compared with three alternative methods and across four datasets. The simplification algorithm removes redundant curves to improve interactive visualization, with performance gauged relative to random subsampling. The selection algorithm extracts bundles across a population using a one-class Gaussian classifier derived from an atlas prototype, with performance gauged by scan-rescan reliability and sensitivity to normal aging, as compared to manual mask-based selection. Our results demonstrate how the SCPT enables the novel application of existing vector-space ML algorithms to create effective and efficient tools for tractography processing. Our experimental data is available online, and our software implementation is available in the Quantitative Imaging Toolkit.

Mesoscale diffusion magnetic resonance imaging of the ex vivo human hippocampus

Mesoscale diffusion magnetic resonance imaging (MRI) endeavors to bridge the gap between macroscopic white matter tractography and microscopic studies investigating the cytoarchitecture of human brain tissue. To ensure a robust measurement of diffusion at the mesoscale, acquisition parameters were arrayed to investigate their effects on scalar indices (mean, radial, axial diffusivity, and fractional anisotropy) and streamlines (i.e., graphical representation of axonal tracts) in hippocampal layers. A mesoscale resolution afforded segementation of the pyramidal cell layer (CA1-4), the dentate gyrus, as well as stratum moleculare, radiatum, and oriens. Using ex vivo samples, surgically excised from patients with intractable epilepsy (n = 3), we found that shorter diffusion times (23.7 ms) with a b-value of 4,000 s/mm2 were advantageous at the mesoscale, providing a compromise between mean diffusivity and fractional anisotropy measurements. Spatial resolution and sample orientation exerted a major effect on tractography, whereas the number of diffusion gradient encoding directions minimally affected scalar indices and streamline density. A sample temperature of 15°C provided a compromise between increasing signal-to-noise ratio and increasing the diffusion properties of the tissue. Optimization of the acquisition afforded a system's view of intra- and extra-hippocampal connections. Tractography reflected histological boundaries of hippocampal layers. Individual layer connectivity was visualized, as well as streamlines emanating from individual sub-fields. The perforant path, subiculum and angular bundle demonstrated extra-hippocampal connections. Histology of the samples confirmed individual cell layers corresponding to ROIs defined on MR images. We anticipate that this ex vivo mesoscale imaging will yield novel insights into human hippocampal connectivity.

Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease

While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test-retest reliability of functional network measures in sporadic SVD patients participating in a high-frequency (monthly) serial imaging study (RUN DMC-InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto-parietal task control network, FPCN; visual network, VN; hand somatosensory-motor network, HSMN) were constructed based on resting-state multi-band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = -.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = -.20, p = .047; direct path: std. beta = -.19, p = .25; total effect: std. beta = -.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high-frequency serial MRI dataset of the sporadic SVD patients revealed poor test-retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD-related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age-related comorbidities, impedes the analysis in elderly SVD patients.

Anatomo-radiological correlation between diffusion tensor imaging and histologic analyses of glial tumors: a preliminary study

BACKGROUND AND PURPOSE

The challenge of the neurosurgical management of gliomas lies in achieving a maximal resection without persistent functional deficit. Diffusion tensor imaging (DTI) allows non-invasive identification of white matter tracts and their interactions with the tumor. Previous DTI validation studies were compared with intraoperative cortical stimulation, but none was performed based on the tumor anatomopathological analysis. This preliminary study evaluates the correlation between the preoperative subcortical DTI tractography and histology in terms of fiber direction as well as potential tumor-related fiber disruption.

METHODS

Eleven patients harboring glial tumors underwent preoperative DTI images. Correlations were performed between the visual color-coded anisotropy (FA) map analysis and the tumor histology after "en bloc" resection. Thirty-one tumor areas were classified according to the degree of tumor infiltration, the destruction of myelin fibers and neurofilaments, the presence of organized white matter fibers, and their orientation in space.

RESULTS

After histologic comparison, the DTI sensitivity and specificity to predict disrupted fiber tracts were respectively of 89% and 90%. The positive and negative predicted values of DTI were 80% and 95%. The DTI data were in line with the histologic myelin fiber orientation in 90% of patients. In our series, the prevalence of destructed fiber was 31%. Glioblastoma WHO grade IV harbored a higher proportion of destructed white matter tracts. Lower WHO grades were associated with higher preservation of subcortical fiber tracts.

CONCLUSION

This DTI/histology study of "en bloc"-resected gliomas reported a high and reproducible concordance of the visual color-coded FA map with the histologic examination to predict subcortical fiber tract disruption. Our series brought consistency to the DTI data that could be performed routinely for glioma surgery to predict the tumor grade and the postoperative clinical outcomes.

XTRACT - Standardised protocols for automated tractography in the human and macaque brain

We present a new software package with a library of standardised tractography protocols devised for the robust automated extraction of white matter tracts both in the human and the macaque brain. Using in vivo data from the Human Connectome Project (HCP) and the UK Biobank and ex vivo data for the macaque brain datasets, we obtain white matter atlases, as well as atlases for tract endpoints on the white-grey matter boundary, for both species. We illustrate that our protocols are robust against data quality, generalisable across two species and reflect the known anatomy. We further demonstrate that they capture inter-subject variability by preserving tract lateralisation in humans and tract similarities stemming from twinship in the HCP cohort. Our results demonstrate that the presented toolbox will be useful for generating imaging-derived features in large cohorts, and in facilitating comparative neuroanatomy studies. The software, tractography protocols, and atlases are publicly released through FSL, allowing users to define their own tractography protocols in a standardised manner, further contributing to open science.

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A new software package for standardised and automated cross-species tractography.

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Homologous white matter bundles in the human and macaque brain.

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Human white matter tract atlases generated from large datasets (1000 subjects).

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Tractography protocols are standardised, but preserve individual variability.

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Generalisability across datasets shown using the HCP and the UK Biobank data.

Computer-assisted planning for minimally invasive anterior two-thirds laser corpus callosotomy: A feasibility study with probabilistic tractography validation

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Laser interstitial thermal therapy (LITT) is a novel minimally invasive technique for the treatment of epilepsy.

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We test computer assisted planning with LITT to disrupt seizure spread.

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Trajectory parameters and models were automatically generated from a single T1 image.

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Probabilistic tractography revealed comparable interhemispheric disconnection to blinded expert surgeons.

Background

Anterior two-thirds corpus callosotomy is an effective palliative neurosurgical procedure for drug-refractory epilepsy that is most commonly used to treat drop-attacks. Laser interstitial thermal therapy is a novel stereotactic ablative technique that has been utilised as a minimally invasive alternative to resective and disconnective open neurosurgery. Case series have reported success in performing laser anterior two-thirds corpus callosotomy. Computer-assisted planning algorithms may help to automate and optimise multi-trajectory planning for this procedure.

