Diffusion weighted image denoising using overcomplete local PCA

Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space

Diffusion magnetic resonance imaging (DMRI) suffers from lower signal-to-noise-ratio (SNR) due to MR signal attenuation associated with the motion of water molecules. To improve SNR, the non-local means (NLM) algorithm has demonstrated state-of-the-art performance in noise reduction. However, existing NLM algorithms do not take into account explicitly the fact that DMRI signal can vary significantly with local fiber orientations. Applying NLM naïvely can hence blur subtle structures and aggravate partial volume effects. To overcome this limitation, we improve NLM by performing neighborhood matching in non-flat domains and removing noise with information from both x -space (spatial domain) and q -space (wavevector domain). Specifically, we first encode the q -space sampling domain using a graph. We then perform graph framelet transforms to extract robust rotation-invariant features for each sampling point in x-q space. The resulting features are employed for robust neighborhood matching to locate recurrent information. Finally, we remove noise via an NLM framework. To adapt to the various types of noise in multi-coil MR imaging, we transform the signal before denoising so that it is Gaussian-distributed, allowing noise removal to be carried out in an unbiased manner. Our method is able to more effectively locate recurrent information in white matter structures with different orientations, avoiding the blurring effects caused by naïvely applying NLM. Experiments on synthetic, repetitively-acquired, and infant DMRI data demonstrate that our method is able to preserve subtle structures while effectively removing noise.

Longitudinal changes in rich club organization and cognition in cerebral small vessel disease

Cerebral small vessel disease (SVD) is considered the most important vascular contributor to the development of cognitive impairment and dementia. There is increasing awareness that SVD exerts its clinical effects by disrupting white matter connections, predominantly disrupting connections between rich club nodes, a set of highly connected and interconnected regions. Here we examined the progression of disturbances in rich club organization in older adults with SVD and their associations with conventional SVD markers and cognitive decline. We additionally investigated associations of baseline network measures with dementia. In 270 participants of the RUN DMC study, we performed diffusion tensor imaging (DTI) and cognitive assessments longitudinally. Rich club organization was examined in structural networks derived from DTI followed by deterministic tractography. Global efficiency (p<0.05) and strength of rich club connections (p<0.001) declined during follow-up. Decline in strength of peripheral connections was associated with a decline in overall cognition (β=0.164; p<0.01), psychomotor speed (β=0.151; p<0.05) and executive function (β=0.117; p<0.05). Baseline network measures were reduced in participants with dementia, and the association between WMH and dementia was causally mediated by global efficiency (p = =0.037) and peripheral connection strength (p = =0.040). SVD-related disturbances in rich club organization progressed over time, predominantly in participants with severe SVD. In this study, we found no specific role of rich club connectivity disruption in causing cognitive decline or dementia. The effect of WMH on dementia was mediated by global network efficiency and the strength of peripheral connections, suggesting an important role for network disruption in causing cognitive decline and dementia in older adults with SVD.

Complex diffusion-weighted image estimation via matrix recovery under general noise models

We propose a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions. It operates on the complex data resulting from a sensitivity encoding reconstruction, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum. Simple strategies are presented to deal with phase inconsistencies and optimize patch construction. The pertinence of our contributions is quantitatively validated on synthetic data, an in vivo adult example, and challenging neonatal and fetal cohorts. Our methodology is compared with related approaches, which generally operate on magnitude-only data and use data-based noise level estimation and singular value truncation. Visual examples are provided to illustrate effectiveness in generating denoised and debiased diffusion estimates with well preserved spatial and diffusion detail.

Denoising High-Field Multi-Dimensional MRI With Local Complex PCA

Modern high field and ultra high field magnetic resonance imaging (MRI) experiments routinely collect multi-dimensional data with high spatial resolution, whether multi-parametric structural, diffusion or functional MRI. While diffusion and functional imaging have benefited from recent advances in multi-dimensional signal analysis and denoising, structural MRI has remained untouched. In this work, we propose a denoising technique for multi-parametric quantitative MRI, combining a highly popular denoising method from diffusion imaging, over-complete local PCA, with a reconstruction of the complex-valued MR signal in order to define stable estimates of the noise in the decomposition. With this approach, we show signal to noise ratio (SNR) improvements in high resolution MRI without compromising the spatial accuracy or generating spurious perceptual boundaries.

Multimodal Hippocampal Subfield Grading For Alzheimer's Disease Classification

Numerous studies have proposed biomarkers based on magnetic resonance imaging (MRI) to detect and predict the risk of evolution toward Alzheimer's disease (AD). Most of these methods have focused on the hippocampus, which is known to be one of the earliest structures impacted by the disease. To date, patch-based grading approaches provide among the best biomarkers based on the hippocampus. However, this structure is complex and is divided into different subfields, not equally impacted by AD. Former in-vivo imaging studies mainly investigated structural alterations of these subfields using volumetric measurements and microstructural modifications with mean diffusivity measurements. The aim of our work is to improve the current classification performances based on the hippocampus with a new multimodal patch-based framework combining structural and diffusivity MRI. The combination of these two MRI modalities enables the capture of subtle structural and microstructural alterations. Moreover, we propose to study the efficiency of this new framework applied to the hippocampal subfields. To this end, we compare the classification accuracy provided by the different hippocampal subfields using volume, mean diffusivity, and our novel multimodal patch-based grading framework combining structural and diffusion MRI. The experiments conducted in this work show that our new multimodal patch-based method applied to the whole hippocampus provides the most discriminating biomarker for advanced AD detection while our new framework applied into subiculum obtains the best results for AD prediction, improving by two percentage points the accuracy compared to the whole hippocampus.

Low-Rank Tensor Models for Improved Multi-Dimensional MRI: Application to Dynamic Cardiac T 1 Mapping

Multi-dimensional, multi-contrast magnetic resonance imaging (MRI) has become increasingly available for comprehensive and time-efficient evaluation of various pathologies, providing large amounts of data and offering new opportunities for improved image reconstructions. Recently, a cardiac phase-resolved myocardial T 1 mapping method has been introduced to provide dynamic information on tissue viability. Improved spatio-temporal resolution in clinically acceptable scan times is highly desirable but requires high acceleration factors. Tensors are well-suited to describe inter-dimensional hidden structures in such multi-dimensional datasets. In this study, we sought to utilize and compare different tensor decomposition methods, without the use of auxiliary navigator data. We explored multiple processing approaches in order to enable high-resolution cardiac phase-resolved myocardial T 1 mapping. Eight different low-rank tensor approximation and processing approaches were evaluated using quantitative analysis of accuracy and precision in T 1 maps acquired in six healthy volunteers. All methods provided comparable T 1 values. However, the precision was significantly improved using local processing, as well as a direct tensor rank approximation. Low-rank tensor approximation approaches are well-suited to enable dynamic T 1 mapping at high spatio-temporal resolutions.

MIND food and speed of processing training in older adults with low education, the MINDSpeed Alzheimer's disease prevention pilot trial

BACKGROUND

Multiple national organizations and leaders have called for increased attention to dementia prevention in those most vulnerable, for example persons with limited formal education. Prevention recommendations have included calls for multicomponent interventions that have the potential to improve both underlying neurobiological health and the ability to function despite neurobiological pathology, or what has been termed cognitive reserve.

OBJECTIVES

Test feasibility, treatment modifier, mechanism, and cognitive function effects of a multicomponent intervention consisting of foods high in polyphenols (i.e., MIND foods) to target neurobiological health, and speed of processing training to enhance cognitive reserve. We refer to this multicomponent intervention as MINDSpeed.

DESIGN

MINDSpeed is being evaluated in a 2 × 2 randomized factorial design with 180 participants residing independently in a large Midwestern city. Qualifying participants are 60 years of age or older with no evidence of dementia, and who have completed 12 years or less of education. All participants receive a study-issued iPad to access the custom study application that enables participants, depending on randomization, to select either control or MIND food, and to play online cognitive games, either speed of processing or control games.

METHODS

All participants complete informed consent and baseline assessment, including urine and blood samples. Additionally, up to 90 participants will complete neuroimaging. Assessments are repeated immediately following 12 weeks of active intervention, and at 24 weeks post-randomization. The primary outcome is an executive cognitive composite score. Secondary outcomes include oxidative stress, pro-inflammatory cytokines, and neuroimaging-captured structural and functional metrics of the hippocampus and cortical brain regions.

