Comparison of gradient encoding schemes for diffusion-tensor MRI

Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study

Introduction

Multi-shell diffusion Magnetic Resonance Imaging (dMRI) data has been widely used to characterise white matter microstructure in several neurodegenerative diseases. The lack of standardised dMRI protocols often implies the acquisition of redundant measurements, resulting in prolonged acquisition times. In this study, we investigate the impact of the number of gradient directions on Diffusion Tensor Imaging (DTI) and on Neurite Orientation Dispersion and Density Imaging (NODDI) metrics.

Methods

Data from 124 healthy controls collected in three different longitudinal studies were included. Using an in-house algorithm, we reduced the number of gradient directions in each data shell. We estimated DTI and NODDI measures on six white matter bundles clinically relevant for neurodegenerative diseases.

Results

Fractional Anisotropy (FA) measures on bundles where data were sampled at the 30% rate, showed a median L1 distance of up to 3.92% and a 95% CI of (1.74, 8.97)% when compared to those obtained at reference sampling. Mean Diffusivity (MD) reached up to 4.31% and a 95% CI of (1.60, 16.98)% on the same premises. At a sampling rate of 50%, we obtained a median of 3.90% and a 95% CI of (1.99, 16.65)% in FA, and 5.49% with a 95% CI of (2.14, 21.68)% in MD. The Intra-Cellular volume fraction (ICvf) median L1 distance was up to 2.83% with a 95% CI of (1.98, 4.82)% at a 30% sampling rate and 3.95% with a 95% CI of (2.39, 7.81)% at a 50% sampling rate. The volume difference of the reconstructed white matter at reference and 50% sampling reached a maximum of (2.09 ± 0.81)%.

Discussion

In conclusion, DTI and NODDI measures reported at reference sampling were comparable to those obtained when the number of dMRI volumes was reduced by up to 30%. Close to reference DTI and NODDI metrics were estimated with a significant reduction in acquisition time using three shells, respectively with: 4 directions at a b value of 700 s/mm2, 14 at 1000 s/mm2, and 32 at 2000 s/mm2. The study revealed aspects that can be important for large-scale clinical studies on bundle-specific diffusion MRI.

Effect of number of diffusion encoding directions in neonatal diffusion tensor imaging using Tract-Based Spatial Statistical analysis

Diffusion tensor imaging (DTI) has been used to study the developing brain in early childhood, infants and in utero studies. In infants, number of used diffusion encoding directions has traditionally been smaller in earlier studies down to the minimum of 6 orthogonal directions. Whereas the more recent studies often involve more directions, number of used directions remain an issue when acquisition time is optimized without compromising on data quality and in retrospective studies. Variability in the number of used directions may introduce bias and uncertainties to the DTI scalar estimates that affect cross-sectional and longitudinal study of the brain. We analysed DTI images of 133 neonates, each data having 54 directions after quality control, to evaluate the effect of number of diffusion weighting directions from 6 to 54 with interval of 6 to the DTI scalars with Tract-Based Spatial Statistics (TBSS) analysis. The TBSS analysis was applied to DTI scalar maps, and the mean region of interest (ROI) values were extracted using JHU atlas. We found significant bias in ROI mean values when only 6 directions were used (positive in fractional anisotropy [FA] and negative in fractional anisotropy [MD], axial diffusivity [AD] and fractional anisotropy [RD]), while when using 24 directions and above, the difference to scalar values calculated from 54 direction DTI was negligible. In repeated measures voxel-wise analysis, notable differences to 54 direction DTI were observed with 6, 12 and 18 directions. DTI measurements from data with at least 24 directions may be used in comparisons with DTI measurements from data with higher numbers of directions.

Synthesis of patient-specific multipoint 4D flow MRI data of turbulent aortic flow downstream of stenotic valves

We propose to synthesize patient-specific 4D flow MRI datasets of turbulent flow paired with ground truth flow data to support training of inference methods. Turbulent blood flow is computed based on the Navier-Stokes equations with moving domains using realistic boundary conditions for aortic shapes, wall displacements and inlet velocities obtained from patient data. From the simulated flow, synthetic multipoint 4D flow MRI data is generated with user-defined spatiotemporal resolutions and reconstructed with a Bayesian approach to compute time-varying velocity and turbulence maps. For MRI data synthesis, a fixed hypothetical scan time budget is assumed and accordingly, changes to spatial resolution and time averaging result in corresponding scaling of signal-to-noise ratios (SNR). In this work, we focused on aortic stenotic flow and quantification of turbulent kinetic energy (TKE). Our results show that for spatial resolutions of 1.5 and 2.5 mm and time averaging of 5 ms as encountered in 4D flow MRI in practice, peak total turbulent kinetic energy downstream of a 50, 75 and 90% stenosis is overestimated by as much as 23, 15 and 14% (1.5 mm) and 38, 24 and 23% (2.5 mm), demonstrating the importance of paired ground truth and 4D flow MRI data for assessing accuracy and precision of turbulent flow inference using 4D flow MRI exams.

A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis

Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p <10-4) than the FA obtained using the standard pipeline. For the clinical datasets, the network FA retained the same microstructural characteristics as the FA calculated with all DW volumes using the standard method. At the subject level, the comparison between white matter (WM) ground truth FA values and network FA showed the same distribution; at the group level, statistical differences of WM values detected in the clinical datasets with the ground truth FA were reproduced when using values from the network FA, i.e., the network retained sensitivity to pathology. In conclusion, the proposed network provides a clinically available method to obtain FA from a generic set of 10 DW volumes acquirable in 1 minute, augmenting data quality compared to direct model fitting, reducing the possibility of bias from sub-sampled data, and retaining FA pathological sensitivity, which is very attractive for clinical applications.

Accuracy and reliability of diffusion imaging models

Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.

Nonparametric D-R1-R2 distribution MRI of the living human brain

Diffusion-relaxation correlation NMR can simultaneously characterize both the microstructure and the local chemical composition of complex samples that contain multiple populations of water. Recent developments on tensor-valued diffusion encoding and Monte Carlo inversion algorithms have made it possible to transfer diffusion-relaxation correlation NMR from small-bore scanners to clinical MRI systems. Initial studies on clinical MRI systems employed 5D D-R₁ and D-R₂ correlation to characterize healthy brain in vivo. However, these methods are subject to an inherent bias that originates from not including R₂ or R₁ in the analysis, respectively. This drawback can be remedied by extending the concept to 6D D-R₁-R₂ correlation. In this work, we present a sparse acquisition protocol that records all data necessary for in vivo 6D D-R₁-R₂ correlation MRI across 633 individual measurements within 25 min-a time frame comparable to previous lower-dimensional acquisition protocols. The data were processed with a Monte Carlo inversion algorithm to obtain nonparametric 6D D-R₁-R₂ distributions. We validated the reproducibility of the method in repeated measurements of healthy volunteers. For a post-therapy glioblastoma case featuring cysts, edema, and partially necrotic remains of tumor, we present representative single-voxel 6D distributions, parameter maps, and artificial contrasts over a wide range of diffusion-, R₁-, and R₂-weightings based on the rich information contained in the D-R₁-R₂ distributions.