Objective

To undertake a simulation-based feasibility study of computer-assisted corpus callostomy planning in comparison with expert manual plans in the same patients.

Methods

Ten patients were selected from a prospectively maintained database. Patients had previously undergone diffusion-weighted imaging and digital subtraction angiography as part of routine SEEG care. Computer-assisted planning was performed using the EpiNav™ platform and compared to manually planned trajectories from two independent blinded experts. Estimated ablation cavities were used in conjunction with probabilistic tractography to simulate the expected extent of interhemispheric disconnection.

Results

Computer-assisted planning resulted in significantly improved trajectory safety metrics (risk score and minimum distance to vasculature) compared to blinded external expert manual plans. Probabilistic tractography revealed residual interhemispheric connectivity in 1/10 cases following computer-assisted planning compared to 4/10 and 2/10 cases with manual planning.

Conclusion

Computer-assisted planning successfully generates multi-trajectory plans capable of LITT anterior two-thirds corpus callosotomy. Computer-assisted planning may provide a means of standardising trajectory planning and serves as a potential new tool for optimising trajectories. A prospective validation study is now required to determine if this translates into improved patient outcomes.

Comparison of quality control methods for automated diffusion tensor imaging analysis pipelines

The processing of brain diffusion tensor imaging (DTI) data for large cohort studies requires fully automatic pipelines to perform quality control (QC) and artifact/outlier removal procedures on the raw DTI data prior to calculation of diffusion parameters. In this study, three automatic DTI processing pipelines, each complying with the general ENIGMA framework, were designed by uniquely combining multiple image processing software tools. Different QC procedures based on the RESTORE algorithm, the DTIPrep protocol, and a combination of both methods were compared using simulated ground truth and artifact containing DTI datasets modeling eddy current induced distortions, various levels of motion artifacts, and thermal noise. Variability was also examined in 20 DTI datasets acquired in subjects with vascular cognitive impairment (VCI) from the multi-site Ontario Neurodegenerative Disease Research Initiative (ONDRI). The mean fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were calculated in global brain grey matter (GM) and white matter (WM) regions. For the simulated DTI datasets, the measure used to evaluate the performance of the pipelines was the normalized difference between the mean DTI metrics measured in GM and WM regions and the corresponding ground truth DTI value. The performance of the proposed pipelines was very similar, particularly in FA measurements. However, the pipeline based on the RESTORE algorithm was the most accurate when analyzing the artifact containing DTI datasets. The pipeline that combined the DTIPrep protocol and the RESTORE algorithm produced the lowest standard deviation in FA measurements in normal appearing WM across subjects. We concluded that this pipeline was the most robust and is preferred for automated analysis of multisite brain DTI data.

Replication and generalization in applied neuroimaging

There is much interest in translating neuroimaging findings into meaningful clinical diagnostics. The goal of scientific discoveries differs from clinical diagnostics. Scientific discoveries must replicate under a specific set of conditions; to translate to the clinic we must show that findings using purpose-built scientific instruments will be observable in clinical populations and instruments. Here we describe and evaluate data and computational methods designed to translate a scientific observation to a clinical setting. Using diffusion weighted imaging (DWI), Wahl et al. (2010) observed that across subjects the mean fractional anisotropy (FA) of homologous pairs of tracts is highly correlated. We hypothesize that this is a fundamental biological trait that should be present in most healthy participants, and deviations from this assessment may be a useful diagnostic metric. Using this metric as an illustration of our methods, we analyzed six pairs of homologous white matter tracts in nine different DWI datasets with 44 subjects each. Considering the original FA measurement as a baseline, we show that the new metric is between 2 and 4 times more precise when used in a clinical context. Our framework to translate research findings into clinical practice can be applied, in principle, to other neuroimaging results.

Tractography-based parcellation does not provide strong evidence of anatomical organisation within the thalamus

Connectivity-based parcellation of subcortical structures using diffusion tractography is now a common paradigm in neuroscience. These analyses often imply voxel-level specificity of connectivity, and the formation of compact, spatially coherent clusters is often taken as strong imaging-based evidence for anatomically distinct subnuclei in an individual. In this study, we demonstrate that internal structure in diffusion anisotropy is not necessary for a plausible parcellation to be obtained, by spatially permuting diffusion parameters within the thalami and repeating the parcellation. Moreover, we show that, in a winner-takes-all paradigm, most voxels receive the same label before and after this shuffling process-a finding that is stable across image acquisitions and tractography algorithms. We therefore suggest that such parcellations should be interpreted with caution.

Manual for clinical language tractography

Background

We introduce a user-friendly, standardized protocol for tractography of the major language fiber bundles.

Method

The introduced method uses dMRI images for tractography whereas the ROI definition is based on structural T1 MPRAGE MRI templates, without normalization to MNI space. ROIs for five language-relevant fiber bundles were visualized on an axial, coronal, or sagittal view of T1 MPRAGE images. The ROIs were defined based upon the tracts’ obligatory pathways, derived from literature and own experiences in peritumoral tractography.

Results

The resulting guideline was evaluated for each fiber bundle in ten healthy subjects and ten patients by one expert and three raters.

Overall, 300 ROIs were evaluated and compared. The targeted language fiber bundles could be tracked in 88% of the ROI pairs, based on the raters’ result blinded ROI placements. The evaluation indicated that the precision of the ROIs did not relate to the varying experience of the raters.

Conclusions

Our guideline introduces a standardized language tractography method for routine preoperative workup and for research contexts. The ROI placement guideline based on easy-to-identify anatomical landmarks proved to be user-friendly and accurate, also in inexperienced test persons.

Automated fiber tract reconstruction for surgery planning: Extensive validation in language-related white matter tracts

Diffusion MRI and tractography hold great potential for surgery planning, especially to preserve eloquent white matter during resections. However, fiber tract reconstruction requires an expert with detailed understanding of neuroanatomy. Several automated approaches have been proposed, using different strategies to reconstruct the white matter tracts in a supervised fashion. However, validation is often limited to comparison with manual delineation by overlap-based measures, which is limited in characterizing morphological and topological differences. In this work, we set up a fully automated pipeline based on anatomical criteria that does not require manual intervention, taking advantage of atlas-based criteria and advanced acquisition protocols available on clinical-grade MRI scanners. Then, we extensively validated it on epilepsy patients with specific focus on language-related bundles. The validation procedure encompasses different approaches, including simple overlap with manual segmentations from two experts, feasibility ratings from external multiple clinical raters and relation with task-based functional MRI. Overall, our results demonstrate good quantitative agreement between automated and manual segmentation, in most cases better performances of the proposed method in qualitative terms, and meaningful relationships with task-based fMRI. In addition, we observed significant differences between experts in terms of both manual segmentation and external ratings. These results offer important insights on how different levels of validation complement each other, supporting the idea that overlap-based measures, although quantitative, do not offer a full perspective on the similarities and differences between automated and manual methods.