SUMMARY

MINDSpeed is the first study to evaluate the multicomponent intervention of high polyphenol intake and speed of processing training. It is also one of the first dementia prevention trials to target older adults with low education. The results of the study will guide future dementia prevention efforts and trials in high risk populations.

White matter alterations in early-stage Alzheimer's disease: A tract-specific study

Introduction

Diffusion magnetic resonance imaging may allow for microscopic characterization of white matter degeneration in early stages of Alzheimer's disease.

Methods

Multishell Diffusion magnetic resonance imaging data were acquired from 100 participants (40 cognitively normal, 38 with subjective cognitive decline, and 22 with mild cognitive impairment [MCI]). White matter microscopic degeneration in 27 major tracts of interest was assessed using diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging, and q-space imaging.

Results

Lower DTI fractional anisotropy and higher radial diffusivity were observed in the cingulum, thalamic radiation, and forceps major of participants with MCI. These tracts of interest also had the highest predictive power to discriminate groups. Diffusion metrics were associated with cognitive performance, particularly Rey Auditory Verbal Learning Test immediate recall, with the highest association observed in participants with MCI.

Discussion

While DTI was the most sensitive, neurite orientation dispersion and density imaging and q-space imaging complementarily characterized reduced axonal density accompanied with dispersed and less restricted white matter microstructures.

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Mild cognitive decline poses microstructural alterations in white matter tracts.

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The alterations include higher axonal dispersion and lower tissue restriction.

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Diffusion metrics are associated with cognitive outcomes in AD continuum.

Interactive effect of age and APOE-ε4 allele load on white matter myelin content in cognitively normal middle-aged subjects

The apolipoprotein E gene (APOE) ε4 allele has a strong and manifold impact on cognition and neuroimaging phenotypes in cognitively normal subjects, including alterations in the white matter (WM) microstructure. Such alterations have often been regarded as a reflection of potential thinning of the myelin sheath along axons, rather than pure axonal degeneration. Considering the main role of APOE in brain lipid transport, characterizing the impact of APOE on the myelin coating is therefore of crucial interest, especially in healthy APOE-ε4 homozygous individuals, who are exposed to a twelve-fold higher risk of developing Alzheimer's disease (AD), compared to the rest of the population. We examined T1w/T2w ratio maps in 515 cognitively healthy middle-aged participants from the ALFA study (ALzheimer and FAmilies) cohort, a single-site population-based study enriched for AD risk (68 APOE-ε4 homozygotes, 197 heterozygotes, and 250 non-carriers). Using tract-based spatial statistics, we assessed the impact of age and APOE genotype on this ratio taken as an indirect descriptor of myelin content. Healthy APOE-ε4 carriers display decreased T1w/T2w ratios in extensive regions in a dose-dependent manner. These differences were found to interact with age, suggesting faster changes in individuals with more ε4 alleles. These results obtained with T1w/T2w ratios, confirm the increased vulnerability of WM tracts in APOE-ε4 healthy carriers. Early alterations of myelin content could be the result of the impaired function of the ε4 isoform of the APOE protein in cholesterol transport. These findings help to clarify the possible interactions between the APOE-dependent non-pathological burden and age-related changes potentially at the source of the AD pathological cascade.

In vivo muscle morphology comparison in post-stroke survivors using ultrasonography and diffusion tensor imaging

Skeletal muscle architecture significantly influences the performance capacity of a muscle. A DTI-based method has been recently considered as a new reference standard to validate measurement of muscle structure in vivo. This study sought to quantify muscle architecture parameters such as fascicle length (FL), pennation angle (PA) and muscle thickness (tm) in post-stroke patients using diffusion tensor imaging (DTI) and to quantitatively compare the differences with 2D ultrasonography (US) and DTI. Muscle fascicles were reconstructed to examine the anatomy of the medial gastrocnemius, posterior soleus and tibialis anterior in seven stroke survivors using US- and DTI-based techniques, respectively. By aligning the US and DTI coordinate system, DTI reconstructed muscle fascicles at the same scanning plane of the US data can be identified. The architecture parameters estimated based on two imaging modalities were further compared. Significant differences were observed for PA and tm between two methods. Although mean FL was not significantly different, there were considerable intra-individual differences in FL and PA. On the individual level, parameters measured by US agreed poorly with those from DTI in both deep and superficial muscles. The significant differences in muscle parameters we observed suggested that the DTI-based method seems to be a better method to quantify muscle architecture parameters which can provide important information for treatment planning and to personalize a computational muscle model.

Muscle architecture in children with cerebral palsy and ankle contractures: an investigation using diffusion tensor imaging

BACKGROUND

Children with cerebral palsy frequently have ankle contractures which may be caused by changes in architecture of calf muscles. Here, we compared the architecture of medial gastrocnemius muscles in children with unilateral cerebral palsy and typically developing children using novel imaging techniques.

METHODS AND PROCEDURES

Muscle volumes, fascicle lengths, pennation angles and physiological cross-sectional areas were measured from diffusion tensor images and mDixon scans obtained from 20 ambulant children with unilateral spastic cerebral palsy who had ankle contractures (age 11 ± 3 years; mean ± standard deviation) and 20 typically developing children (11 ± 4 years).

FINDINGS

In children with cerebral palsy, the more-affected side had, on average, 13° less dorsiflexion range and the medial gastrocnemius muscle had 4.9 mm shorter fascicles, 50 cm³ smaller volume and 9.5 cm² smaller physiological cross-sectional area than the less-affected side. Compared to typically developing children, the more-affected side had 10° less dorsiflexion range and the medial gastrocnemius muscle had 4.2 mm shorter fascicles, 51 cm³ smaller volume and 10 cm² smaller physiological cross-sectional area. We did not detect differences between the less-affected and typically developing legs.

INTERPRETATION

Three-dimensional measurement of whole medial gastrocnemius muscles confirmed that the architecture of muscles on the more-affected side of children with cerebral palsy differs from the less-affected side and from muscles of typically developing children. Reductions in fascicle length, muscle volume and physiological cross-sectional area may contribute to muscle contracture.

MK-curve - Characterizing the relation between mean kurtosis and alterations in the diffusion MRI signal

Diffusion kurtosis imaging (DKI) is a diffusion MRI (dMRI) technique to quantify brain microstructural properties. While DKI measures are sensitive to tissue alterations, they are also affected by signal alterations caused by imaging artifacts such as noise, motion and Gibbs ringing. Consequently, DKI often yields output parameter values (e.g. mean kurtosis; MK) that are implausible. These include implausible values that are outside of the range dictated by physics/biology, and visually apparent implausible values that form unexpected discontinuities, being too high or too low comparing with their neighborhood. These implausible values will introduce bias into any following data analyses (e.g. between-population statistical computation). Existing studies have attempted to correct implausible DKI parameter values in multiple ways; however, these approaches are not always effective. In this study, we propose a novel method for detecting and correcting voxels with implausible values to enable improved DKI parameter estimation. In particular, we focus on MK parameter estimation. We first characterize the relation between MK and alterations in the dMRI signal including diffusion weighted images (DWIs) and the baseline (b0) images. This is done by calculating MK for a range of synthetic DWI or b0 for each voxel, and generating curves (MK-curve) representing how alterations to the input dMRI signals affect the resulting output MK. We find that voxels with implausible MK values are more likely caused by artifacts in the b0 images than artifacts in DWIs with higher b-values. Accordingly, two characteristic b0 values, which define a range of synthetic b0 values that generate implausible MK values, are identified on the MK-curve. Based on this characterization, we propose an automatic approach for detection of voxels with implausible MK values by comparing a voxel's original b0 signal to the identified two characteristic b0 values, along with a correction strategy to replace the original b0 in each detected implausible voxel with a synthetic b0 value computed from the MK-curve. We evaluate the method on a DKI phantom dataset and dMRI datasets from the Human Connectome Project (HCP), and we compare the proposed correction method with other previously proposed correction methods. Results show that our proposed method is able to identify and correct most voxels with implausible DKI parameter values as well as voxels with implausible diffusion tensor parameter values.

