High Angular Resolution Diffusion Imaging Research Articles

High angular resolution diffusion imaging (HARDI) of porcine menisci: a comparison of diffusion tensor imaging and generalized q-sampling imaging.

Diffusion magnetic resonance imaging (MRI) allows for the quantification of water diffusion properties in soft tissues. The goal of this study was to characterize the 3D collagen fiber network in the porcine meniscus using high angular resolution diffusion imaging (HARDI) acquisition with both diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI). Porcine menisci (n=7) were scanned ex vivo using a three-dimensional (3D) HARDI spin-echo pulse sequence with an isotropic resolution of 500 µm at 7.0 Tesla. Both DTI and GQI reconstruction techniques were used to quantify the collagen fiber alignment and visualize the complex collagen network of the meniscus. The MRI findings were validated with conventional histology. DTI and GQI exhibited distinct fiber orientation maps in the meniscus using the same HARDI acquisition. We found that crossing fibers were only resolved with GQI, demonstrating the advantage of GQI over DTI to visualize the complex collagen fiber orientation in the meniscus. Furthermore, the MRI findings were consistent with conventional histology. HARDI acquisition with GQI reconstruction more accurately resolves the complex 3D collagen architecture of the meniscus compared to DTI reconstruction. In the future, these technologies have the potential to nondestructively assess both normal and abnormal meniscal structure.

Dynamic monitoring of radiation-induced white matter microstructure injury in nasopharyngeal carcinoma via high-angular resolution diffusion imaging

PurposeTo investigate white matter microstructural abnormalities caused by radiotherapy in nasopharyngeal carcinoma (NPC) patients using MRI high-angular resolution diffusion imaging (HARDI). MethodsWe included 127 patients with pathologically confirmed NPC: 36 in the pre-radiotherapy group, 29 in the acute response period (post-RT-AP), 23 in the early delayed period (post-RT-ED) group, and 39 in the late-delayed period (post-RT-LD) group. HARDI data were acquired for each patient, and dispersion parameters were calculated to compare the differences in specific fibre bundles among the groups. The Montreal Neurocognitive Assessment (MoCA) was used to evaluate neurocognitive function, and the correlations between dispersion parameters and MoCA were analysed. ResultsIn the right cingulum frontal parietal bundles, the fractional anisotropy value decreased to the lowest level post-RT-AP and then reversed and increased post-RT-ED and post-RT-LD. The mean, axial, and radial diffusivity were significantly increased in the post-RT-AP (p < 0.05) and decreased in the post-RT-ED and post-RT-LD groups to varying degrees. MoCA scores were decreased post-radiotherapy than those before radiotherapy (p = 0.005). MoCA and mean diffusivity exhibited a mild correlation in the left cingulum frontal parahippocampal bundle. ConclusionsWhite matter tract changes detected by HARDI are potential biomarkers for monitoring radiotherapy-related brain damage in NPC patients.

Diffusion tractography of kidney by high angular resolution diffusion imaging

Diffusion magnetic resonance imaging (MRI) has been utilized to probe the renal microstructures but investigating the three-dimensional (3D) tubular network still presents significant challenges due to the complicated architecture of kidney. This study aims to assess whether high angular resolution diffusion imaging (HARDI) could improve the reconstruction of 3D tubular architectures. Kidneys from both mice and rats were imaged using 3D diffusion-weighted pulse sequences at 9.4 T. Five healthy mouse kidneys were scanned at an isotropic spatial resolution of 40 μm, with a b value of 1500 s/mm2 across 46 diffusion encoding directions. The study employed diffusion tensor imaging (DTI) and generalized Q-sampling imaging (GQI) to examine the tubular orientation distributions and tractography, validated by conventional histology. Fractional anisotropy (FA) and mean diffusivity (MD) were quantified and compared among the inner medullar (IM), outer medullar (OM), and cortex (CO) at different angular resolutions. FA values, estimated with 6 diffusion-weighted images (DWIs), were significantly overestimated by 49.9% (p < 0.001) in IM, 179.4% (p < 0.001) in OM, and 225.5% (p < 0.001) in CO, compared to using 46 DWIs. In contrast, MD exhibited less variations to angular resolution variations (3.4% in IM, 4.2% in OM, and 4.6% in CO). Both DTI and GQI at high angular resolution successfully traced renal tubular structures throughout the kidney, with GQI demonstrating superior performance in generating more continuous tracts. Furthermore, disrupted renal tubule structures were observed in a chronic kidney disease (CKD) rat model. HARDI, especially when combined with the GQI approach, holds promise in tracking complicated 3D tubule architectures and may serve as a potent tool for kidney disease research.

High angular diffusion tensor imaging estimation from minimal evenly distributed diffusion gradient directions.

Diffusion-weighted Imaging (DWI) is a non-invasive imaging technique based on Magnetic Resonance Imaging (MRI) principles to measure water diffusivity and reveal details of the underlying brain micro-structure. By fitting a tensor model to quantify the directionality of water diffusion a Diffusion Tensor Image (DTI) can be derived and scalar measures, such as fractional anisotropy (FA), can then be estimated from the DTI to summarise quantitative microstructural information for clinical studies. In particular, FA has been shown to be a useful research metric to identify tissue abnormalities in neurological disease (e.g. decreased anisotropy as a proxy for tissue damage). However, time constraints in clinical practice lead to low angular resolution diffusion imaging (LARDI) acquisitions that can cause inaccurate FA value estimates when compared to those generated from high angular resolution diffusion imaging (HARDI) acquisitions. In this work, we propose High Angular DTI Estimation Network (HADTI-Net) to estimate an enhanced DTI model from LARDI with a set of minimal and evenly distributed diffusion gradient directions. Extensive experiments have been conducted to show the reliability and generalisation of HADTI-Net to generate high angular DTI estimation from any minimal evenly distributed diffusion gradient directions and to explore the feasibility of applying a data-driven method for this task. The code repository of this work and other related works can be found at https://mri-synthesis.github.io/.