Same Brain, Different Look?-The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters

In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyra^fit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.

Task-driven assessment of experimental designs in diffusion MRI: A computational framework

This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments' ability to faithfully recover microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI experimental design (protocol, parameter-estimation method, model, etc.), experiments are simulated start-to-finish and task performance is computed from receiver operating characteristic (ROC) curves and associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline's task performance predictions against clinical results, comparing in-silico predictions to real-world ROC/AUC; and second, a demonstration of the pipeline's advantages over traditional CED approaches, using two simulated clinical classification tasks. Comparison with clinical datasets validates our method's predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.

On the down-sampling of diffusion MRI data along the angular dimension

BACKGROUND

It has been established that the diffusion gradient directions in diffusion MRI should be uniformly distributed in 3D spherical space, so that orientation-dependent diffusion properties (e.g., fractional anisotropy or FA) can be properly quantified. Sometimes the acquired data need to be down-sampled along the angular dimension before computing diffusion properties (e.g., to exclude data points corrupted by motion artifact; to harmonize data obtained with different protocols). It is important to quantitatively assess the impact of data down-sampling on measurement of diffusion properties.

MATERIALS AND METHODS

Here we report 1) a numerical procedure for down-sampling diffusion MRI (e.g., for data harmonization), and 2) a spatial uniformity index of diffusion directions, aiming to predict the quality of the chosen down-sampling schemes (e.g., from data harmonization; or rejection of motion corrupted data points). We quantitatively evaluated human diffusion MRI data, which were down-sampled from 64 or 60 diffusion gradient directions to 30 directions, in terms of their 1) FA value accuracy (using fully-sampled data as the ground truth), 2) FA fitting residuals, and 3) spatial uniformity indices.

RESULTS

Our experimental data show that the proposed spatial uniformity index is correlated with errors in FA obtained from down-sampled diffusion MRI data. The FA fitting residuals that are typically used to assess diffusion MRI quality are not correlated with either FA errors or spatial uniformity index.

CONCLUSIONS

These results suggest that the spatial uniformity index could be more valuable in assessing quality of down-sampled diffusion MRI data, as compared with FA fitting residual measures. We expect that our implemented software procedure should prove valuable for 1) guiding data harmonization for multi-site diffusion MRI studies, and 2) assessing the impact of rejecting motion corrupted data points on the accuracy of diffusion measures.

Optimal Diffusion Gradient Encoding Scheme for Diffusion Tensor Imaging Based on Golden Ratio

BACKGROUND

The accuracy of the estimated diffusion tensor elements can be improved by using a well-chosen magnetic resonance imaging (MRI) diffusion gradient encoding scheme (DGES). Conversely, diffusion tensor imaging (DTI) is typically challenged by the subject's motion during data acquisition and results in corrupted image data.

PURPOSE

To identify a reliable DGES based on the golden ratio (GR) that can generate an arbitrary number of uniformly distributed directions to precisely estimate the DTI parameters of partially acquired datasets owing to subject motion.

STUDY TYPE

Prospective.

POPULATION

Simulations study; three healthy volunteers.

FIELD STRENGTH/SEQUENCE

3 T/DTI data were obtained using a single-shot echo planar imaging sequence.

STATISTICAL TESTS

A paired sample t-test and the Wilcoxon test were used, P < 0.05 was considered statistically significant.

ASSESSMENT

Two corrupted scenarios A and B were considered and evaluated. For the simulation study, the GR DGES and generated subsets were compared with the Jones and spiral DGESs by electric potential (EP) and condition number (CN). For the human study, the specific subsets A and B selected from scenarios A and B were used for MRI to evaluate fractional anisotropic (FA) map.

RESULTS

For the simulation study, the EPs of the GR (14034.25 ± 12957.24) DGES were significantly lower than the Jones (15112.81 ± 13926.08) and spiral (14297.49 ± 13232.94) DGESs. CN variations of GR (1.633 ± 0.024) DGES were significantly lower than Jones (1.688 ± 0.119) and spiral (4.387 ± 2.915) DGESs. For the human study, GR (0.008 ± 0.020) DGES performed similarly with Jones (0.008 ± 0.022) DGES and was superior to spiral (0.022 ± 0.054) DGES in the FA map error.

DATA CONCLUSION

The GR DGES ensured that directions of the complete sets and subsets were uniform. The GR DGES had lower error propagation sensitivity, which can help image infants or patients who cannot stay still during scanning.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

Diffusion Tensor Imaging Color-Coded Maps: An Alternative to Tractography

INTRODUCTION

White matter tracts can be observed using tractograms generated from diffusion tensor imaging (DTI). However, the dependence of these white matter tract images on subjective variables, including how seed points are placed and the preferred level of fractional anisotropy, introduces interobserver inconsistency and potential lack of reliability. We propose that color-coded maps (CCM) generated from DTI can be a preferred method for the visualization of important white matter tracts, circumventing bias in preoperative brain tumor resection planning.

METHODS

DTI was acquired retrospectively in 25 patients with brain tumors. Lesions included 15 tumors of glial origin, 9 metastatic tumors, 2 meningiomas, and 1 cavernous angioma. Tractograms of the pyramidal tract and/or optic radiations, based on tumor location, were created by marking seed regions of interest using known anatomical locations. We compared the degree of tract involvement and white matter alteration between CCMs and tractograms. Neurological outcomes were obtained from chart reviews.

RESULTS

The pyramidal tract was evaluated in 20/25 patients, the visual tracts were evaluated in 10/25, and both tracts were evaluated in 5/25. In 19/25 studies, the same patterns of white matter alternations were found between the CCMs and tractograms. In the 6 patients where patterns differed, 2 tractograms were not useful in determining pattern alteration; in the remaining 4/6, no practical difference was seen in comparing the studies. Two patients were lost to follow-up. Thirteen patients were neurologically improved or remained intact after intervention. In these, 10 of the 13 patients showed tumor-induced white matter tract displacement on CCM. Twelve patients had no improvement of their preoperative deficit. In 9 of these 12 patients, CCM showed white matter disruption.

CONCLUSION

CCMs provide a convenient, practical, and objective method of visualizing white matter tracts, obviating the need for potentially subjective and time-consuming tractography. CCMs are at least as reliable as tractograms in predicting neurological outcomes after neurosurgical intervention.

Brain MRI Diffusion Encoding Direction Number Affects Tract-Based Spatial Statistics Results in Multiple Sclerosis

BACKGROUND AND PURPOSE

Diffusion tensor imaging (DTI) is a promising approach to detect the underlying brain pathology. These alterations can be seen in several diseases such as multiple sclerosis. Tract-based spatial statistics (TBSS) is an easy to use and robust way for analyzing diffusion data. The effect of acquisition parameters of DTI on TBSS has not been evaluated, especially the number of diffusion encoding directions (NDED), which is directly proportional with scan time.