A longitudinal human phantom reliability study of multi-center T1-weighted, DTI, and resting state fMRI data

Multi-center MRI studies can enhance power, generalizability, and discovery for clinical neuroimaging research in brain disorders. Here, we sought to establish the utility of a clustering algorithm as an alternative to more traditional intra-class correlation coefficient approaches in a longitudinal multi-center human phantom study. We completed annual reliability scans on 'travelling human phantoms'. Acquisitions across sites were harmonized prospectively. Twenty-seven MRI sessions were available across four participants, scanned on five scanners, across three years. For each scan, three metrics were extracted: cortical thickness (CT), white matter fractional anisotropy (FA), and resting state functional connectivity (FC). For each metric, hierarchical clustering (Ward's method) was performed. The cluster solutions were compared to participant and scanner using the adjusted Rand index (ARI). For all metrics, data clustered by participant rather than by scanner (ARI > 0.8 comparing clusters to participants, ARI < 0.2 comparing clusters to scanners). These results demonstrate that hierarchical clustering can reliably identify structural and functional scans from different participants imaged on different scanners across time. With increasing interest in data-driven approaches in psychiatric and neurologic brain imaging studies, our findings provide a framework for multi-center analytic approaches aiming to identify subgroups of participants based on brain structure or function.

Mono, bi- and tri-exponential diffusion MRI modelling for renal solid masses and comparison with histopathological findings

PURPOSE

To compare diffusion tensor imaging (DTI), intravoxel incoherent motion (IVIM), and tri-exponential models of the diffusion magnetic resonance imaging (MRI) signal for the characterization of renal lesions in relationship to histopathological findings.

METHODS

Sixteen patients planned to undergo nephrectomy for kidney tumour were scanned before surgery at 3 T magnetic resonance imaging (MRI), with T2-weighted imaging, DTI and diffusion weighted imaging (DWI) using ten b-values. DTI parameters (mean diffusivity [MD] and fractional anisotropy [FA]) were obtained by iterative weighted linear least squared fitting of the DTI data and bi-, and tri-exponential fit parameters (Dbi, fstar,and Dtri, ffast,finterm) using a nonlinear fit of the multiple b-value DWI data. Average parameters were calculated for regions of interest, selecting the lesions and healthy kidney tissue. Tumour type and specificities were determined after surgery by histological examination. Mean parameter values of healthy tissue and solid lesions were compared using a Wilcoxon-signed ranked test and MANOVA.

RESULTS

Thirteen solid lesions (nine clear cell carcinomas, two papillary renal cell carcinoma, one haemangioma and one oncocytoma) and four cysts were included. The mean MD of solid lesions are significantly (p < 0.05) lower than healthy cortex and medulla, (1.94 ± 0.32*10- 3 mm2/s versus 2.16 ± 0.12*10- 3 mm2/s and 2.21 ± 0.14*10- 3 mm2/s, respectively) whereas ffast is significantly higher (7.30 ± 3.29% versus 4.14 ± 1.92% and 4.57 ± 1.74%) and finterm is significantly lower (18.7 ± 5.02% versus 28.8 ± 5.09% and 26.4 ± 6.65%). Diffusion coefficients were high (≥2.0*10- 3 mm2/s for MD, 1.90*10- 3 mm2/s for Dbi and 1.6*10- 3 mm2/s for Dtri) in cc-RCCs with cystic structures and/or haemorrhaging and low (≤1.80*10- 3 mm2/s for MD, 1.40*10- 3 mm2/s for Dbi and 1.05*10- 3 mm2/s for Dtri) in tumours with necrosis or sarcomatoid differentiation.

CONCLUSION

Parameters derived from a two- or three-component fit of the diffusion signal are sensitive to histopathological features of kidney lesions.

Tracking and validation techniques for topographically organized tractography

Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.

Individualized tractography-based parcellation of the globus pallidus pars interna using 7T MRI in movement disorder patients prior to DBS surgery

The success of deep brain stimulation (DBS) surgeries for the treatment of movement disorders relies on the accurate placement of an electrode within the motor portion of subcortical brain targets. However, the high number of electrodes requiring relocation indicates that today's methods do not ensure sufficient accuracy for all patients. Here, with the goal of aiding DBS targeting, we use 7 Tesla (T) MRI data to identify the functional territories and parcellate the globus pallidus pars interna (GPi) into motor, associative and limbic regions in individual subjects. 7 T MRI scans were performed in seventeen patients (prior to DBS surgery) and one healthy control. Tractography-based parcellation of each patient's GPi was performed. The cortex was divided into four masks representing motor, limbic, associative and "other" regions. Given that no direct connections between the GPi and the cortex have been shown to exist, the parcellation was carried out in two steps: 1) The thalamus was parcellated based on the cortical targets, 2) The GPi was parcellated using the thalamus parcels derived from step 1. Reproducibility, via repeated scans of a healthy subject, and validity of the findings, using different anatomical pathways for parcellation, were assessed. Lastly, post-operative imaging data was used to validate and determine the clinical relevance of the parcellation. The organization of the functional territories of the GPi observed in our individual patient population agrees with that previously reported in the literature: the motor territory was located posterolaterally, followed anteriorly by the associative region, and further antero-ventrally by the limbic territory. While this organizational pattern was observed across patients, there was considerable variability among patients. The organization of the functional territories of the GPi was remarkably reproducible in intra-subject scans. Furthermore, the organizational pattern was observed consistently by performing the parcellation of the GPi via the thalamus and via a different pathway, going through the striatum. Finally, the active therapeutic contact of the DBS electrode, identified with a combination of post-operative imaging and post-surgery DBS programming, overlapped with the high-probability "motor" region of the GPi as defined by imaging-based methods. The consistency, validity, and clinical relevance of our findings have the potential for improving DBS targeting, by increasing patient-specific knowledge of subregions of the GPi to be targeted or avoided, at the stage of surgical planning, and later, at the stage when stimulation is adjusted.