Super-Resolution Diffusion Tensor Imaging using SRCNN: A Feasibility Study

High-resolution diffusion imaging with submillimeter isotropic voxels requires long scan times that are usually clinically impractical. Even with those long scans, the image quality can still suffer from low signal-to-noise ratio (SNR) and severe geometric distortion due to long echo spacing in echo-planar imaging sequences. In this study, we proposed and validated the efficacy of using a state-of-the-art deep-learning method, super-resolution convolutional neural network (SRCNN), to achieve submillimeter super-resolution diffusion-weighted (DW) images. The 2D-based deep-learning method was validated by comparing with the ground truth using numerical simulations and by studying region-of-interest (ROI) using real human data of three healthy volunteers. Furthermore, we interrogated the proposed method under different real-life SNR conditions. The results demonstrated that the proposed deep-learning method was able to reproduce sufficient details in the anatomy that can only be detected using high-resolution diffusion imaging. The percentage errors in diffusion tensor imaging (DTI) derived metrics were less than 8% when the baseline SNR larger than 20. The ROI results demonstrated that the proposed method produced comparable values of diffusion metrics to the matched high-resolution diffusion metrics of real human data. Particularly, the patterns of distributions of the subjects were similar between the proposed method and real data across whole-brain gray-matter and white-matter ROIs. A deep-learned submillimeter resolution of 0.625 mm diffusion directional image showed high image quality, particularly in the cortical gray matter. We demonstrated the feasibility of using a deep-learning algorithm based on SRCNN in DTI. This approach can be a robust alternative when acquiring the true sub-millimeter diffusion MRI is not available.

Diffusion MRI: Assessment of the Impact of Acquisition and Preprocessing Methods Using the BrainVISA-Diffuse Toolbox

Diffusion MR images are prone to severe geometric distortions induced by head movement, eddy-current and inhomogeneity of magnetic susceptibility. Various correction methods have been proposed that depend on the choice of the acquisition settings and potentially provide highly different data quality. However, the impact of this choice has not been evaluated in terms of the ratio between scan time and preprocessed data quality. This study aims at investigating the impact of six well-known preprocessing methods, each associated to specific acquisition settings, on the outcome of diffusion analyses. For this purpose, we developed a comprehensive toolbox called Diffuse which automatically guides the user to the best preprocessing pipeline according to the input data. Using MR images of 20 subjects from the HCP dataset, we compared the six pre-processing pipelines regarding the following criteria: the ability to recover brain’s true geometry, the tensor model estimation and derived indices in the white matter, and finally the spatial dispersion of six well known connectivity pathways. As expected the pipeline associated to the longer acquisition fully repeated with reversed phase-encoding (RPE) yielded the higher data quality and was used as a reference to evaluate the other pipelines. In this way, we highlighted several significant aspects of other pre-processing pipelines. Our results first established that eddy-current correction improves the tensor-fitting performance with a localized impact especially in the corpus callosum. Concerning susceptibility distortions, we showed that the use of a field map is not sufficient and involves additional smoothing, yielding to an artificial decrease of tensor-fitting error. Of most importance, our findings demonstrate that, for an equivalent scan time, the acquisition of a b0 volume with RPE ensures a better brain’s geometry reconstruction and local improvement of tensor quality, without any smoothing of the image. This was found to be the best scan time/data quality compromise. To conclude, this study highlights and attempts to quantify the strong dependence of diffusion metrics on acquisition settings and preprocessing methods.

Bootstrap analysis of diffusion tensor and mean apparent propagator parameters derived from multiband diffusion MRI

PURPOSE

To directly compare diffusion metrics derived from multiband (MB) imaging sequences to those derived using a single-band acquisition.

METHODS

In this work, diffusion metrics from DTI and mean apparent propagator MRI derived from a commercial MB sequence with an acceleration factor of 3 are compared with those derived from a conventional diffusion MRI sequence using a novel bootstrapping analysis scheme on oversampled diffusion MRI data. The average parameter values for fractional anisotropy and mean diffusivity derived from DTI, as well as propagator anisotropy and return to origin probability derived from mean apparent propagator MRI, are compared.

RESULTS

Fractional anisotropy and propagator anisotropy are very similar when computed from data collected with and without MB, but show minor differences at low and high values of fractional anisotropy/propagator anisotropy. Mean diffusivity values are generally lower in the MB-derived maps, and return to origin probability is generally higher. The coefficient of variation of each parameter is shown to be slightly higher on average from the maps derived from MB versus single band when the TR is short, and slightly lower when the TR of the MB and single-band experiments is equal.

CONCLUSION

These results demonstrate that the MB sequence tested in this work provides very similar results to a conventional diffusion MRI sequence. The MB sequence is affected minimally by the slight decrease in SNR associated with the parallel reconstruction and reduced TR, and there are relaxation effects associated with the reduced TR.

Evaluation of principal component analysis image denoising on multi-exponential MRI relaxometry

PURPOSE

Multi-exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal-to-noise ratio (SNR). This work evaluates the use of principal-component-analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures.

METHODS

PCA denoising was evaluated using both simulated and experimental MRI data. Bi-exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared.

RESULTS

Denoising reduced the root-mean-square-error of parameters estimated from multi-exponential relaxometry by factors of ≈3×, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution.

CONCLUSION

Experimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi-exponential MRI relaxometry.

Principal component analysis fosr fast and model-free denoising of multi b-value diffusion-weighted MR images

Despite the utility of tumour characterisation using quantitative parameter maps from multi-b-value diffusion-weighted MRI (DWI), clinicians often prefer the use of the image with highest diffusion-weighting (b-value), for instance for defining regions of interest (ROIs). However, these images are typically degraded by noise, as they do not utilize the information from the full acquisition. We present a principal component analysis (PCA) approach for model-free denoising of DWI data. PCA-denoising was compared to synthetic MRI, where a diffusion model is fitted for each voxel and a denoised image at a given b-value is generated from the model fit. A quantitative comparison of systematic and random errors was performed on data simulated using several diffusion models (mono-exponential, bi-exponential, stretched-exponential and kurtosis). A qualitative visual comparison was also performed for in vivo images in six healthy volunteers and three pancreatic cancer patients. In simulations, the reduction in random errors from PCA-denoising was substantial (up to 55%) and similar to synthetic MRI (up to 53%). Model-based synthetic MRI denoising resulted in substantial (up to 29% of signal) systematic errors, whereas PCA-denoising was able to denoise without introducing systematic errors (less than 2%). In vivo, the signal-to-noise ratio (SNR) and sharpness of PCA-denoised images were superior to synthetic MRI, resulting in clearer tumour boundaries. In the presence of motion, PCA-denoising did not cause image blurring, unlike image averaging or synthetic MRI. Multi-b-value MRI can be denoised model-free with our PCA-denoising strategy that reduces noise to a level similar to synthetic MRI, but without introducing systematic errors associated with the synthetic MRI method.

Reduced brain microstructural asymmetry in patients with childhood leukemia treated with chemotherapy compared with healthy controls

Microstructural asymmetry of the brain can provide more direct causal explanations of functional lateralization than can macrostructural asymmetry. We performed a cross-sectional diffusion imaging study of 314 patients treated for childhood acute lymphoblastic leukemia (ALL) at a single institution and 92 healthy controls. An asymmetry index based on diffusion metrics was computed to quantify brain microstructural asymmetry. The effects of age and the asymmetry metrics of the two cohorts were examined with t-tests and linear models. We discovered two new types of microstructural asymmetry. Myelin-related asymmetry in controls was prominent in the back brain (89% right), whereas axon-related asymmetry occurred in the front brain (67% left) and back brain (88% right). These asymmetries indicate that white matter is more mature and more myelinated in the left back brain, potentially explaining the leftward lateralization of language and visual functions. The asymmetries increase throughout childhood and adolescence (P = 0.04) but were significantly less in patients treated for ALL (P<0.01), especially in younger patients. Our results indicate that atypical brain development may appear long before patients treated with chemotherapy become symptomatic.

Alterations in White Matter Microstructure and Connectivity in Young Adults with Alcohol Use Disorder

BACKGROUND

Magnetic resonance imaging (MRI) studies have shown differences in volume and structure in the brains of individuals with alcohol use disorder (AUD). Most research has focused on neuropathological effects of alcohol that appear after years of chronic alcohol misuse. However, few studies have investigated white matter (WM) microstructure and diffusion MRI-based (DWI) connectivity during early stages of AUD. Therefore, the goal of this work was to investigate WM integrity and structural connectivity in emerging adulthood AUD subjects using both conventional DWI metrics and a novel connectomics approach.

METHODS

Twenty-two AUD and 18 controls (CON) underwent anatomic and diffusion MRI. Outcome measures were scalar diffusion metrics and structural network connectomes. Tract-Based Spatial Statistics was used to investigate group differences in diffusion measures. Structural connectomes were used as input into a community structure procedure to obtain a coclassification index matrix (an indicator of community association strength) for each subject. Differences in coclassification and structural connectivity (indexed by streamline density) were assessed via the Network Based Statistics Toolbox.