METHODS

We analyzed a large set of DTI data of healthy controls (N = 126) and multiple sclerosis patients (N = 78). The highest NDED (60 directions) was reduced and a tensor calculation was done separately for every subset. We calculated the mean and standard deviation of DTI parameters under the white matter mask. Moreover, the FMRIB Software Library TBSS pipeline was used on DTI images with 15, 30, 45, and 60 directions to compare differences between groups. Mean DTI parameters were compared between groups as a function of NDED.

RESULTS

The mean value of FA and AD decreased with increasing number of directions. This was more pronounced in areas with smaller FA values. RD and MD were constant. The skeleton size reduced with elevating NDED along with the number of significant voxels. The TBSS analysis showed significant differences between groups throughout the majority of the skeleton and the group difference was associated with NDED.

CONCLUSION

Our results suggested that results of TBSS depended on the NDED, which should be considered when comparing DTI data with varying protocols.

5D Flow Tensor MRI to Efficiently Map Reynolds Stresses of Aortic Blood Flow In-Vivo

Diseased heart valves perturb normal blood flow with a range of hemodynamic and pathologic consequences. In order to better stratify patients with heart valve disease, a comprehensive characterization of blood flow including turbulent contributions is desired. In this work we present a framework to efficiently quantify velocities and Reynolds stresses in the aorta in-vivo. Using a highly undersampled 5D Flow MRI acquisition scheme with locally low-rank image reconstruction, multipoint flow tensor encoding in short and predictable scan times becomes feasible (here, 10 minutes), enabling incorporation of the protocol into clinical workflows. Based on computer simulations, a 19-point 5D Flow Tensor MRI encoding approach is proposed. It is demonstrated that, for in-vivo resolution and signal-to-noise ratios, sufficient accuracy and precision of velocity and turbulent shear stress quantification is achievable. In-vivo proof of concept is demonstrated on patients with a bio-prosthetic heart valve and healthy controls. Results demonstrate that aortic turbulent shear stresses and turbulent kinetic energy are elevated in the patients compared to the healthy subjects. Based on these data, it is concluded that 5D Flow Tensor MRI holds promise to provide comprehensive flow assessment in patients with heart valve diseases.

Diffusion tensor imaging of the abdominal organs: Influence of oriented intravoxel flow compartments

Water flow in partially oriented intravoxel compartments mimics an anisotropic fast-diffusion regime, which contributes to the signal attenuation in diffusion-weighted images. In the abdominal organs, this flow may reflect physiological fluid movements (eg, tubular urine flow in kidneys, or bile flow through the liver) and have a clinical relevance. This study investigated the influence of anisotropic intravoxel water flow on diffusion tensor imaging (DTI) of the abdominal organs. Diffusion-weighted images were acquired in five healthy volunteers using an EPI sequence with diffusion preparation (TR/TE: 1000 ms/71 ms; b-values: 0, 10, 20, 40, 70, 120, 250, 450, 700, 1000 s/mm² ; 12 noncollinear diffusion-encoding directions). DTI of liver and kidneys was performed assuming (i) monoexponential decay of the diffusion-weighted signal, and (ii) accounting for potential anisotropy of the fast-diffusion compartments using a tensorial generalization of the IVIM model. Additionally, potential dependency of the metrics of the tensors from the anatomical location was evaluated. Significant differences in the metrics of the diffusion tensor (DT) were found in both liver and kidneys when comparing the two models. In both organs, the trace and the fractional anisotropy of the DT were significantly higher in the monoexponential model than when accounting for perfusion. The comparison of areas of the liver proximal to the hilum with distal regions and of renal cortex with the medulla also proved a location dependency of the size of the fast-diffusion compartments. Pseudo-diffusion correction in DTI enables the assessment of the solid parenchyma regardless of the organ perfusion or other pseudo-diffusive fluid movements. This may have a clinical relevance in the assessment of parenchymal pathologies (eg, liver fibrosis). The fast pseudo-diffusion components present a detectable anisotropy, which may reflect the hepatic microcirculation or other sources of mesoscopic fluid movement in the abdominal organs.

Correction for fast pseudo-diffusive fluid motion contaminations in diffusion tensor imaging

In this prospective study, we quantified the fast pseudo-diffusion contamination by blood perfusion or cerebrospinal fluid (CSF) intravoxel incoherent movements on the measurement of the diffusion tensor metrics in healthy brain tissue. Diffusion-weighted imaging (TR/TE = 4100 ms/90 ms; b-values: 0, 5, 10, 20, 35, 55, 80, 110, 150, 200, 300, 500, 750, 1000, 1300 s/mm², 20 diffusion-encoding directions) was performed on a cohort of five healthy volunteers at 3 Tesla. The projections of the diffusion tensor along each diffusion-encoding direction were computed using a two b-value approach (2b), by fitting the signal to a monoexponential curve (mono), and by correcting for fast pseudo-diffusion compartments using the biexponential intravoxel incoherent motion model (IVIM) (bi). Fractional anisotropy (FA) and mean diffusivity (MD) of the diffusion tensor were quantified in regions of interest drawn over white matter areas, gray matter areas, and the ventricles. A significant dependence of the MD from the evaluation method was found in all selected regions. A lower MD was computed when accounting for the fast-diffusion compartments. A larger dependence was found in the nucleus caudatus (bi: median 0.86 10^-3 mm²/s, Δ2b: -11.2%, Δmono: -14.4%; p = 0.007), in the anterior horn (bi: median 2.04 10^-3 mm²/s, Δ2b: -9.4%, Δmono: -11.5%, p = 0.007) and in the posterior horn of the lateral ventricles (bi: median 2.47 10^-3 mm²/s, Δ2b: -5.5%, Δmono: -11.7%; p = 0.007). Also for the FA, the signal modeling affected the computation of the anisotropy metrics. The deviation depended on the evaluated region with significant differences mainly in the nucleus caudatus (bi: median 0.15, Δ2b: +39.3%, Δmono: +14.7%; p = 0.022) and putamen (bi: median 0.19, Δ2b: +3.1%, Δmono: +17.3%; p = 0.015). Fast pseudo-diffusive regimes locally affect diffusion tensor imaging (DTI) metrics in the brain. Here, we propose the use of an IVIM-based method for correction of signal contaminations through CSF or perfusion.

Feasibility of fast brain diffusion MRI to quantify white matter injury in pediatric hydrocephalus

OBJECTIVE

Traditionally, diffusion MRI (dMRI) has been performed in parallel with high-resolution conventional MRI, which requires long scan times and may require sedation or general anesthesia in infants and young children. Conversely, fast brain MRI permits image acquisition without the need for sedation, although its short pulse sequences, susceptibility to motion artifact, and contrast resolution have limited its use to assessing ventricular size or major structural variations. Here, the authors demonstrate the feasibility of leveraging a 3-direction fast brain MRI protocol to obtain reliable dMRI measures.