A novel DTI-QA tool: Automated metric extraction exploiting the sphericity of an agar filled phantom

PURPOSE

To develop a quality assurance (QA) tool (acquisition guidelines and automated processing) for diffusion tensor imaging (DTI) data using a common agar-based phantom used for fMRI QA. The goal is to produce a comprehensive set of automated, sensitive and robust QA metrics.

METHODS

A readily available agar phantom was scanned with and without parallel imaging reconstruction. Other scanning parameters were matched to the human scans. A central slab made up of either a thick slice or an average of a few slices, was extracted and all processing was performed on that image. The proposed QA relies on the creation of two ROIs for processing: (i) a preset central circular region of interest (ccROI) and (ii) a signal mask for all images in the dataset. The ccROI enables computation of average signal for SNR calculations as well as average FA values. The production of the signal masks enables automated measurements of eddy current and B0 inhomogeneity induced distortions by exploiting the sphericity of the phantom. Also, the signal masks allow automated background localization to assess levels of Nyquist ghosting.

RESULTS

The proposed DTI-QA was shown to produce eleven metrics which are robust yet sensitive to image quality changes within site and differences across sites. It can be performed in a reasonable amount of scan time (~15min) and the code for automated processing has been made publicly available.

CONCLUSIONS

A novel DTI-QA tool has been proposed. It has been applied successfully on data from several scanners/platforms. The novelty lies in the exploitation of the sphericity of the phantom for distortion measurements. Other novel contributions are: the computation of an SNR value per gradient direction for the diffusion weighted images (DWIs) and an SNR value per non-DWI, an automated background detection for the Nyquist ghosting measurement and an error metric reflecting the contribution of EPI instability to the eddy current induced shape changes observed for DWIs.

Quantitative neuroimaging in mucopolysaccharidoses clinical trials

The mucopolysaccharidosis (MPS) disorders are rare lysosomal storage disorders caused by mutations in lysosomal enzymes involved in glycosaminoglycan (GAG) degradation. The resulting intracellular accumulation of GAGs leads to widespread tissue and organ dysfunction. In addition to somatic signs and symptoms, patients with MPS can present with neurological manifestations such as cognitive decline, behavioral problems (e.g. hyperactivity and aggressiveness), sleep disturbances, and/or epilepsy. These are associated with significant abnormalities of the central nervous system (CNS), including white and gray matter lesions, brain atrophy, ventriculomegaly, and spinal cord compression. In order to effectively manage and develop therapies for MPS that target neurological disease, it is important to visualize and quantify these CNS abnormalities. This review describes optimal approaches for conducting magnetic resonance imaging assessments in multi-center clinical studies, and summarizes current knowledge from neuroimaging studies in MPS disorders. The content of the review is based on presentations and discussions on these topics that were held during a meeting of an international group of experts.

Evaluating the diagnostic utility of applying a machine learning algorithm to diffusion tensor MRI measures in individuals with major depressive disorder

Using MRI to diagnose mental disorders has been a long-term goal. Despite this, the vast majority of prior neuroimaging work has been descriptive rather than predictive. The current study applies support vector machine (SVM) learning to MRI measures of brain white matter to classify adults with Major Depressive Disorder (MDD) and healthy controls. In a precisely matched group of individuals with MDD (n =25) and healthy controls (n =25), SVM learning accurately (74%) classified patients and controls across a brain map of white matter fractional anisotropy values (FA). The study revealed three main findings: 1) SVM applied to DTI derived FA maps can accurately classify MDD vs. healthy controls; 2) prediction is strongest when only right hemisphere white matter is examined; and 3) removing FA values from a region identified by univariate contrast as significantly different between MDD and healthy controls does not change the SVM accuracy. These results indicate that SVM learning applied to neuroimaging data can classify the presence versus absence of MDD and that predictive information is distributed across brain networks rather than being highly localized. Finally, MDD group differences revealed through typical univariate contrasts do not necessarily reveal patterns that provide accurate predictive information.

Improving Estimation of Fiber Orientations in Diffusion MRI Using Inter-Subject Information Sharing

Diffusion magnetic resonance imaging is widely used to investigate diffusion patterns of water molecules in the human brain. It provides information that is useful for tracing axonal bundles and inferring brain connectivity. Diffusion axonal tracing, namely tractography, relies on local directional information provided by the orientation distribution functions (ODFs) estimated at each voxel. To accurately estimate ODFs, data of good signal-to-noise ratio and sufficient angular samples are desired. This is however not always available in practice. In this paper, we propose to improve ODF estimation by using inter-subject image correlation. Specifically, we demonstrate that diffusion-weighted images acquired from different subjects can be transformed to the space of a target subject to drastically increase the number of angular samples to improve ODF estimation. This is largely due to the incoherence of the angular samples generated when the diffusion signals are reoriented and warped to the target space. To reorient the diffusion signals, we propose a new spatial normalization method that directly acts on diffusion signals using local affine transforms. Experiments on both synthetic data and real data show that our method can reduce noise-induced artifacts, such as spurious ODF peaks, and yield more coherent orientations.

Statistical estimation of white matter microstructure from conventional MRI

Diffusion tensor imaging (DTI) has become the predominant modality for studying white matter integrity in multiple sclerosis (MS) and other neurological disorders. Unfortunately, the use of DTI-based biomarkers in large multi-center studies is hindered by systematic biases that confound the study of disease-related changes. Furthermore, the site-to-site variability in multi-center studies is significantly higher for DTI than that for conventional MRI-based markers. In our study, we apply the Quantitative MR Estimation Employing Normalization (QuEEN) model to estimate the four DTI measures: MD, FA, RD, and AD. QuEEN uses a voxel-wise generalized additive regression model to relate the normalized intensities of one or more conventional MRI modalities to a quantitative modality, such as DTI. We assess the accuracy of the models by comparing the prediction error of estimated DTI images to the scan-rescan error in subjects with two sets of scans. Across the four DTI measures, the performance of the models is not consistent: Both MD and RD estimations appear to be quite accurate, while AD estimation is less accurate than MD and RD; the accuracy of FA estimation is poor. Thus, in some cases when assessing white matter integrity, it may be sufficient to acquire conventional MRI sequences alone.