RESULTS

AUD had higher fractional anisotropy (FA) values throughout the major WM tracts, but also had lower FA values in WM tracts in the cerebellum and right insula (p_TFCE  < 0.05). Mean diffusivity was generally lower in the AUD group (p_TFCE  < 0.05). AUD had lower coclassification of nodes between ventral attention and default mode networks and higher coclassification between nodes of visual, default mode, and somatomotor networks. Additionally, AUD had higher fiber density between an adjacent pair of nodes within the default mode network.

CONCLUSIONS

Our results indicate that emerging adulthood AUD subjects may have differential patterns of FA and distinct differences in structural connectomes compared with CON. These data suggest that such alterations in microstructure and structural connectivity may uniquely characterize early stages of AUD and/or a predisposition for development of AUD.

Paediatric brain tissue properties measured with magnetic resonance elastography

The aim of this study is to characterise the stiffness of white and grey matter in paediatric subjects using magnetic resonance elastography (MRE) and to determine whether these properties change throughout normal development. MRE was performed using a clinical 3T MRI scanner at three frequencies (30, 40 and 60 Hz) on 36 healthy paediatric subjects aged between 7 and 18 years (19 F) and 11 adults aged 23-44 years (6 F). Anatomical and diffusion tensor imaging was also collected. The stiffness quantified as the magnitude of the complex shear modulus (G*), fractional anisotropy (FA), mean diffusivity (MD) and volume of white and grey matter were calculated. One-way analysis of variance and Tukey's multiple comparison tests were used to compare data in age groups separated into children (7-12 years), adolescents (13-18 years) and adults (18+ years), and Spearman's correlations were performed for paediatric data. White and grey matter stiffness for each frequency and their frequency dependence was found to be very similar in paediatric and adult subjects (p > 0.05 all variables). No significant correlations were found when comparing G* with age, FA, MD or volume. Adult G*, FA, MD and volume values were within range of others reported in the literature. Paediatric white and grey matter stiffness values are similar to those of adults. We conclude that clinically, adult values can be used as a baseline measure in paediatric brain MRE.

Contemporary image-based methods for measuring passive mechanical properties of skeletal muscles in vivo

Skeletal muscles' primary function in the body is mechanical: to move and stabilize the skeleton. As such, their mechanical behavior is a key aspect of their physiology. Recent developments in medical imaging technology have enabled quantitative studies of passive muscle mechanics, ranging from measurements of intrinsic muscle mechanical properties, such as elasticity and viscosity, to three-dimensional muscle architecture and dynamic muscle deformation and kinematics. In this review we summarize the principles and applications of contemporary imaging methods that have been used to study the passive mechanical behavior of skeletal muscles. Elastography measurements can provide in vivo maps of passive muscle mechanical parameters, and both MRI and ultrasound methods are available (magnetic resonance elastography and ultrasound shear wave elastography, respectively). Both have been shown to differentiate between healthy muscle and muscles affected by a broad range of clinical conditions. Detailed muscle architecture can now be depicted using diffusion tensor imaging, which not only is particularly useful for computational modeling of muscle but also has potential in assessing architectural changes in muscle disorders. More dynamic information about muscle mechanics can be obtained using a range of dynamic MRI methods, which characterize the detailed internal muscle deformations during motion. There are several MRI techniques available (e.g., phase-contrast MRI, displacement-encoded MRI, and "tagged" MRI), each of which can be collected in synchrony with muscle motion and postprocessed to quantify muscle deformation. Together, these modern imaging techniques can characterize muscle motion, deformation, mechanical properties, and architecture, providing complementary insights into skeletal muscle function.

A kernel-based image denoising method for improving parametric image generation

One of the main challenges in the pixel-wise modeling analysis is the presence of high noise levels. Wang and Qi proposed a kernel-based method for dynamic positron emission tomgraphy reconstruction. Inspired by this method, we propose a kernel-based image denoising method based on the minimization of a kernel-based lp-norm regularized problem. To solve the kernel-based image denoising problem, we used the general-threshold filtering algorithm in combination with total difference. In the present study, we investigated whether diffusion-weighted magnetic resonance imaging (DW-MRI) data denoised using the proposed method can provide improved intravoxel incoherent motion (IVIM) parametric images. We also compared the proposed method with the method using the local principal component analysis (LPCA). The simulated DW-MR magnitude images are assumed to have Rician distributed noise. Computer simulations show that the proposed image denoising method can achieve a better bias-variance trade-off than the LPCA method. Moreover, the proposed method can reduce variance while simultaneously preserving edges in the parametric images. We tested our image denoising method on in vivo DW-MRI data, and the result showed that the denoised DWI-MRI data obtained using the proposed method can substantially improve the quality of IVIM parametric images.

Noise reduction in diffusion MRI using non-local self-similar information in joint x-q space

Diffusion MRI affords valuable insights into white matter microstructures, but suffers from low signal-to-noise ratio (SNR), especially at high diffusion weighting (i.e., b-value). To avoid time-intensive repeated acquisition, post-processing algorithms are often used to reduce noise. Among existing methods, non-local means (NLM) has been shown to be particularly effective. However, most NLM algorithms for diffusion MRI focus on patch matching in the spatial domain (i.e., x-space) and disregard the fact that the data live in a combined 6D space covering both spatial domain and diffusion wavevector domain (i.e., q-space). This drawback leads to inaccurate patch matching in curved white matter structures and hence the inability to effectively use recurrent information for noise reduction. The goal of this paper is to overcome this limitation by extending NLM to the joint x-q space. Specifically, we define for each point in the x-q space a spherical patch from which we extract rotation-invariant features for patch matching. The ability to perform patch matching across q-samples allows patches from differentially orientated structures to be used for effective noise removal. Extensive experiments on synthetic, repeated-acquisition, and HCP data demonstrate that our method outperforms state-of-the-art methods, both qualitatively and quantitatively.

CSF glial biomarkers YKL40 and sTREM2 are associated with longitudinal volume and diffusivity changes in cognitively unimpaired individuals

Cerebrospinal fluid (CSF) YKL40 and sTREM2 are astroglial and microglial activity biomarkers, respectively. We assessed whether CSF YKL40 and sTREM2 baseline levels are associated with longitudinal brain volume and diffusivity changes in cognitively unimpaired adults. Two brain MRI scans of 36 participants (57 to 78-years old, 12 male) were acquired in a 2-year interval. Aβ42, p-tau, YKL40 and sTREM2 concentrations in CSF were determined at baseline. We calculated gray and white matter volume changes per year maps (ΔGM and ΔWM, respectively) by means of longitudinal pairwise registration, and mean diffusivity variation per year (ΔMD) by subtraction. We checked voxel-wise for associations between ΔGM, ΔWM and ΔMD and baseline CSF level of YKL40 and sTREM2 and verified to what extent these associations were modulated by age (YKL40xAGE and sTREM2xAGE interactions). We found a positive association between ΔGM and YKL40 in the left inferior parietal region and no association between sTREM2 and ΔGM. Negative associations were also observed between ΔGM and YKL40xAGE (bilateral frontal areas, left precuneus and left postcentral and supramarginal gyri) and sTREM2xAGE (bilateral temporal and frontal cortex, putamen and left middle cingulate gyrus). We found negative associations between ΔWM and YKL40xAGE (bilateral superior longitudinal fasciculus) and sTREM2xAGE (bilateral superior longitudinal fasciculus, left superior corona radiata, retrolenticular external capsule and forceps minor, among other regions) but none between ΔWM and neither YKL40 nor sTREM2. ΔMD was positively correlated with YKL40 in right orbital region and negatively with sTREM2 in left lingual gyrus and precuneus. In addition, significant associations were found between ΔMD and YKL40xAGE (tail of left hippocampus and surrounding areas and right anterior cingulate gyrus) and sTREM2xAGE (right superior temporal gyrus). Areas showing statistically significant differences were disjoint in analyses involving YKL40 and sTREM2. These results suggest that glial biomarkers exert a relevant and distinct influence in longitudinal brain macro- and microstructural changes in cognitively unimpaired adults, which appears to be modulated by age. In younger subjects increased glial markers (both YKL40 and sTREM2) predict a better outcome, as indicated by a decrease in ΔGM and ΔWM and an increase in ΔMD, whereas in older subjects this association is inverted and higher levels of glial markers are associated with a poorer neuroimaging outcome.