METHODS

Fast brain MRI with 3-direction dMRI was performed in infants and children before and after hydrocephalus treatment. Regions of interest in the posterior limbs of the internal capsules (PLICs) and the genu of the corpus callosum (gCC) were drawn on diffusion-weighted images, and mean diffusivity (MD) data were extracted. Ventricular size was determined by the frontal occipital horn ratio (FOHR). Differences between and within groups pre- and posttreatment, and FOHR-MD correlations were assessed.

RESULTS

Of 40 patients who met inclusion criteria (median age 27.5 months), 15 (37.5%), 17 (42.5%), and 8 (20.0%) had posthemorrhagic hydrocephalus (PHH), congenital hydrocephalus (CH), or no intracranial abnormality (controls), respectively. A hydrocephalus group included both PHH and CH patients. Prior to treatment, the FOHR (p < 0.001) and PLIC MD (p = 0.027) were greater in the hydrocephalus group than in the controls. While the mean gCC MD in the hydrocephalus group (1.10 × 10-3 mm2/sec) was higher than that of the control group (0.98), the difference was not significant (p = 0.135). Following a median follow-up duration of 14 months, decreases in FOHR, PLIC MD, and gCC MD were observed in the hydrocephalus group and were similar to those in the control group (p = 0.107, p = 0.702, and p = 0.169, respectively). There were no correlations identified between FOHR and MDs at either time point.

CONCLUSIONS

The utility of fast brain MRI can be extended beyond anatomical assessments to obtain dMRI measures. A reduction in PLIC and gCC MD to levels similar to those of controls was observed within 14 months following shunt surgery for hydrocephalus in PHH and CH infants. Further studies are required to assess the role of fast brain dMRI for assessing clinical outcomes in pediatric hydrocephalus patients.

Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain

The anatomical wiring of the brain is a central focus in network neuroscience. Diffusion MRI tractography offers the unique opportunity to investigate the brain fiber architecture in vivo and noninvasively. However, its reliability is still highly debated. Here, we explored the ability of diffusion MRI tractography to match invasive anatomical tract-tracing connectivity data of the ferret brain. We also investigated the influence of several state-of-the-art tractography algorithms on this match to ground truth connectivity data. Tract-tracing connectivity data were obtained from retrograde tracer injections into the occipital, parietal, and temporal cortices of adult ferrets. We found that the relative densities of projections identified from the anatomical experiments were highly correlated with the estimates from all the studied diffusion tractography algorithms (Spearman's rho ranging from 0.67 to 0.91), while only small, nonsignificant variations appeared across the tractography algorithms. These results are comparable to findings reported in mouse and monkey, increasing the confidence in diffusion MRI tractography results. Moreover, our results provide insights into the variations of sensitivity and specificity of the tractography algorithms, and hence into the influence of choosing one algorithm over another.

Multimodal MRI Imaging of Apoptosis-Triggered Microstructural Alterations in the Postnatal Cerebral Cortex

Prematurely born children often develop neurodevelopmental delay that has been correlated with reduced growth and microstructural alterations in the cerebral cortex. Much research has focused on apoptotic neuronal cell death as a key neuropathological features following preterm brain injuries. How scattered apoptotic death of neurons may contribute to microstructural alterations remains unknown. The present study investigated in a rat model the effects of targeted neuronal apoptosis on cortical microstructure using in vivo MRI imaging combined with neuronal reconstruction and histological analysis. We describe that mild, targeted death of layer IV neurons in the developing rat cortex induces MRI-defined metabolic and microstructural alterations including increased cortical fractional anisotropy. Delayed architectural modifications in cortical gray matter and myelin abnormalities in the subcortical white matter such as hypomyelination and microglia activation follow the acute phase of neuronal death and axonal degeneration. These results establish the link between mild cortical apoptosis and MRI-defined microstructure changes that are reminiscent to those previously observed in preterm babies.

Longitudinal In Vivo Diffusion Magnetic Resonance Imaging Remote from the Lesion Site in Rat Spinal Cord Injury

Diffusion tensor imaging (DTI) has demonstrated success as a biomarker of spinal cord injury (SCI) severity as shown from numerous pre-clinical studies. However, artifacts from stabilization hardware at the lesion have precluded its use for longitudinal assessments. Previous research has documented ex vivo diffusion changes in the spinal cord both caudal and cranial to the injury epicenter. The aim of this study was to use a rat contusion model of SCI to evaluate the utility of in vivo cervical DTI after a thoracic injury. Forty Sprague-Dawley rats underwent a thoracic contusion (T8) of mild, moderate, severe, or sham severity. Magnetic resonance imaging (MRI) of the cervical cord was performed at 2, 30, and 90 days post-injury, and locomotor performance was assessed weekly using the Basso, Bresnahan, and Beattie (BBB) scoring scale. The relationships between BBB scores and MRI were assessed using region of interest analysis and voxel-wise linear regression of DTI, and free water elimination (FWE) modeling to reduce partial volume effects. At 90 days, axial diffusivity (AD_FWE), mean diffusivity (MD_FWE), and free water fraction (FWF_FWE) using the FWE model were found to be significantly correlated with BBB score. FWE was found to be more predictive of injury severity than conventional DTI, specifically at later time-points. This study validated the use of FWE technique in spinal cord and demonstrated its sensitivity to injury remotely.

Physical activity is associated with left corticospinal tract microstructure in bipolar depression

Psychomotor retardation and reduced daily activities are core features of the depressive syndrome including bipolar disorder (BD). It was the aim of this study to investigate white matter microstructure of the motor system in BD during depression and its association with motor activity. We hypothesized reduced physical activity, microstructural alterations of motor tracts and different associations between activity levels and motor tract microstructure in BD. Nineteen bipolar patients with a current depressive episode (BD) and 19 healthy controls (HC) underwent diffusion weighted magnetic resonance imaging (DW-MRI)-scans. Quantitative motor activity was assessed with 24 h actigraphy recordings. Bilateral corticospinal tracts (CST), interhemispheric connections between the primary motor cortices (M1) and between the pre-supplementary motor areas (pre-SMA) were reconstructed individually based on anatomical landmarks using Diffusion Tensor Imaging (DTI) based tractography. Mean fractional anisotropy (FA) was sampled along the tracts. To enhance specificity of putative findings a segment of the optic radiation was reconstructed as comparison tract. Analyses were complemented with Tract Based Spatial Statistics (TBSS) analyses. BD had lower activity levels (AL). There was a sole increase of fractional anisotropy (FA) in BD in the left CST. Further, there was a significant group x AL interaction for FA of the left CST pointing to a selective positive association between FA and AL in BD. The comparison tract and TBSS analyses did not detect significant group differences. Our results point to white matter microstructure alterations of the left CST in BD. The positive association between motor activity and white matter microstructure suggests a compensatory role of the left CST for psychomotor retardation in BD.