The impact of quality assurance assessment on diffusion tensor imaging outcomes in a large-scale population-based cohort

BACKGROUND

Diffusion tensor imaging (DTI) is applied in investigation of brain biomarkers for neurodevelopmental and neurodegenerative disorders. However, the quality of DTI measurements, like other neuroimaging techniques, is susceptible to several confounding factors (e.g., motion, eddy currents), which have only recently come under scrutiny. These confounds are especially relevant in adolescent samples where data quality may be compromised in ways that confound interpretation of maturation parameters. The current study aims to leverage DTI data from the Philadelphia Neurodevelopmental Cohort (PNC), a sample of 1601 youths with ages of 8-21 who underwent neuroimaging, to: 1) establish quality assurance (QA) metrics for the automatic identification of poor DTI image quality; 2) examine the performance of these QA measures in an external validation sample; 3) document the influence of data quality on developmental patterns of typical DTI metrics.

METHODS

All diffusion-weighted images were acquired on the same scanner. Visual QA was performed on all subjects completing DTI; images were manually categorized as Poor, Good, or Excellent. Four image quality metrics were automatically computed and used to predict manual QA status: Mean voxel intensity outlier count (MEANVOX), Maximum voxel intensity outlier count (MAXVOX), mean relative motion (MOTION) and temporal signal-to-noise ratio (TSNR). Classification accuracy for each metric was calculated as the area under the receiver-operating characteristic curve (AUC). A threshold was generated for each measure that best differentiated visual QA status and applied in a validation sample. The effects of data quality on sensitivity to expected age effects in this developmental sample were then investigated using the traditional MRI diffusion metrics: fractional anisotropy (FA) and mean diffusivity (MD). Finally, our method of QA is compared with DTIPrep.

RESULTS

TSNR (AUC=0.94) best differentiated Poor data from Good and Excellent data. MAXVOX (AUC=0.88) best differentiated Good from Excellent DTI data. At the optimal threshold, 88% of Poor data and 91% Good/Excellent data were correctly identified. Use of these thresholds on a validation dataset (n=374) indicated high accuracy. In the validation sample 83% of Poor data and 94% of Excellent data was identified using thresholds derived from the training sample. Both FA and MD were affected by the inclusion of poor data in an analysis of an age, sex and race matched comparison sample. In addition, we show that the inclusion of poor data results in significant attenuation of the correlation between diffusion metrics (FA and MD) and age during a critical neurodevelopmental period. We find higher correspondence between our QA method and DTIPrep for Poor data, but we find our method to be more robust for apparently high-quality images.

CONCLUSION

Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.

Merits and Limits of Tractography Techniques for the Uninitiated

The implementation of fiber tracking or tractography modules in commercial navigation systems resulted in a broad availability of visualization possibilities for major white matter tracts in the neurosurgical community. Unfortunately the implemented algorithms and tracking approaches do not represent the state of the art of tractography strategies and may lead to false tracking results. The application of advanced tractography techniques for neurosurgical procedures poses even additional challenges that relate to effects of the individual anatomy that might be altered by edema and tumor, to stereotactic inaccuracies due to image distortion, as well as to registration inaccuracies and brain shift.

Real-time measurement and correction of both B0 changes and subject motion in diffusion tensor imaging using a double volumetric navigated (DvNav) sequence

Diffusion tensor imaging (DTI) requires a set of diffusion weighted measurements in order to acquire enough information to characterize local structure. The MRI scanner automatically performs a shimming process by acquiring a field map before the start of a DTI scan. Changes in B0, which can occur throughout the DTI acquisition due to several factors (including heating of the iron shim coils or subject motion), cause significant signal distortions that result in warped diffusion tensor (DT) parameter estimates. In this work we introduce a novel technique to simultaneously measure, report and correct in real time subject motion and changes in B0 field homogeneity, both in and through the imaging plane. This is achieved using double volumetric navigators (DvNav), i.e. a pair of 3D EPI acquisitions, interleaved with the DTI pulse sequence. Changes in the B0 field are evaluated in terms of zero-order (frequency) and first-order (linear gradients) shim. The ability of the DvNav to accurately estimate the shim parameters was first validated in a water phantom. Two healthy subjects were scanned both in the presence and absence of motion using standard, motion corrected (single navigator, vNav), and DvNav DTI sequences. The difference in performance between the proposed 3D EPI field maps and the standard 3D gradient echo field maps of the MRI scanner was also evaluated in a phantom and two healthy subjects. The DvNav sequence was shown to accurately measure and correct changes in B0 following manual adjustments of the scanner's central frequency and the linear shim gradients. Compared to other methods, the DvNav produced DTI results that showed greater spatial overlap with anatomical references, particularly in scans with subject motion. This is largely due to the ability of the DvNav system to correct shim changes and subject motion between each volume acquisition, thus reducing shear distortion.

Methodological considerations on tract-based spatial statistics (TBSS)

Having gained a tremendous amount of popularity since its introduction in 2006, tract-based spatial statistics (TBSS) can now be considered as the standard approach for voxel-based analysis (VBA) of diffusion tensor imaging (DTI) data. Aiming to improve the sensitivity, objectivity, and interpretability of multi-subject DTI studies, TBSS includes a skeletonization step that alleviates residual image misalignment and obviates the need for data smoothing. Although TBSS represents an elegant and user-friendly framework that tackles numerous concerns existing in conventional VBA methods, it has limitations of its own, some of which have already been detailed in recent literature. In this work, we present general methodological considerations on TBSS and report on pitfalls that have not been described previously. In particular, we have identified specific assumptions of TBSS that may not be satisfied under typical conditions. Moreover, we demonstrate that the existence of such violations can severely affect the reliability of TBSS results. With TBSS being used increasingly, it is of paramount importance to acquaint TBSS users with these concerns, such that a well-informed decision can be made as to whether and how to pursue a TBSS analysis. Finally, in addition to raising awareness by providing our new insights, we provide constructive suggestions that could improve the validity and increase the impact of TBSS drastically.

Influence of noise correction on intra- and inter-subject variability of quantitative metrics in diffusion kurtosis imaging

Diffusion kurtosis imaging (DKI) is a promising extension of diffusion tensor imaging, giving new insights into the white matter microstructure and providing new biomarkers. Given the rapidly increasing number of studies, DKI has a potential to establish itself as a valuable tool in brain diagnostics. However, to become a routine procedure, DKI still needs to be improved in terms of robustness, reliability, and reproducibility. As it requires acquisitions at higher diffusion weightings, results are more affected by noise than in diffusion tensor imaging. The lack of standard procedures for post-processing, especially for noise correction, might become a significant obstacle for the use of DKI in clinical routine limiting its application. We considered two noise correction schemes accounting for the noise properties of multichannel phased-array coils, in order to improve the data quality at signal-to-noise ratio (SNR) typical for DKI. The SNR dependence of estimated DKI metrics such as mean kurtosis (MK), mean diffusivity (MD) and fractional anisotropy (FA) is investigated for these noise correction approaches in Monte Carlo simulations and in in vivo human studies. The intra-subject reproducibility is investigated in a single subject study by varying the SNR level and SNR spatial distribution. Then the impact of the noise correction on inter-subject variability is evaluated in a homogeneous sample of 25 healthy volunteers. Results show a strong impact of noise correction on the MK estimate, while the estimation of FA and MD was affected to a lesser extent. Both intra- and inter-subject SNR-related variability of the MK estimate is considerably reduced after correction for the noise bias, providing more accurate and reproducible measures. In this work, we have proposed a straightforward method that improves accuracy of DKI metrics. This should contribute to standardization of DKI applications in clinical studies making valuable inferences in group analysis and longitudinal studies.