Spin echo based cardiac diffusion imaging at 7T: An ex vivo study of the porcine heart at 7T and 3T

Purpose of this work was to assess feasibility of cardiac diffusion tensor imaging (cDTI) at 7 T in a set of healthy, unfixed, porcine hearts using various parallel imaging acceleration factors and to compare SNR and derived cDTI metrics to a reference measured at 3 T. Magnetic resonance imaging was performed on 7T and 3T whole body systems using a spin echo diffusion encoding sequence with echo planar imaging readout. Five reference (b = 0 s/mm2) images and 30 diffusion directions (b = 700 s/mm2) were acquired at both 7 T and 3 T using a GRAPPA acceleration factor R = 1. Scans at 7 T were repeated using R = 2, R = 3, and R = 4. SNR evaluation was based on 30 reference (b = 0 s/mm2) images of 30 slices of the left ventricle and cardiac DTI metrics were compared within AHA segmentation. The number of hearts scanned at 7 T and 3 T was n = 11. No statistically significant differences were found for evaluated helix angle, secondary eigenvector angle, fractional anisotropy and apparent diffusion coefficient at the different field strengths, given sufficiently high SNR and geometrically undistorted images. R≥3 was needed to reduce susceptibility induced geometric distortions to an acceptable amount. On average SNR in myocardium of the left ventricle was increased from 29±3 to 44±6 in the reference image (b = 0 s/mm2) when switching from 3 T to 7 T. Our study demonstrates that high resolution, ex vivo cDTI is feasible at 7 T using commercial hardware.

Apathy is associated with large-scale white matter network disruption in small vessel disease

OBJECTIVE

To investigate whether white matter network disruption underlies the pathogenesis of apathy, but not depression, in cerebral small vessel disease (SVD).

METHODS

Three hundred thirty-one patients with SVD from the Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Cohort (RUN DMC) study completed measures of apathy and depression and underwent structural MRI. Streamlines reflecting underlying white matter fibers were reconstructed with diffusion tensor tractography. First, path analysis was used to determine whether network measures mediated associations between apathy and radiologic markers of SVD. Next, we examined differences in whole-brain network measures between participants with only apathy, only depression, and comorbid apathy and depression and a control group free of neuropsychiatric symptoms. Finally, we examined regional network differences associated with apathy.

RESULTS

Path analysis demonstrated that network disruption mediated the relationship between apathy and SVD markers. Patients with apathy, compared to all other groups, were impaired on whole-brain measures of network density and efficiency. Regional network analyses in both the apathy subgroup and the entire sample revealed that apathy was associated with impaired connectivity in premotor and cingulate regions.

CONCLUSIONS

Our results suggest that apathy, but not depression, is associated with white matter tract disconnection in SVD. The subnetworks delineated suggest that apathy may be driven by damage to white matter networks underlying action initiation and effort-based decision making.

Structure Prior Constrained Estimation of Human Cardiac Diffusion Tensors

OBJECTIVE

The purpose of this paper is to increase the accuracy of human cardiac diffusion tensor (DT) estimation in diffusion magnetic resonance imaging (dMRI) with a few diffusion gradient directions.

METHODS

A structure prior constrained (SPC) method is proposed. The method consists in introducing two regularizers in the conventional nonlinear least squares estimator. The two regularizers penalize the dissimilarity between neighboring DTs and the difference between estimated and prior fiber orientations, respectively. A novel numerical solution is presented to ensure the positive definite estimation.

RESULTS

Experiments on ex vivo human cardiac data show that the SPC method is able to well estimate DTs at most voxels, and is superior to state-of-the-art methods in terms of the mean errors of principal eigenvector, second eigenvector, helix angle, transverse angle, fractional anisotropy, and mean diffusivity.

CONCLUSION

The SPC method is a practical and reliable alternative to current denoising- or regularization-based methods for the estimation of human cardiac DT.

SIGNIFICANCE

The SPC method is able to accurately estimate human cardiac DTs in dMRI with a few diffusion gradient directions.

Denoising high angular resolution diffusion imaging data by combining singular value decomposition and non-local means filter

BACKGROUND

High angular resolution diffusion imaging (HARDI) data is typically corrupted with Rician noise. Although larger b-values help to retrieve more accurate angular diffusivity information, they also lead to an increase in noise generation.

NEW METHOD

In order to sufficiently reduce noise in HARDI images and improve the construction of orientation distribution function (ODF) fields, a novel denoising method was developed in this study by combining the singular value decomposition (SVD) and non-local means (NLM) filter. Similar 3D patches were first recruited into a matrix from a search volume. HARDI signals in the matrix were then re-estimated using the SVD low rank approximation, and a NLM filter was employed to filter out any residual noise.

RESULTS

The performance of the proposed method was evaluated against the state-of-the-art denoising methods based on both synthetic and real HARDI datasets. Results demonstrated the superior performance of the developed SVD-NLM method in denoising HARDI data through preserving fine angular structural details and estimating diffusion orientations from improved ODF fields.

CONCLUSION

The proposed SVD-NLM method can improve HARDI quantitative computations, such as MRI brain tissue segmentation and diffusion profile estimation, that rely on the quality of imaging data.

Test-Retest Reliability of Diffusion Measures Extracted Along White Matter Language Fiber Bundles Using HARDI-Based Tractography

High angular resolution diffusion imaging (HARDI)-based tractography has been increasingly used in longitudinal studies on white matter macro- and micro-structural changes in the language network during language acquisition and in language impairments. However, test-retest reliability measurements are essential to ascertain that the longitudinal variations observed are not related to data processing. The aims of this study were to determine the reproducibility of the reconstruction of major white matter fiber bundles of the language network using anatomically constrained probabilistic tractography with constrained spherical deconvolution based on HARDI data, as well as to assess the test-retest reliability of diffusion measures extracted along them. Eighteen right-handed participants were scanned twice, one week apart. The arcuate, inferior longitudinal, inferior fronto-occipital, and uncinate fasciculi were reconstructed in the left and right hemispheres and the following diffusion measures were extracted along each tract: fractional anisotropy, mean, axial, and radial diffusivity, number of fiber orientations, mean length of streamlines, and volume. All fiber bundles showed good morphological overlap between the two scanning timepoints and the test-retest reliability of all diffusion measures in most fiber bundles was good to excellent. We thus propose a fairly simple, but robust, HARDI-based tractography pipeline reliable for the longitudinal study of white matter language fiber bundles, which increases its potential applicability to research on the neurobiological mechanisms supporting language.

Detecting white matter alterations in multiple sclerosis using advanced diffusion magnetic resonance imaging

Multiple sclerosis is a neurodegenerative and inflammatory disease, a hallmark of which is demyelinating lesions in the white matter. We hypothesized that alterations in white matter microstructures can be non-invasively characterized by advanced diffusion magnetic resonance imaging. Seven diffusion metrics were extracted from hybrid diffusion imaging acquisitions via classic diffusion tensor imaging, neurite orientation dispersion and density imaging, and q-space imaging. We investigated the sensitivity of the diffusion metrics in 36 sets of regions of interest in the brain white matter of six female patients (age 52.8 ± 4.3 years) with multiple sclerosis. Each region of interest set included a conventional T2-defined lesion, a matched perilesion area, and normal-appearing white matter. Six patients with multiple sclerosis (n = 5) or clinically isolated syndrome (n = 1) at a mild to moderate disability level were recruited. The patients exhibited microstructural alterations from normal-appearing white matter transitioning to perilesion areas and lesions, consistent with decreased tissue restriction, decreased axonal density, and increased classic diffusion tensor imaging diffusivity. The findings suggest that diffusion compartment modeling and q-space analysis appeared to be sensitive for detecting subtle microstructural alterations between perilesion areas and normal-appearing white matter.