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Daily physical activity is reduced in bipolar patients with a current depressive episode (BD)

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The left corticospinal tract (CST) in BD shows increased fractional anisotropy (FA)

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Increases of FA in the left corticospinal tract in BD are related to less pronounced psychomotor retardation

Filter-probe diffusion imaging improves spinal cord injury outcome prediction

OBJECTIVE

Diffusion-weighted imaging (DWI) is a powerful tool for investigating spinal cord injury (SCI), but has limited specificity for axonal damage, which is the most predictive feature of long-term functional outcome. In this study, a technique designed to detect acute axonal injury, filter-probe double diffusion encoding (FP-DDE), is compared with standard DWI for predicting long-term functional and cellular outcomes.

METHODS

This study extends FP-DDE to predict long-term functional and histological outcomes in a rat SCI model of varying severities (n = 58). Using a 9.4T magnetic resonance imaging (MRI) system, a whole-cord FP-DDE spectroscopic voxel was acquired in 3 minutes at the lesion site and compared to DWI at 48 hours postinjury. Relationships with chronic (30-day) locomotor and histological outcomes were evaluated with linear regression.

RESULTS

The FP-DDE measure of parallel diffusivity (ADC|| ) was significantly related to chronic hind limb locomotor functional outcome (R2  = 0.63, p < 0.0001), and combining this measurement with acute functional scores demonstrated prognostic benefit versus functional testing alone (p = 0.0007). Acute ADC|| measurements were also more closely related to the number of injured axons measured 30 days after the injury than standard DWI. Furthermore, acute FP-DDE images showed a clear and easily interpretable pattern of injury that closely corresponded with chronic MRI and histology observations.

INTERPRETATION

Collectively, these results demonstrate FP-DDE benefits from greater specificity for acute axonal damage in predicting functional and histological outcomes with rapid acquisition and fully automated analysis, improving over standard DWI. FP-DDE is a promising technique compatible with clinical settings, with potential research and clinical applications for evaluation of spinal cord pathology. Ann Neurol 2018;83:37-50.

modCHIMERA: a novel murine closed-head model of moderate traumatic brain injury

Traumatic brain injury is a major source of global disability and mortality. Preclinical TBI models are a crucial component of therapeutic investigation. We report a tunable, monitored model of murine non-surgical, diffuse closed-head injury—modCHIMERA—characterized by impact as well as linear and rotational acceleration. modCHIMERA is based on the Closed-Head Impact Model of Engineered Rotational Acceleration (CHIMERA) platform. We tested this model at 2 energy levels: 1.7 and 2.1 Joules—substantially higher than previously reported for this system. Kinematic analysis demonstrated linear acceleration exceeding injury thresholds in humans, although outcome metrics tracked impact energy more closely than kinematic parameters. Acute severity metrics were consistent with a complicated-mild or moderate TBI, a clinical population characterized by high morbidity but potentially reversible pathology. Axonal injury was multifocal and bilateral, neuronal death was detected in the hippocampus, and microglial neuroinflammation was prominent. Acute functional analysis revealed prolonged post-injury unconsciousness, and decreased spontaneous behavior and stimulated neurological scores. Neurobehavioral deficits were demonstrated in spatial learning/memory and socialization at 1-month. The overall injury profile of modCHIMERA corresponds with the range responsible for a substantial portion of TBI-related disability in humans. modCHIMERA should provide a reliable platform for efficient analysis of TBI pathophysiology and testing of treatment modalities.

Diffusion tensor optical coherence tomography

In situ measurements of diffusive particle transport provide insight into tissue architecture, drug delivery, and cellular function. Analogous to diffusion-tensor magnetic resonance imaging (DT-MRI), where the anisotropic diffusion of water molecules is mapped on the millimeter scale to elucidate the fibrous structure of tissue, here we propose diffusion-tensor optical coherence tomography (DT-OCT) for measuring directional diffusivity and flow of optically scattering particles within tissue. Because DT-OCT is sensitive to the sub-resolution motion of Brownian particles as they are constrained by tissue macromolecules, it has the potential to quantify nanoporous anisotropic tissue structure at micrometer resolution as relevant to extracellular matrices, neurons, and capillaries. Here we derive the principles of DT-OCT, relating the detected optical signal from a minimum of six probe beams with the six unique diffusion tensor and three flow vector components. The optimal geometry of the probe beams is determined given a finite numerical aperture, and a high-speed hardware implementation is proposed. Finally, Monte Carlo simulations are employed to assess the ability of the proposed DT-OCT system to quantify anisotropic diffusion of nanoparticles in a collagen matrix, an extracellular constituent that is known to become highly aligned during tumor development.

Diffusion Tensor Imaging (DTI)

Diffusion Tensor Imaging is an MRI technique that allows in vivo noninvasive measurement of the translational motion of water, providing information about its anisotropy (or lack of it) in different tissues. DTI has been commonly used to quantitatively measure the integrity of tissues which may be compromised by neurological disease, such as white matter tracks of the brain, which normally impart significant anisotropy to water motion in healthy brains. However, this anisotropic effect is diminished when axonal or neuronal damage is present. This chapter describes a standard protocol for DTI data acquisition in preclinical studies.

Velocity reconstruction with nonconvex optimization for low-velocity-encoding phase-contrast MRI

PURPOSE

To introduce and demonstrate a nonconvex optimization method for reconstructing velocity data from low-velocity-encoding (V_enc ) phase-contrast MRI data.

THEORY AND METHODS

Solving for velocity values from phase-contrast MRI data was formulated as a nonconvex optimization problem. Weighting was added to account for intravoxel dephasing, and a Laplacian-based regularization was used to account for residual velocity aliasing. The reconstruction was tested with two low-V_enc schemes: dual-V_enc and a multidirectional high-moment encoding. The reconstruction method was tested in a digital simulation, in flow phantoms, and in healthy volunteers (N = 5).

RESULTS

The nonconvex-optimization reconstruction velocity error was lower than the conventional reconstruction in simulations (4.6 versus 3.0 cm/s for multidirectional high moment, 8.3 versus 3.8 cm/s for dual-V_enc ) and in flow phantoms (23.9 versus 5.9 cm/s for multidirectional high moment, 15.2 versus 6.4 cm/s for dual-V_enc ). Qualitative assessment of velocity fields in all experiments, including healthy volunteers, showed decreased apparent noise in the velocity fields and fewer phase wraps. No additional velocity bias in measured velocities was seen in volunteers with the proposed method.

CONCLUSIONS

The proposed nonconvex-optimization reconstruction method incorporates additional information to solve for velocities when using any type of low-V_enc (high-moment) acquisition. The method reduces the amount of residual phase aliasing, and decreases velocity errors that result from intravoxel dephasing. These improvements allow for more robust acquisitions, and for V_enc to be lowered 2 to 4 times relative to conventional acquisitions, thereby increasing the velocity-to-noise ratio. Magn Reson Med, 2017. © 2017 International Society for Magnetic Resonance in Medicine. Magn Reson Med 80:42-52, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Assessment of Reynolds stress components and turbulent pressure loss using 4D flow MRI with extended motion encoding

PURPOSE

To measure the Reynolds stress tensor using 4D flow MRI, and to evaluate its contribution to computed pressure maps.