Advanced diffusion imaging sequences could aid assessing patients with focal cortical dysplasia and epilepsy

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Malformations of cortical development are a common cause of refractory epilepsy.

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They are often invisible on structural imaging and only detected following surgery.

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We assess a novel diffusion imaging technique (NODDI) in patients with dysplasia.

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This shows more conspicuous changes than other clinical or diffusion scans.

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This technique may assist the identification of FCD in patients with epilepsy.

Malformations of cortical development (MCD), particularly focal cortical dysplasia (FCD), are a common cause of refractory epilepsy but are often invisible on structural imaging. NODDI (neurite orientation dispersion and density imaging) is an advanced diffusion imaging technique that provides additional information on tissue microstructure, including intracellular volume fraction (ICVF), a marker of neurite density.

We applied this technique in 5 patients with suspected dysplasia to show that the additional parameters are compatible with the underlying disrupted tissue microstructure and could assist in the identification of the affected area.

The consistent finding was reduced ICVF in the area of dysplasia. In one patient, an area of reduced ICVF and increased fibre dispersion was identified that was not originally seen on the structural imaging. The focal reduction in ICVF on imaging is compatible with previous iontophoretic data in surgical specimens, was more conspicuous than on other clinical or diffusion images (supported by an increased contrast-to-noise ratio) and more localised than on previous DTI studies.

NODDI may therefore assist the clinical identification and localisation of FCD in patients with epilepsy. Future studies will assess this technique in a larger cohort including MRI negative patients.

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.

Inter subject variability and reproducibility of diffusion tensor imaging within and between different imaging sessions

The aim of these studies was to provide reference data on intersubject variability and reproducibility of diffusion tensor imaging. Healthy volunteers underwent imaging on two occasions using the same 3T Siemens Verio magnetic resonance scanner. At each session two identical diffusion tensor sequences were obtained along with standard structural imaging. Fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity maps were created and regions of interest applied in normalised space. The baseline data from all 26 volunteers were used to calculate the intersubject variability, while within session and between session reproducibility were calculated from all the available data. The reproducibility of measurements were used to calculate the overall and within session 95% prediction interval for zero change. The within and between session reproducibility data were lower than the values for intersubject variability, and were different across the brain. The regional mean (range) coefficient of variation figures for within session reproducibility were 2.1 (0.9-5.5%), 1.2 (0.4-3.9%), 1.2 (0.4-3.8%) and 1.8 (0.4-4.3%) for fractional anisotropy, apparent diffusion coefficient, axial and radial diffusivity, and were lower than between session reproducibility measurements (2.4 (1.1-5.9%), 1.9 (0.7-5.7%), 1.7 (0.7-4.7%) and 2.4 (0.9-5.8%); p<0.001). The calculated overall and within session 95% prediction intervals for zero change were similar. This study provides additional reference data concerning intersubject variability and reproducibility of diffusion tensor imaging conducted within the same imaging session and different imaging sessions. These data can be utilised in interventional studies to quantify change within a single imaging session, or to assess the significance of change in longitudinal studies of brain injury and disease.

Atlas-based and DTI-guided quantification of human brain cerebral blood flow: feasibility, quality assurance, spatial heterogeneity and age effects

Accurate and noninvasive quantification of regional cerebral blood perfusion (CBF) of the human brain tissue would advance the study of the complex interplay between human brain structure and function, in both health and disease. Despite the plethora of works on CBF in gray matter, a detailed quantitative white matter perfusion atlas has not been presented on healthy adults using the International Consortium for Brain Mapping atlases. In this study, we present a host of assurance measures such as temporal stability, spatial heterogeneity and age effects of regional and global CBF in selected deep, cortical gray matter and white matter tracts identified and quantified using diffusion tensor imaging (DTI). We utilized whole brain high-resolution DTI combined with arterial spin labeling to quantify regional CBF on 15 healthy adults aged 23.2-57.1years. We present total brain and regional CBF, corresponding volume, mean diffusivity and fractional anisotropy spatial heterogeneity, and dependence on age as additional quality assurance measures to compare with published trends using both MRI and nuclear medicine methods. Total CBF showed a steady decrease with age in gray matter (r=-0.58; P=.03), whereas total CBF of white matter did not significantly change with age (r=0.11; P=.7). This quantitative report offers a preliminary baseline of CBF, volume and DTI measurements for the design of future multicenter and clinical studies utilizing noninvasive perfusion and DT-MRI.

Anatomic MR imaging and functional diffusion tensor imaging of peripheral nerve tumors and tumorlike conditions

BACKGROUND AND PURPOSE

A number of benign and malignant peripheral nerve tumor and tumorlike conditions produce similar imaging features on conventional anatomic MR imaging. Functional MR imaging using DTI can increment the diagnostic performance in differentiation of these lesions. Our aim was to evaluate the role of 3T anatomic MR imaging and DTI in the characterization of peripheral nerve tumor and tumorlike conditions.

MATERIALS AND METHODS

Twenty-nine patients (13 men, 16 women; mean age, 41±18 years; range, 11-83 years) with a nerve tumor or tumorlike condition (25 benign, 5 malignant) underwent 3T MR imaging by using anatomic (n=29), functional diffusion, DWI (n=21), and DTI (n=24) techniques. Images were evaluated for image quality (3-point scale), ADC of the lesion, tractography, and fractional anisotropy of nerves with interobserver reliability in ADC and FA measurements.