Local PCA Shows How the Effect of Population Structure Differs Along the Genome

Population structure leads to systematic patterns in measures of mean relatedness between individuals in large genomic data sets, which are often discovered and visualized using dimension reduction techniques such as principal component analysis (PCA). Mean relatedness is an average of the relationships across locus-specific genealogical trees, which can be strongly affected on intermediate genomic scales by linked selection and other factors. We show how to use local PCA to describe this intermediate-scale heterogeneity in patterns of relatedness, and apply the method to genomic data from three species, finding in each that the effect of population structure can vary substantially across only a few megabases. In a global human data set, localized heterogeneity is likely explained by polymorphic chromosomal inversions. In a range-wide data set of Medicago truncatula, factors that produce heterogeneity are shared between chromosomes, correlate with local gene density, and may be caused by linked selection, such as background selection or local adaptation. In a data set of primarily African Drosophila melanogaster, large-scale heterogeneity across each chromosome arm is explained by known chromosomal inversions thought to be under recent selection and, after removing samples carrying inversions, remaining heterogeneity is correlated with recombination rate and gene density, again suggesting a role for linked selection. The visualization method provides a flexible new way to discover biological drivers of genetic variation, and its application to data highlights the strong effects that linked selection and chromosomal inversions can have on observed patterns of genetic variation.

Arterial stiffness and white matter integrity in the elderly: A diffusion tensor and magnetization transfer imaging study

BACKGROUND AND PURPOSE

The stiffness of large arteries and increased pulsatility can have an impact on the brain white matter (WM) microstructure, however those mechanisms are still poorly understood. The aim of this study was to investigate the association between central artery stiffness, axonal and myelin integrity in 54 cognitively unimpaired elderly subjects (65-75 years old).

METHODS

The neuronal fiber integrity of brain WM was assessed using diffusion tensor metrics and magnetization transfer imaging as measures of axonal organization (Fractional anisotropy, Radial diffusivity) and state of myelination (Myelin volume fraction). Central artery stiffness was measured by carotid-femoral pulse wave velocity (cfPWV). Statistical analyses included 4 regions (the corpus callosum, the internal capsule, the corona radiata and the superior longitudinal fasciculus) which have been previously denoted as vulnerable to increased central artery stiffness.

RESULTS

cfPWV was significantly associated with fractional anisotropy and radial diffusivity (p < 0.05, corrected for multiple comparisons) but not with myelin volume fraction. Findings from this study also show that improved executive function performance correlates with Fractional anisotropy positively (p < 0.05 corrected) as well as with myelin volume fraction and radial diffusivity negatively (p < 0.05 corrected).

CONCLUSIONS

These findings suggest that arterial stiffness is associated with axon degeneration rather than demyelination. Controlling arterial stiffness may play a role in maintaining the health of WM axons in the aging brain.

An Efficient Modelling-Simulation-Analysis Workflow to Investigate Stump-Socket Interaction Using Patient-Specific, Three-Dimensional, Continuum-Mechanical, Finite Element Residual Limb Models

The lack of an efficient modelling-simulation-analysis workflow for creating and utilising detailed subject-specific computational models is one of the key reasons why simulation-based approaches for analysing socket-stump interaction have not yet been successfully established. Herein, we propose a novel and efficient modelling-simulation-analysis workflow that uses commercial software for generating a detailed subject-specific, three-dimensional finite element model of an entire residual limb from Diffusion Tensor MRI images in <20 min. Moreover, to complete the modelling-simulation-analysis workflow, the generated subject-specific residual limb model is used within an implicit dynamic FE simulation of bipedal stance to predict the potential sites of deep tissue injury. For this purpose, a nonlinear hyperelastic, transversely isotropic skeletal muscle constitutive law containing a deep tissue injury model was implemented in LS-DYNA. To demonstrate the feasibility of the entire modelling-simulation-analysis workflow and the fact that detailed, anatomically realistic, multi-muscle models are superior to state-of-the-art, fused-muscle models, an implicit dynamic FE analysis of 2-h bipedal stance is carried out. By analysing the potential volume of damaged muscle tissue after donning an optimally-fitted and a misfitted socket, i.e., a socket whose volume was isotropically shrunk by 10%, we were able to highlight the differences between the detailed individual- and fused-muscle models. The results of the bipedal stance simulation showed that peak stresses in the fused-muscle model were four times lower when compared to the multi-muscle model. The peak interface stress in the individual-muscle model, at the end of bipedal stance analysis, was 2.63 times lower than that in the deep tissues of the stump. At the end of the bipedal stance analysis using the misfitted socket, the fused-muscle model predicted that 7.65% of the residual limb volume was injured, while the detailed-model predicted 16.03%. The proposed approach is not only limited to modelling residual limbs but also has applications in predicting the impact of plastic surgery, for detailed forward-dynamics simulations of normal musculoskeletal systems.

The Impact of Alcohol Use on Frontal White Matter in HIV

BACKGROUND

Alcohol use disorder (AUD) is prevalent among individuals diagnosed with human immunodeficiency virus (HIV), and both HIV and alcohol use have been shown to negatively affect the integrity of white matter pathways in the brain. Behavioral, functional, and anatomical impairments have been linked independently to HIV and alcohol use, and these impairments have bases in specific frontally mediated pathways within the brain.

METHODS

Magnetic resonance imaging data were acquired for 37 HIV+ participants without dementia or hepatitis C. Imaging data were processed through the FreeSurfer and TraCULA pipelines to obtain 4 bilateral frontal white matter tracts for each participant. Diffusion metrics of white matter integrity along the highest probability pathway for each tract were analyzed with respect to demographics, disease-specific variables, and reported substance use.

RESULTS

Significantly increased axial diffusivity (decreased axonal integrity) and a trending increase in mean diffusivity were observed along the anterior thalamic radiation (ATR) in participants with a history of AUD. A diagnosis of AUD explained over 36% of the variance in diffusivity along the ATR overall when accounting for clinical variables including nadir CD4 and age-adjusted HIV infection length.

CONCLUSIONS

This study provides evidence of HIV-related associations between alcohol use and indicators of axonal integrity loss along the ATR, a frontal pathway involved in the inhibition of addictive or unwanted behaviors. Reduced axonal integrity of this pathway was greatest in HIV+ participants with an AUD, even when considering the effect of age-adjusted disease length and severity (nadir CD4). This finding implicates a potential biological mechanism linking reduced integrity of frontal white matter to the high prevalence of AUD in an HIV+ population without dementia or hepatitis C.

A general-threshold filtering method for improving intravoxel incoherent motion parameter estimates

In this study, we present an image denoising method for diffusion-weighted magnetic resonance imaging (DW-MRI) data. Our aim is to improve the estimation of intravoxel incoherent motion (IVIM) parameters using denoised DW-MRI data. A general-threshold filtering (GTF) reconstruction via total variation minimization has been proposed to improve image quality in few-view computed tomography. Here, we applied the combination of GTF and total difference to image denoising. Voxel-wise IVIM analysis was performed using both real and simulated DW-MRI data. Using an institutional review board-approved protocol with written informed consent, DW-MRI imaging was performed at a 3 T hybrid PET/MR system in 10 patients with Hodgkin lymphoma lesions. A simulated phantom consisting of four organs (liver, pancreas, spleen and kidney) was used to generate noisy DW-MRI data according to the IVIM model at different noise levels. DW-MRI data were denoised before IVIM parameter estimation. The proposed image denoising method was compared with the image denoising method using joint rank and edge constraints (JREC). The results of simulated data show that at the lower signal-to-noise ratios the proposed image denoising method outperformed the JREC method in terms of the accuracy and precision of the IVIM parameter estimates. The experimental results also show that the proposed image denoising method could yield better parametric images than the JREC method in terms of noise reduction and edge preservation.

Acute White-Matter Abnormalities in Sports-Related Concussion: A Diffusion Tensor Imaging Study from the NCAA-DoD CARE Consortium

Sports-related concussion (SRC) is an important public health issue. Although standardized assessment tools are useful in the clinical management of acute concussion, the underlying pathophysiology of SRC and the time course of physiological recovery after injury remain unclear. In this study, we used diffusion tensor imaging (DTI) to detect white matter alterations in football players within 48 h after SRC. As part of the NCAA-DoD CARE Consortium study of SRC, 30 American football players diagnosed with acute concussion and 28 matched controls received clinical assessments and underwent advanced magnetic resonance imaging scans. To avoid selection bias and partial volume effects, whole-brain skeletonized white matter was examined by tract-based spatial statistics to investigate between-group differences in DTI metrics and their associations with clinical outcome measures. Mean diffusivity was significantly higher in brain white matter of concussed athletes, particularly in frontal and subfrontal long white matter tracts. In the concussed group, axial diffusivity was significantly correlated with the Brief Symptom Inventory and there was a similar trend with the symptom severity score of the Sport Concussion Assessment Tool. In addition, concussed athletes with higher fractional anisotropy performed better on the cognitive component of the Standardized Assessment of Concussion. Overall, the results of this study are consistent with the hypothesis that SRC is associated with changes in white matter tracts shortly after injury, and these differences are correlated clinically with acute symptoms and functional impairments.