METHODS

A method to assess both velocity and Reynolds stress using 4D flow MRI is presented and evaluated. The Reynolds stress is compared by cross-sectional integrals of the Reynolds stress invariants. Pressure maps are computed using the pressure Poisson equation-both including and neglecting the Reynolds stress.

RESULT

Good agreement is seen for Reynolds stress between computational fluid dynamics, simulated MRI, and MRI experiment. The Reynolds stress can significantly influence the computed pressure loss for simulated (eg, -0.52% vs -15.34% error; P < 0.001) and experimental (eg, 306 ± 11 vs 203 ± 6 Pa; P < 0.001) data. A 54% greater pressure loss is seen at the highest experimental flow rate when accounting for Reynolds stress (P < 0.001).

CONCLUSION

4D flow MRI with extended motion-encoding enables quantification of both the velocity and the Reynolds stress tensor. The additional information provided by this method improves the assessment of pressure gradients across a stenosis in the presence of turbulence. Unlike conventional methods, which are only valid if the flow is laminar, the proposed method is valid for both laminar and disturbed flow, a common presentation in diseased vessels. Magn Reson Med 79:1962-1971, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

Impact of Gradient Number and Voxel Size on Diffusion Tensor Imaging Tractography for Resective Brain Surgery

OBJECTIVE

To explore quantitatively and qualitatively how the number of gradient directions (NGD) and spatial resolution (SR) affect diffusion tensor imaging (DTI) tractography in patients planned for brain tumor surgery, using routine clinical magnetic resonance imaging protocols.

METHODS

Of 67 patients with intracerebral lesions who had 2 different DTI scans, 3 DTI series were reconstructed to compare the effects of NGD and SR. Tractographies for 4 clinically relevant tracts (corticospinal tract, superior longitudinal fasciculus, optic radiation, and inferior fronto-occipital fasciculus) were constructed with a probabilistic tracking algorithm and automated region of interest placement and compared for 3 quantitative measurements: tract volume, median fiber density, and mean fractional anisotropy, using linear mixed-effects models. The mean tractography volume and intersubject reliability were visually compared across scanning protocols, to assess the clinical relevance of the quantitative differences.

RESULTS

Both NGD and SR significantly influenced tract volume, median fiber density, and mean fractional anisotropy, but not to the same extent. In particular, higher NGD increased tract volume and median fiber density. More importantly, these effects further increased when tracts were affected by disease. The effects were tract specific, but not dependent on threshold. The superior longitudinal fasciculus and inferior fronto-occipital fasciculus showed the most significant differences. Qualitative assessment showed larger tract volumes given a fixed confidence level, and better intersubject reliability for the higher NGD protocol. SR in the range we considered seemed less relevant than NGD.

CONCLUSIONS

This study indicates that, under time constraints of clinical imaging, a higher number of diffusion gradients is more important than spatial resolution for superior DTI probabilistic tractography in patients undergoing brain tumor surgery.

Diffusion tensor imaging in abdominal organs

Initially, diffusion tensor imaging (DTI) was mainly applied in studies of the human brain to analyse white matter tracts. As DTI is outstanding for the analysis of tissue´s microstructure, the interest in DTI for the assessment of abdominal tissues has increased continuously in recent years. Tissue characteristics of abdominal organs differ substantially from those of the human brain. Further peculiarities such as respiratory motion and heterogenic tissue composition lead to difficult conditions that have to be overcome in DTI measurements. Thus MR measurement parameters have to be adapted for DTI in abdominal organs. This review article provides information on the technical background of DTI with a focus on abdominal imaging, as well as an overview of clinical studies and application of DTI in different abdominal regions. Copyright © 2015 John Wiley & Sons, Ltd.

Rapid in vivo detection of rat spinal cord injury with double-diffusion-encoded magnetic resonance spectroscopy

PURPOSE

Diffusion-weighted imaging is a common experimental tool for evaluating spinal cord injury (SCI), yet it suffers from complications that decrease its clinical effectiveness. The most commonly used technique, diffusion tensor imaging (DTI), is often confounded by effects of edema accompanying acute SCI, limiting its sensitivity to the important functional status marker of axonal integrity. The purpose of this study is to introduce a novel diffusion-acquisition method with the goal of overcoming these limitations.

METHODS

A double diffusion encoding (DDE) pulse sequence was implemented with a diffusion-weighted filter orthogonal to the spinal cord for suppressing nonneural signals prior to diffusion weighting parallel to the cord. A point-resolved spectroscopy readout (DDE-PRESS) was used for improved sensitivity and compared with DTI in a rat model of SCI with varying injury severities.

RESULTS

The DDE-PRESS parameter, restricted fraction, showed a strong relationship with injury severity (P < 0.001, R²  = 0.67). Although the whole-cord averaged DTI parameter values exhibited only minor injury relationships, a weighted region of interest (ROI) based DTI analysis improved sensitivity to injury (P < 0.001, R²  = 0.66).

CONCLUSIONS

In a rat model of SCI, DDE-PRESS demonstrated high sensitivity to injury with substantial decreases in acquisition time and data processing. This method shows promise for application in rapid evaluation of SCI severity. Magn Reson Med 77:1639-1649, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Financial literacy is associated with white matter integrity in old age

Financial literacy, the ability to understand, access, and utilize information in ways that contribute to optimal financial outcomes, is important for independence and wellbeing in old age. We previously reported that financial literacy is associated with greater functional connectivity between brain regions in old age. Here, we tested the hypothesis that higher financial literacy would be associated with greater white matter integrity in old age. Participants included 346 persons without dementia (mean age=81.36, mean education=15.39, male/female=79/267, mean MMSE=28.52) from the Rush Memory and Aging Project. Financial literacy was assessed using a series of questions imbedded as part of an ongoing decision making study. White matter integrity was assessed with diffusion anisotropy measured with diffusion tensor magnetic resonance imaging (DTI). We tested the hypothesis that higher financial literacy is associated with higher diffusion anisotropy in white matter, adjusting for the effects of age, education, sex, and white matter hyperintense lesions. We then repeated the analysis also adjusting for cognitive function. Analyses revealed regions with significant positive associations between financial literacy and diffusion anisotropy, and many remained significant after accounting for cognitive function. White matter tracts connecting right hemisphere temporal-parietal brain regions were particularly implicated. Greater financial literacy is associated with higher diffusion anisotropy in white matter of nondemented older adults after adjusting for important covariates. These results suggest that financial literacy is positively associated with white matter integrity in old age.