RESULTS

No significant differences were observed in age (benign, 40±18 versus malignant, 45±19 years) and sex (benign, male/female=12:12 versus malignant, male/female=3:2) (P>.05). All anatomic (29/29, 100%) MR imaging studies received "good" quality; 20/21 (95%) DWI and 21/24 (79%) DTI studies received "good" quality. ADC of benign lesions (1.848±0.40×10(-3) mm2/s) differed from that of malignant lesions (0.900±0.25×10(-3) mm2/s, P<.001) with excellent interobserver reliability (ICC=0.988 [95% CI, 0.976-0.994]). There were no FA or ADC differences between men and women (P>.05). FA of involved nerves was lower than that in contralateral healthy nerves (P<.001) with excellent interobserver reliability (ICC=0.970 [95% CI, 0.946-0.991]). ADC on DTI and DWI was not statistically different (P>.05), with excellent intermethod reliability (ICC=0.943 [95% CI, 0.836-0.980]). Tractography differences were observed in benign and malignant lesions.

CONCLUSIONS

3T MR imaging and DTI are valuable methods for anatomic and functional evaluation of peripheral nerve lesions with excellent interobserver reliability. While tractography and low FA provide insight into neural integrity, low diffusivity values indicate malignancy in neural masses.

Whole brain approaches for identification of microstructural abnormalities in individual patients: comparison of techniques applied to mild traumatic brain injury

PURPOSE

Group-wise analyses of DTI in mTBI have demonstrated evidence of traumatic axonal injury (TAI), associated with adverse clinical outcomes. Although mTBI is likely to have a unique spatial pattern in each patient, group analyses implicitly assume that location of injury will be the same across patients. The purpose of this study was to optimize and validate a procedure for analysis of DTI images acquired in individual patients, which could detect inter-individual differences and be applied in the clinical setting, where patients must be assessed as individuals.

MATERIALS AND METHODS

After informed consent and in compliance with HIPAA, 34 mTBI patients and 42 normal subjects underwent 3.0 Tesla DTI. Four voxelwise assessment methods (standard Z-score, "one vs. many" t-test, Family-Wise Error Rate control using pseudo t-distribution, EZ-MAP) for use in individual patients, were applied to each patient's fractional anisotropy (FA) maps and tested for its ability to discriminate patients from controls. Receiver Operating Characteristic (ROC) analyses were used to define optimal thresholds (voxel-level significance and spatial extent) for reliable and robust detection of mTBI pathology.

RESULTS

ROC analyses showed EZ-MAP (specificity 71%, sensitivity 71%), "one vs. many" t-test and standard Z-score (sensitivity 65%, specificity 76% for both methods) resulted in a significant area under the curve (AUC) score for discriminating mTBI patients from controls in terms of the total number of abnormal white matter voxels detected while the FWER test was not significant. EZ-MAP is demonstrated to be robust to assumptions of Gaussian behavior and may serve as an alternative to methods that require strict Gaussian assumptions.

CONCLUSION

EZ-MAP provides a robust approach for delineation of regional abnormal anisotropy in individual mTBI patients.

Improving DTI tractography by including diagonal tract propagation

Tractography algorithms have been developed to reconstruct likely WM pathways in the brain from diffusion tensor imaging (DTI) data. In this study, an elegant and simple means for improving existing tractography algorithms is proposed by allowing tracts to propagate through diagonal trajectories between voxels, instead of only rectilinearly to their facewise neighbors. A series of tests (using both real and simulated data sets) are utilized to show several benefits of this new approach. First, the inclusion of diagonal tract propagation decreases the dependence of an algorithm on the arbitrary orientation of coordinate axes and therefore reduces numerical errors associated with that bias (which are also demonstrated here). Moreover, both quantitatively and qualitatively, including diagonals decreases overall noise sensitivity of results and leads to significantly greater efficiency in scanning protocols; that is, the obtained tracts converge much more quickly (i.e., in a smaller amount of scanning time) to those of data sets with high SNR and spatial resolution. Importantly, the inclusion of diagonal propagation adds essentially no appreciable time of calculation or computational costs to standard methods. This study focuses on the widely-used streamline tracking method, FACT (fiber assessment by continuous tracking), and the modified method is termed "FACTID" (FACT including diagonals).

Diffusion tensor imaging fiber tracking using navigated brain stimulation--a feasibility study

BACKGROUND

Navigated brain stimulation (NBS) is a newly evolving technique. In addition to its supposed purpose, e.g., preoperative mapping of the central region, little is known about its further use in neurosurgery. We evaluated the usefulness of diffusion tensor imaging fiber tracking (DTI-FT) based on NBS compared to conventional characterization of the seed region.

METHODS

We examined 30 patients with tumors in or close to the corticospinal tract (CST) using NBS with the Nexstim eXimia system. NBS was performed for motor cortex mapping, and DTI-FT was performed by three different clinicians using BrainLAB iPlan® Cranial 3.0.1 at two time points. Number of fibers, tract volume, aberrant tracts, and proximity to the tumor were compared between the two methods.

RESULTS

We recognized a higher number of fibers (1,298 ± 1,279 vs. 916 ± 986 fibers; p < 0.01), tract volume (23.0 ± 15.3 vs. 18.3 ± 14.0 cm(3); p < 0.01), and aberrant tracts (0.6 ± 0.5 vs. 0.3 ± 0.5 aberrant tracts/tracked CST; p < 0.001) when the seed region was defined conventionally, while proximity of the tracts to the tumor did not differ. While NBS-based DTI-FT is independent of the planning clinician, conventional outlining of the seed region shows generally higher variability between investigators.

CONCLUSIONS

Conventional DTI-FT showed significant differences between the two modalities, most likely because of the more specific definition of the seed region when DTI-FT is based on NBS. Moreover, NBS-aided DTI fiber tracking is user-independent and, therefore, a method for further standardization of DTI fiber tracking.

Multicenter reliability of diffusion tensor imaging

A number of studies are now collecting diffusion tensor imaging (DTI) data across sites. While the reliability of anatomical images has been established by a number of groups, the reliability of DTI data has not been studied as extensively. In this study, five healthy controls were recruited and imaged at eight imaging centers. Repeated measures were obtained across two imaging protocols allowing intra-subject and inter-site variability to be assessed. Regional measures within white matter were obtained for standard rotationally invariant measures: fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity. Intra-subject coefficient of variation (CV) was typically <1% for all scalars and regions. Inter-site CV increased to ~1%-3%. Inter-vendor variation was similar to inter-site variability. This variability includes differences in the actual implementation of the sequence.

Correlation of quantitative diffusion tensor tractography with clinical grades of subacute sclerosing panencephalitis

BACKGROUND AND PURPOSE

SSPE is a persistent infection of the central nervous system caused by the measles virus. The correlation between the clinical staging and conventional MR imaging is usually poor. The purpose of this study was to determine whether tract-specific DTI measures in the major white mater tracts correlate with clinical grades as defined by the Jabbour classification for SSPE.