Weighted Schatten p-norm minimization for 3D magnetic resonance images denoising

Magnetic resonance (MR) imaging plays an important role in clinical diagnosis and scientific research. A clean MR image can better provide patient's information to doctors or researchers for further treatment. However, in real life, MR images are inevitably corrupted by annoying Rician noise in the process of imaging. Aiming at the Rician noise of 3D MR images, a framework is proposed to suppress noise by low-rank matrix approximation (LRMA) with weighted Schatten p-norm minimization regularization (WSNMD-3D). The proposed method not only considers the importance of different rank components, but can also approximate the true rank of the latent low-rank matrix. This approach first groups similar non-local cubic patches extracted from the noisy 3D MR image into a matrix whose columns are vectorized patches. The above matrix can be modeled as a low-rank matrix approximate model. Then weighted Schatten p-norm minimization (WSNM) is applied to the model, which shrinks different rank components with different treatments. Finally, the denoised 3D MR image is acquired by aggregating all denoised patches with weighted averaging. Experimental results on synthetic and real 3D MR data show that the proposed method obtains better results than state-of-the-art methods, both visually and quantitatively.

Multi-scale locally low-rank noise reduction for high-resolution dynamic quantitative cardiac MRI

Evaluation of myocardial T₁ times is conventionally limited to a single temporal snapshot of the cardiac cycle, leaving the dependence between functional and tissue characterization unexplored. We recently proposed a technique that alleviates this limitation by acquiring dynamic quantitative myocardial T₁ maps. However, tradeoffs between temporal resolution, scan duration and SNR limit the spatial resolution. In this work, we propose a multi-scale locally low rank noise reduction approach without parameter-tuning to enable high acceleration rates in the acquisition, facilitating superior spatial and temporal resolutions in dynamic myocardial T₁ mapping.

Tract-Specific White Matter Correlates of Age-Related Reward Devaluation Deficits in Macaque Monkeys

Aim:

Cognitive aging is known to alter reward-guided behaviors that require interactions between the orbitofrontal cortex (OFC) and amygdala. In macaques, OFC, but not amygdala volumes decline with age and correlate with performance on a reward devaluation (RD) task. The present study used diffusion magnetic resonance imaging (dMRI) methods to investigate whether the condition of the white matter associated with amygdala-OFC connectivity changes with age and relates to reward devaluation.

Methods:

Diffusion-, T1- and T2-weighted MRIs were acquired from adult and aged bonnet macaques. Using probabilistic tractography, fractional anisotropy (FA) estimates from two separate white matter tracts associated with amygdala-OFC connectivity, the uncinate fasciculus (UF) and amygdalofugal (AF) pathways, were obtained. Performance measures on RD and reversal learning (RL) tasks were also acquired and related to FA indices from each anatomical tract.

Results:

Aged monkeys were impaired on both the RD and RL tasks and had lower FA indices in the AF pathway. Higher FA indices from the right hemisphere UF pathway correlated with better performance on an object-based RD task, whereas higher FA indices from the right hemisphere AF were associated with better performance on an object-free version of the task. FA measures from neither tract correlated with RL performance.

Conclusions:

These results suggest that the condition of the white matter connecting the amygdala and OFC may impact reward devaluation behaviors. Furthermore, the observation that FA indices from the UF and AF differentially relate to reward devaluation suggests that the amygdala-OFC interactions that occur via these separate tracts are partially independent.

Hybrid Diffusion Imaging in Mild Traumatic Brain Injury

Mild traumatic brain injury (mTBI) is an important public health problem. Although conventional medical imaging techniques can detect moderate-to-severe injuries, they are relatively insensitive to mTBI. In this study, we used hybrid diffusion imaging (HYDI) to detect white matter alterations in 19 patients with mTBI and 23 other trauma control patients. Within 15 days (standard deviation = 10) of brain injury, all subjects underwent magnetic resonance HYDI and were assessed with a battery of neuropsychological tests of sustained attention, memory, and executive function. Tract-based spatial statistics (TBSS) was used for voxel-wise statistical analyses within the white matter skeleton to study between-group differences in diffusion metrics, within-group correlations between diffusion metrics and clinical outcomes, and between-group interaction effects. The advanced diffusion imaging techniques, including neurite orientation dispersion and density imaging (NODDI) and q-space analyses, appeared to be more sensitive then classic diffusion tensor imaging. Only NODDI-derived intra-axonal volume fraction (V_ic) demonstrated significant group differences (i.e., 5–9% lower in the injured brain). Within the mTBI group, V_ic and a q-space measure, P₀, correlated with 6 of 10 neuropsychological tests, including measures of attention, memory, and executive function. In addition, the direction of correlations differed significantly between groups (R² > 0.71 and p_interation < 0.03). Specifically, in the control group, higher V_ic and P₀ were associated with better performances on clinical assessments, whereas in the mTBI group, higher V_ic and P₀ were associated with worse performances with correlation coefficients >0.83. In summary, the NODDI-derived axonal density index and q-space measure for tissue restriction demonstrated superior sensitivity to white matter changes shortly after mTBI. These techniques hold promise as a neuroimaging biomarker for mTBI.

Human Cerebellar Sub-millimeter Diffusion Imaging Reveals the Motor and Non-motor Topography of the Dentate Nucleus

The reciprocal cortico-cerebellar loops that underlie cerebellar contributions to motor and cognitive behavior form one of the largest systems in the primate brain. Work with non-human primates has shown that the dentate nucleus, the major output nucleus of the cerebellum, contains topographically distinct connections to both motor and non-motor regions, yet there is no evidence for how the cerebellar cortex connects to the dentate nuclei in humans. Here we used in-vivo sub-millimeter diffusion imaging to characterize this fundamental component of the cortico-cerebellar loop, and identified a pattern of superior motor and infero-lateral non-motor connectivity strikingly similar to that proposed by animal work. Crucially, we also present first evidence that the dominance for motor connectivity observed in non-human primates may be significantly reduced in man - a finding that is in accordance with the proposed increase in cerebellar contributions to higher cognitive behavior over the course of primate evolution.

White matter microstructure is altered in cognitively normal middle-aged APOE-ε4 homozygotes

BACKGROUND

The ε4 allele of the apolipoprotein E gene (APOE-ε4) is the strongest genetic factor for late-onset Alzheimer's disease. During middle age, cognitively healthy APOE-ε4 carriers already show several brain alterations that resemble those of Alzheimer's disease (AD), but to a subtler degree. These include microstructural white matter (WM) changes that have been proposed as one of the earliest structural events in the AD cascade. However, previous studies have focused mainly on comparison of APOE-ε4 carriers vs noncarriers. Therefore, the extent and magnitude of the brain alterations in healthy ε4 homozygotes, who are the individuals at highest risk, remain to be characterized in detail.

METHODS

We examined mean, axial, and radial water diffusivity (MD, AxD, and RD, respectively) and fractional anisotropy in the WM as measured by diffusion-weighted imaging in 532 cognitively healthy middle-aged participants from the ALFA study (ALzheimer and FAmilies) cohort, a single-site population-based study enriched for AD risk (68 APOE-ε4 homozygotes, 207 heterozygotes, and 257 noncarriers). We examined the impact of age and APOE genotype on these parameters using tract-based spatial statistics.

RESULTS

Healthy APOE-ε4 homozygotes display increased WM diffusivity in regions known to be affected by AD. The effects in AxD were much smaller than in RD, suggesting a disruption of the myelin sheath rather than pure axonal damage.

CONCLUSIONS

These findings could be interpreted as the result of the reduced capacity of the ε4 isoform of the APOE protein to keep cholesterol homeostasis in the brain. Because cerebral lipid metabolism is strongly related to the pathogenesis of AD, our results shed light on the possible mechanisms through which the APOE-ε4 genotype is associated with an increased risk of AD.

Progression of White Matter Hyperintensities Preceded by Heterogeneous Decline of Microstructural Integrity

BACKGROUND AND PURPOSE

White matter hyperintensities (WMH) are frequently seen on neuroimaging of elderly and are associated with cognitive decline and the development of dementia. Yet, the temporal dynamics of conversion of normal-appearing white matter (NAWM) into WMH remains unknown. We examined whether and when progression of WMH was preceded by changes in fluid-attenuated inversion recovery and diffusion tensor imaging values, thereby taking into account differences between participants with mild versus severe baseline WMH.