Integrative Bayesian analysis of neuroimaging-genetic data with application to cocaine dependence

Neuroimaging and genetic studies provide distinct and complementary information about the structural and biological aspects of a disease. Integrating the two sources of data facilitates the investigation of the links between genetic variability and brain mechanisms among different individuals for various medical disorders. This article presents a general statistical framework for integrative Bayesian analysis of neuroimaging-genetic (iBANG) data, which is motivated by a neuroimaging-genetic study in cocaine dependence. Statistical inference necessitated the integration of spatially dependent voxel-level measurements with various patient-level genetic and demographic characteristics under an appropriate probability model to account for the multiple inherent sources of variation. Our framework uses Bayesian model averaging to integrate genetic information into the analysis of voxel-wise neuroimaging data, accounting for spatial correlations in the voxels. Using multiplicity controls based on the false discovery rate, we delineate voxels associated with genetic and demographic features that may impact diffusion as measured by fractional anisotropy (FA) obtained from DTI images. We demonstrate the benefits of accounting for model uncertainties in both model fit and prediction. Our results suggest that cocaine consumption is associated with FA reduction in most white matter regions of interest in the brain. Additionally, gene polymorphisms associated with GABAergic, serotonergic and dopaminergic neurotransmitters and receptors were associated with FA.

Diffusion Tensor Imaging in Patients with Glioblastoma Multiforme Using the Supertoroidal Model

Purpose

Diffusion Tensor Imaging (DTI) is a powerful imaging technique that has led to improvements in the diagnosis and prognosis of cerebral lesions and neurosurgical guidance for tumor resection. Traditional tensor modeling, however, has difficulties in differentiating tumor-infiltrated regions and peritumoral edema. Here, we describe the supertoroidal model, which incorporates an increase in surface genus and a continuum of toroidal shapes to improve upon the characterization of Glioblastoma multiforme (GBM).

Materials and Methods

DTI brain datasets of 18 individuals with GBM and 18 normal subjects were acquired using a 3T scanner. A supertoroidal model of the diffusion tensor and two new diffusion tensor invariants, one to evaluate diffusivity, the toroidal volume (TV), and one to evaluate anisotropy, the toroidal curvature (TC), were applied and evaluated in the characterization of GBM brain tumors. TV and TC were compared with the mean diffusivity (MD) and fractional anisotropy (FA) indices inside the tumor, surrounding edema, as well as contralateral to the lesions, in the white matter (WM) and gray matter (GM).

Results

The supertoroidal model enhanced the borders between tumors and surrounding structures, refined the boundaries between WM and GM, and revealed the heterogeneity inherent to tumor-infiltrated tissue. Both MD and TV demonstrated high intensities in the tumor, with lower values in the surrounding edema, which in turn were higher than those of unaffected brain parenchyma. Both TC and FA were effective in revealing the structural degradation of WM tracts.

Conclusions

Our findings indicate that the supertoroidal model enables effective tensor visualization as well as quantitative scalar maps that improve the understanding of the underlying tissue structure properties. Hence, this approach has the potential to enhance diagnosis, preoperative planning, and intraoperative image guidance during surgical management of brain lesions.

Glyph-Based Comparative Visualization for Diffusion Tensor Fields

Diffusion Tensor Imaging (DTI) is a magnetic resonance imaging modality that enables the in-vivo reconstruction and visualization of fibrous structures. To inspect the local and individual diffusion tensors, glyph-based visualizations are commonly used since they are able to effectively convey full aspects of the diffusion tensor. For several applications it is necessary to compare tensor fields, e.g., to study the effects of acquisition parameters, or to investigate the influence of pathologies on white matter structures. This comparison is commonly done by extracting scalar information out of the tensor fields and then comparing these scalar fields, which leads to a loss of information. If the glyph representation is kept, simple juxtaposition or superposition can be used. However, neither facilitates the identification and interpretation of the differences between the tensor fields. Inspired by the checkerboard style visualization and the superquadric tensor glyph, we design a new glyph to locally visualize differences between two diffusion tensors by combining juxtaposition and explicit encoding. Because tensor scale, anisotropy type, and orientation are related to anatomical information relevant for DTI applications, we focus on visualizing tensor differences in these three aspects. As demonstrated in a user study, our new glyph design allows users to efficiently and effectively identify the tensor differences. We also apply our new glyphs to investigate the differences between DTI datasets of the human brain in two different contexts using different b-values, and to compare datasets from a healthy and HIV-infected subject.

Preliminary Study of MR Diffusion Tensor Imaging of the Liver for the Diagnosis of Hepatocellular Carcinoma

OBJECTIVES

To evaluate the feasibility of differentiating between hepatocellular carcinomas (HCC) and healthy liver using diffusion tensor imaging (DTI).

MATERIAL AND METHODS

All subjects underwent an abdominal examination on a 3.0T MRI scanner. Two radiologists independently scored the image quality (IQ). An optimal set of DTI parameters was obtained from a group of fifteen volunteers with multiple b-values (100, 300, 500, and 800 s/mm2) and various diffusion-encoding directions (NED = 6, 9, and 12)using two way ANOVA analysis. Eighteen Patients with HCC underwent DTI scans with the optimized parameters. Fractional anisotropy(FA) and average apparent diffusion coefficient (ADC) values were measured. The differences of FA and ADC values between liver healthy region and HCC lesion were compared through paired t tests.

RESULTS

There were no significant changes in liver IQ and FA/ADC values with increased NED(P >0.05), whereas the liver IQ and FA/ADC values decreased significantly with increased b-values(P <0.05). Good IQ, acceptable scan time and reasonable FA/ADC values were acquired using NED = 9 with b-value of (0,300) s/mm2. Using the optimized DTI sequence, ADC value of the tumor lesion was significantly lower than that of the healthy liver region (1.30 ± 0.34×10-3 vs 1.52 ± 0.27×10-3 mm2/s, P = 0.013), whereas the mean FA value of the tumor lesion (0.42 ± 0.11) was significantly higher than the normal liver region (0.32 ± 0.10) (P = 0.004).

CONCLUSION

Either FA or ADC value from DTI can be used to differentiate HCC from healthy liver. HCC lead to higher FA value and lower ADC value on DTI than healthy liver.

Functional Neuroimaging: Fundamental Principles and Clinical Applications

Functional imaging modalities, such as functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI), are rapidly changing the scope and practice of neuroradiology. While these modalities have long been used in research, they are increasingly being used in clinical practice to enable reliable identification of eloquent cortex and white matter tracts in order to guide treatment planning and to serve as a diagnostic supplement when traditional imaging fails. An understanding of the scientific principles underlying fMRI and DTI is necessary in current radiological practice. fMRI relies on a compensatory hemodynamic response seen in cortical activation and the intrinsic discrepant magnetic properties of deoxy- and oxyhemoglobin. Neuronal activity can be indirectly visualized based on a hemodynamic response, termed neurovascular coupling. fMRI demonstrates utility in identifying areas of cortical activation (i.e., task-based activation) and in discerning areas of neuronal connectivity when used during the resting state, termed resting state fMRI. While fMRI is limited to visualization of gray matter, DTI permits visualization of white matter tracts through diffusion restriction along different axes. We will discuss the physical, statistical and physiological principles underlying these functional imaging modalities and explore new promising clinical applications.