MATERIALS AND METHODS

Quantitative DTT was performed on 20 patients with SSPE (mean age, 9 years) and 14 age- and sex-matched controls. All patients were graded on the basis of the Jabbour classification into grade II (n=9), grade III (n=6), and grade IV (n=5) SSPE. The major white matter tracts quantified included the CC, SLF, ILF, CST, CNG, SCP, MCP, ICP, ATR, STR, and PTR.

RESULTS

Although a successive decrease in mean FA values was observed in all the fiber tracts except for the SCP and ICP, moving from controls to grade IV, a significant inverse correlation between clinical grade and mean FA values was observed only in the splenium (r=-0.908, P<.001), CST (r=-0.663, P=.013), SLF (r=-0.533, P=.050), ILF (r=-0.776, P=.001), STR (r=-0.538, P=.047), and PTR (r=-0.686, P=.035) fibers. No significant correlation of mean MD values from these white matter tracts was observed with clinical grades of the disease.

CONCLUSIONS

We conclude that the grade of encephalopathy correlates inversely with the tract-specific mean FA values. This information may be valuable in studying the disease progression with time and in assessing the therapeutic response in the future.

A framework on surface-based connectivity quantification for the human brain

Quantifying the connectivity between arbitrary surface patches in the human brain cortex can be used in studies on brain function and to characterize clinical diseases involving abnormal connectivity. Cortical regions of human brain in their natural forms can be represented in surface formats. In this paper, we present a framework to quantify connectivity using cortical surface segmentation and labeling from structural magnetic resonance images, tractography from diffusion tensor images, and nonlinear inter-subject registration. For a single subject, the connectivity intensity of any point on the cortical surface is set to unity if the point is connected and zero if it is not connected. The connectivity proportion is defined as the ratio of the total connected surface area to the total area of the surface patch. By nonlinearly registering the connectivity data of a group of normal controls into a template space, a population connectivity metric can be defined as either the average connectivity intensity of a cortical point or the average connectivity proportion of a cortical region. In the template space, a connectivity profile and a connectivity histogram of an arbitrary cortical region of interest can then be derived from these connectivity quantification values. Results from the application of these quantification metrics to a population of schizophrenia patients and normal controls are presented, revealing connectivity signatures of specified cortical regions and detecting connectivity abnormalities.

A diffusion tensor imaging study on the white matter skeleton in individuals with sports-related concussion

Recognizing and managing the effects of cerebral concussion is very challenging, given the discrete symptomatology. Most individuals with sports-related concussion will not score below 15 on the Glasgow Coma Scale, but will present with rapid onset of short-lived neurological impairment, demonstrating no structural changes on traditional magnetic resonance imaging (MRI) and computed tomography (CT) scans. The return-to-play decision is one of the most difficult responsibilities facing the physician, and so far this decision has been primarily based on neurological examination, symptom checklists, and neuropsychological (NP) testing. Diffusion tensor imaging (DTI) may be a more objective tool to assess the severity and recovery of function after concussion. We assessed white matter (WM) fiber tract integrity in varsity level college athletes with sports-related concussion without loss of consciousness, who experienced protracted symptoms for at least 1 month after injury. Evaluation of fractional anisotropy (FA) and mean diffusivity (MD) of the WM skeleton using tract-based spatial statistics (TBSS) revealed a large cluster of significantly increased MD for concussed subjects in several WM fiber tracts in the left hemisphere, including parts of the inferior/superior longitudinal and fronto-occipital fasciculi, the retrolenticular part of the internal capsule, and posterior thalamic and acoustic radiations. Qualitative comparison of average FA and MD suggests that with increasing level of injury severity (ranging from sports-related concussion to severe traumatic brain injury), MD might be more sensitive at detecting mild injury, whereas FA captures more severe injuries. In conclusion, the TBSS analysis used to evaluate diffuse axonal injury of the WM skeleton seems sensitive enough to detect structural changes in sports-related concussion.

Diffusion tensor imaging of the pediatric spinal cord at 1.5T: preliminary results

BACKGROUND AND PURPOSE

Recent studies suggest that pediatric subjects as old as 8-years-of-age may have difficulty with the ISNCSCI examinations. Our aim was to investigate DTI parameters of healthy spinal cord in children with noncervical IS for comparison with children with SCI and to prospectively evaluate reliability measures of DTI and to correlate the measures obtained in children with SCI with the ISNCSCI.

MATERIALS AND METHODS

Five controls with thoracic and lumbar IS and 5 children with cervical SCI were imaged twice by using a single-shot echo-planar diffusion-weighted sequence. Axial imaging was performed to cover the entire cervical spinal cord in controls. For the SCI subjects, 2 vertebral bodies above and below the injury were imaged. FA and D values were obtained at different levels of the cervical spinal cord. All subjects with SCI had undergone ISNCSCI clinical examinations. Statistical analysis was performed to access differences of the DTI indices between the controls and SCI subjects, reproducibility measurements, and correlations between DTI and ISNCSCI.

RESULTS

Subjects with SCI showed reduced FA and increased D values compared with controls. Test-retest reproducibility showed good ICC coefficients in all the DTI index values among controls (≥0.9), while the SCI group showed moderate ICC (≥0.77). There were statistically significant correlations between the various DTI indices and ISNCSCI scores.

CONCLUSIONS

Preliminary DTI indices in children were determined and showed good reproducibility. Reduced FA and increased D values were seen in children with SCI in comparison with controls and showed good clinical correlation with ISNCSCI examinations.

Structure of the fetal brain: what we are learning from diffusion tensor imaging

Human brain anatomy is extraordinarily complex, and yet, its origin is a simple tubular structure. It is characterized by dramatic structural changes during fetal development. Revealing detailed anatomy at different stages of human fetal brain development not only aids in understanding this highly ordered process but also provides clues to detect abnormalities caused by genetic or environmental factors. However, anatomical studies of human brain development during this period are surprisingly scarce, and histology-based atlases have become available only recently. Diffusion tensor imaging (DTI), a recently developed technology of magnetic resonance imaging (MRI), is capable of noninvasively delineating macroscopic anatomical components with high contrast and revealing structures at the microscopic level. In this article, the fetal brain white matter is explored using contrasts from DTI-derived images and axonal reconstruction from DTI tractography. The highly organized structures in the cerebral layer have been revealed with primary direction of diffusion tensors. Complementary to the histology, the DTI of the fetal brain provides a valuable resource to understand the structural development of the entire brain. The resultant database will provide reference standards for diagnostic radiology of premature newborns.