METHODS

From 266 participants of the RUN DMC study (Radboud University Nijmegen Diffusion Tensor and Magnetic Resonance Imaging Cohort), we semiautomatically segmented WMH at 3 time points for 9 years. Images were registered to standard space through a subject template. We analyzed differences in baseline fluid-attenuated inversion recovery, fractional anisotropy, and mean diffusivity (MD) values and changes in MD values over time between 4 regions: (1) remaining NAWM, (2) NAWM converting into WMH in the second follow-up period, (3) NAWM converting into WMH in the first follow-up period, and (4) WMH.

RESULTS

NAWM converting into WMH in the first or second time interval showed higher fluid-attenuated inversion recovery and MD values than remaining NAWM. MD values in NAWM converting into WMH in the first time interval were similar to MD values in WMH. When stratified by baseline WMH severity, participants with severe WMH had higher fluid-attenuated inversion recovery and MD and lower fractional anisotropy values than participants with mild WMH, in all areas including the NAWM. MD values in WMH and in NAWM that converted into WMH continuously increased over time.

CONCLUSIONS

Impaired microstructural integrity preceded conversion into WMH and continuously declined over time, suggesting a continuous disease process of white matter integrity loss that can be detected using diffusion tensor imaging even years before WMH become visible on conventional neuroimaging. Differences in microstructural integrity between participants with mild versus severe WMH suggest heterogeneity of both NAWM and WMH, which might explain the clinical variability observed in patients with similar small vessel disease severity.

Differences in White Matter Microstructure and Connectivity in Nontreatment-Seeking Individuals with Alcohol Use Disorder

BACKGROUND

Diffusion-weighted imaging (DWI) has been widely used to investigate the integrity of white matter (WM; indexed by fractional anisotropy [FA]) in alcohol dependence and cigarette smoking. These disorders are highly comorbid, yet cigarette use has often not been adequately controlled in neuroimaging studies of alcohol-dependent populations. In addition, information on WM deficits in currently drinking, nontreatment-seeking (NTS) individuals with alcohol dependence is limited. Therefore, the aim of this work was to investigate WM microstructural integrity in alcohol use disorder by comparing matched samples of cigarette smoking NTS and social drinkers (SD).

METHODS

Thirty-eight smoking NTS and 19 smoking SD subjects underwent DWI as well as structural magnetic resonance imaging. After an in-house preprocessing of the DWI data, FA images were analyzed with tract-based spatial statistics (TBSS). FA obtained from the TBSS skeleton was tested for correlation with recent alcohol consumption.

RESULTS

Smoking NTS had lower FA relative to smoking SD, predominantly in the left hemisphere (p < 0.05, family-wise error rate corrected across FA skeleton). Across the full sample, FA and number of drinks per week were negatively related (ρ = -0.348, p = 0.008). Qualitative analyses of the structural connections through compromised WM as identified by TBSS showed differential connectivity of gray matter in NTS compared to SD subjects of left frontal, temporal, and parietal regions.

CONCLUSIONS

NTS subjects had lower WM FA than SD, indicating compromised WM integrity in the NTS population. The inverse relationship of entire WM skeleton FA with self-reported alcohol consumption supports previous evidence of a continuum of detrimental effects of alcohol consumption on WM. These results provide additional evidence that alcohol dependence is associated with reduced WM integrity in currently drinking NTS alcohol-dependent individuals, after controlling for the key variable of cigarette smoking.

A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI

Over the past several years, significant efforts have been made to improve the spatial resolution of diffusion-weighted imaging (DWI), aiming at better detecting subtle lesions and more reliably resolving white-matter fiber tracts. A major concern with high-resolution DWI is the limited signal-to-noise ratio (SNR), which may significantly offset the advantages of high spatial resolution. Although the SNR of DWI data can be improved by denoising in post-processing, existing denoising procedures may potentially reduce the anatomic resolvability of high-resolution imaging data. Additionally, non-Gaussian noise induced signal bias in low-SNR DWI data may not always be corrected with existing denoising approaches. Here we report an improved denoising procedure, termed diffusion-matched principal component analysis (DM-PCA), which comprises 1) identifying a group of (not necessarily neighboring) voxels that demonstrate very similar magnitude signal variation patterns along the diffusion dimension, 2) correcting low-frequency phase variations in complex-valued DWI data, 3) performing PCA along the diffusion dimension for real- and imaginary-components (in two separate channels) of phase-corrected DWI voxels with matched diffusion properties, 4) suppressing the noisy PCA components in real- and imaginary-components, separately, of phase-corrected DWI data, and 5) combining real- and imaginary-components of denoised DWI data. Our data show that the new two-channel (i.e., for real- and imaginary-components) DM-PCA denoising procedure performs reliably without noticeably compromising anatomic resolvability. Non-Gaussian noise induced signal bias could also be reduced with the new denoising method. The DM-PCA based denoising procedure should prove highly valuable for high-resolution DWI studies in research and clinical uses.

A joint space-angle regularization approach for single 4D diffusion image super-resolution

PURPOSE

Low signal-to-noise-ratio and limited scan time of diffusion magnetic resonance imaging (dMRI) in current clinical settings impede obtaining images with high spatial and angular resolution (HSAR) for a reliable fiber reconstruction with fine anatomical details. To overcome this problem, we propose a joint space-angle regularization approach to reconstruct HSAR diffusion signals from a single 4D low resolution (LR) dMRI, which is down-sampled in both 3D-space and q-space.

METHODS

Different from the existing works which combine multiple 4D LR diffusion images acquired using specific acquisition protocols, the proposed method reconstructs HSAR dMRI from only a single 4D dMRI by exploring and integrating two key priors, that is, the nonlocal self-similarity in the spatial domain as a prior to increase spatial resolution and ridgelet approximations in the diffusion domain as another prior to increase the angular resolution of dMRI. To more effectively capture nonlocal self-similarity in the spatial domain, a novel 3D block-based nonlocal means filter is imposed as the 3D image space regularization term which is accurate in measuring the similarity and fast for 3D reconstruction. To reduce computational complexity, we use the L₂ -norm instead of sparsity constraint on the representation coefficients.

RESULTS

Experimental results demonstrate that the proposed method can obtain the HSAR dMRI efficiently with approximately 2% per-voxel root-mean-square error between the actual and reconstructed HSAR dMRI.

CONCLUSION

The proposed approach can effectively increase the spatial and angular resolution of the dMRI which is independent of the acquisition protocol, thus overcomes the inherent resolution limitation of imaging systems.

Three-dimensional architecture of the whole human soleus muscle in vivo

Background

Most data on the architecture of the human soleus muscle have been obtained from cadaveric dissection or two-dimensional ultrasound imaging. We present the first comprehensive, quantitative study on the three-dimensional anatomy of the human soleus muscle in vivo using diffusion tensor imaging (DTI) techniques.

Methods

We report three-dimensional fascicle lengths, pennation angles, fascicle curvatures, physiological cross-sectional areas and volumes in four compartments of the soleus at ankle joint angles of 69 ± 12° (plantarflexion, short muscle length; average ± SD across subjects) and 108 ± 7° (dorsiflexion, long muscle length) of six healthy young adults. Microdissection and three-dimensional digitisation on two cadaveric muscles corroborated the compartmentalised structure of the soleus, and confirmed the validity of DTI-based muscle fascicle reconstructions.

Results

The posterior compartments of the soleus comprised 80 ± 5% of the total muscle volume (356 ± 58 cm³). At the short muscle length, the average fascicle length, pennation angle and curvature was 37 ± 8 mm, 31 ± 3° and 17 ± 4 /m, respectively. We did not find differences in fascicle lengths between compartments. However, pennation angles were on average 12° larger (p < 0.01) in the posterior compartments than in the anterior compartments. For every centimetre that the muscle-tendon unit lengthened, fascicle lengths increased by 3.7 ± 0.8 mm, pennation angles decreased by −3.2 ± 0.9° and curvatures decreased by −2.7 ± 0.8 /m. Fascicles in the posterior compartments rotated almost twice as much as in the anterior compartments during passive lengthening.

Discussion

The homogeneity in fascicle lengths and inhomogeneity in pennation angles of the soleus may indicate a functionally different role for the anterior and posterior compartments. The data and techniques presented here demonstrate how DTI can be used to obtain detailed, quantitative measurements of the anatomy of complex skeletal muscles in living humans.