Effects of rejecting diffusion directions on tensor-derived parameters

Diffusion Tensor Imaging (DTI) is adversely affected by subject motion. It is necessary to discard the corrupted images before diffusion parameter estimation. However, the consequences of rejecting those images are not well understood. In this study, we investigated the effects of excluding one or more volumes of diffusion weighted images by analyzing the changes in fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD) and the primary eigenvector (V1). Based on the full set of diffusion images acquired by the Jones30 diffusion scheme, we generated incomplete sets of at least six in three different ways: random, uniform and clustered rejections. The results showed that MD was not significantly affected by rejecting diffusion directions. In the cases of random rejections, FA, AD, RD and V1 were overestimated more greatly with increasing number of rejections and the overestimations were worse in low FA regions than high FA regions. For uniform rejections, at which the remaining diffusion directions are evenly distributed on a sphere, little change was observed in FA and in V1. Clustered rejections, on the other hand, displayed the most significant overestimation of the parameters, and the resulting accuracy depended on the relative orientation of the underlying fibers with respect to the excluded directions. In practice, if diffusion direction data is excluded, it is important to note the number and location of directions rejected, in order to make a more precise analysis of the data.

Limbic white matter microstructure plasticity reflects recovery from depression

BACKGROUND

White matter microstructure alterations of limbic and reward pathways have been reported repeatedly for depressive episodes in major depressive disorder (MDD) and bipolar disorder (BD). However, findings during remission are equivocal. It was the aim of this study to investigate if white matter microstructure changes during the time course of clinical remission.

METHODS

Fifteen depressed patients (11 MDD, 4 BD) underwent diffusion-weighted MRI both during depression, and during remission following successful antidepressive treatment (average time interval between scans = 6 months). Fractional anisotropy (FA) was sampled along reconstructions of the supero-lateral medial forebrain bundle (slMFB), the cingulum bundle (CB), the uncinate fasciculus (UF), the parahippocampal cingulum (PHC) and the fornix. Repeated measures ANCOVAs controlling for the effect of age were calculated for each tract.

RESULTS

There was a significant main effect of time (inter-scan interval) for mean-FA for the right CB and for the left PHC. For both pathways there was a significant time × age interaction. In the right CB, FA increased in younger patients, while FA decreased in older patients. In the left PHC, a reverse pattern was seen. FA changes in the right CB correlated positively with symptom reductions. Mean-FA of UF, slMFB and fornix did not change between the two time points.

LIMITATIONS

All patients were medicated, sample size, and lack of control group.

CONCLUSIONS

Right CB and left PHC undergo age-dependent plastic changes during the course of remission and may serve as a state marker in depression. UF, slMFB and FO microstructure remains stable.

Lateral ventricular cerebrospinal fluid diffusivity as a potential neuroimaging marker of brain temperature in multiple sclerosis: a hypothesis and implications

In this retrospective study we tested the hypothesis that the net effect of impaired electrical conduction and therefore increased heat dissipation in multiple sclerosis (MS) results in elevated lateral ventricular (LV) cerebrospinal fluid (CSF) diffusivity as a measure of brain temperature estimated in vivo using diffusion tensor imaging (DTI). We used validated DTI-based segmentation methods to obtain normalized LV-CSF volume and its corresponding CSF diffusivity in 108 MS patients and 103 healthy controls in the age range of 21-63 years. The LV CSF diffusivity was ~2% higher in MS compared to controls that correspond to a temperature rise of ~1°C that could not be explained by changes in the CSF viscosity due to altered CSF protein content in MS. The LV diffusivity decreased with age in healthy controls (r=-0.29; p=0.003), but not in MS (r=0.15; p=0.11), possibly related to MS pathology. Age-adjusted LV diffusivity increased with lesion load (r=0.518; p=1×10(-8)). Our data suggest that the total brain lesion load is the primary contributor to the increase in LV CSF diffusivity in MS. These findings suggest that LV diffusivity is a potential in vivo biomarker of the mismatch between heat generation and dissipation in MS. We also discuss limitations and possible confounders.

Lactoferrin during lactation protects the immature hypoxic-ischemic rat brain

Objective

Lactoferrin (Lf) is an iron-binding glycoprotein secreted in maternal milk presenting anti-inflammatory and antioxidant properties. It shows efficient absorption into the brain from nutritional source. Brain injury frequently resulting from cerebral hypoxia-ischemia (HI) has a high incidence in premature infants with ensuing neurodevelopmental disabilities. We investigated the neuroprotective effect of maternal nutritional supplementation with Lf during lactation in a rat model of preterm HI brain injury using magnetic resonance imaging (MRI), brain gene, and protein expression.

Methods

Moderate brain HI was induced using unilateral common carotid artery occlusion combined with hypoxia (6%, 30 min) in the postnatal day 3 (P3) rat brain (24–28 weeks human equivalent). High-field multimodal MRI techniques were used to investigate the effect of maternal Lf supplementation through lactation. Expression of cytokine coding genes (TNF-α and IL-6), the prosurvival/antiapoptotic AKT protein and caspase-3 activation were also analyzed in the acute phase after HI.

Results

MRI analysis demonstrated reduced cortical injury in Lf rats few hours post-HI and in long-term outcome (P25). Lf reduced HI-induced modifications of the cortical metabolism and altered white matter microstructure was recovered in Lf-supplemented rats at P25. Lf supplementation significantly decreased brain TNF-α and IL-6 gene transcription, increased phosphorylated AKT levels and reduced activation of caspase-3 at 24 h post-injury.

Interpretation

Lf given through lactation to rat pups with cerebral HI injury shows neuroprotective effects on brain metabolism, and cerebral gray and white matter recovery. This nutritional intervention may be of high interest for the clinical field of preterm brain neuroprotection.

Quantifying synovial inflammation: Emerging imaging techniques

Imaging techniques to assess synovial inflammation includes radiography, ultrasound, computed tomography, magnetic resonance imaging (MRI) and recently positron emission tomography. The ideal objective of imaging approaches are to quantify synovial inflammation by capturing features such as synovial hyperplasia, neo-angiogenesis and infiltration of immune cells in the synovium. This may enable clinicians to estimate response to therapy by measuring the improvement in the inflammatory signals at the level of synovium. Ultrasound can provide information regarding thickening of the synovial membrane and can reveal increased synovial blood flow using power Doppler technique. Bone marrow edema and synovial membrane thickness on MRI scan may serve as indicators for arthritis progression. Enhancement of the synovium on dynamic contrast MRI may closely mirror the inflammatory activity in the synovium. Diffusion tensor imaging is an advance MRI approach that evaluates the inflammation related to cell infiltration or aggregation in an inflamed synovium. In this review, we summarize the newer imaging techniques and their developments to evaluate synovial inflammation.