An interactive triangle-based ODF glyph rendering for high angular resolution diffusion imaging

Diffusion magnetic resonance imaging quantifies the diffusion of water molecules. It is unique in showing white matter fiber structures and connectivity in-vivo, relying on the fact that the water molecules’ displacements are more significant along with the fiber orientation than in other directions due to biological barriers. High angular resolution diffusion imaging (HARDI) acquisition enables the differentiation of multiple fiber signals passing through a voxel. Advanced methods for processing diffusion data were developed to synthesize the HARDI data of each voxel into an orientation distribution function (ODF) that relates each direction sample to its probability of water diffusion. Visualizing ODFs through glyphs helps gain insight into the local structure of the brain white matter. Because of the high dimensionality of the data, interactive glyph exploration remains a challenge for a volume containing millions of voxels. This work presents a 3D triangle-based multi-resolution rendering scheme for the ODF glyph. We explored data properties and GPU features to make the triangle-based rendering interactive. Linear performance relative to some fixed-resolution rendered glyphs was assessed using NVIDIA® Nsight Visual StudioTM. A control volume, available in the Retest dataset of the WU-Minn Human Connectome Project, was used to assess the effectiveness of interactive 3D visualization over a multiplanar and curvilinear reformatted volume of ODF glyphs in voxels with multiple fibers, namely the triple crossing at the centrum semiovale, the fanning at the corona radiata, and the crossing of the cingulum and corpus callosum fiber bundles.

High angular resolution susceptibility imaging and estimation of fiber orientation distribution functions in primate brain

Uncovering brain-tissue microstructure including axonal characteristics is a major neuroimaging research focus. Within this scope, anisotropic properties of magnetic susceptibility in white matter have been successfully employed to estimate primary axonal trajectories using mono-tensorial models. However, anisotropic susceptibility has not yet been considered for modeling more complex fiber structures within a voxel, such as intersecting bundles, or an estimation of orientation distribution functions (ODFs). This information is routinely obtained by high angular resolution diffusion imaging (HARDI) techniques. In applications to fixed tissue, however, diffusion-weighted imaging suffers from an inherently low signal-to-noise ratio and limited spatial resolution, leading to high demands on the performance of the gradient system in order to mitigate these limitations. In the current work, high angular resolution susceptibility imaging (HARSI) is proposed as a novel, phase-based methodology to estimate ODFs. A multiple gradient-echo dataset was acquired in an entire fixed chimpanzee brain at 61 orientations by reorienting the specimen in the magnetic field. The constant solid angle method was adapted for estimating phase-based ODFs. HARDI data were also acquired for comparison. HARSI yielded information on whole-brain fiber architecture, including identification of peaks of multiple bundles that resembled features of the HARDI results. Distinct differences between both methods suggest that susceptibility properties may offer complementary microstructural information. These proof-of-concept results indicate a potential to study the axonal organization in post-mortem primate and human brain at high resolution.

A comparison of altered white matter microstructure in youth born with congenital heart disease or born preterm.

Alterations to white matter microstructure as detected by diffusion tensor imaging have been documented in both individuals born with congenital heart disease (CHD) and individuals born preterm. However, it remains unclear if these disturbances are the consequence of similar underlying microstructural disruptions. This study used multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) and neurite orientation dispersion and density imaging (NODDI) to characterize and compare alterations to three specific microstructural elements of white matter - myelination, axon density, and axon orientation - in youth born with CHD or born preterm. Participants aged 16 to 26 years with operated CHD or born ≤33 weeks gestational age and a group of healthy peers of the same age underwent a brain MRI including mcDESPOT and high angular resolution diffusion imaging acquisitions. Using tractometry, average values of myelin water fraction (MWF), neurite density index (NDI), and orientation dispersion index (ODI) were first calculated and compared between groups for 30 white matter bundles. Afterwards, bundle profiling was performed to further characterize the topology of the detected microstructural alterations. The CHD and preterm groups both presented with widespread bundles and bundle segments with lower MWF, accompanied by some occurrences of lower NDI, relative to controls. While there were no differences in ODI between the CHD and control groups, the preterm group presented with both higher and lower ODI compared to the control group and lower ODI compared to the CHD group. While youth born with CHD or born preterm both presented with apparent deficits in white matter myelination and axon density, youth born preterm presented with a unique profile of altered axonal organization. Future longitudinal studies should aim to better understand the emergence of these common and distinct microstructural alterations, which could orient the development of novel therapeutic approaches.

Neural network algorithms predict new diffusion MRI data for multi-compartmental analysis of brain microstructure in a clinical setting

High angular resolution diffusion imaging (HARDI) is a promising method for advanced analysis of brain microstructure. However, comprehensive HARDI analysis requires multiple acquisitions of diffusion images (multi-shell HARDI), which is time consuming and often impractical in clinical settings. This study aimed to establish neural network models that can predict new diffusion datasets from clinically feasible brain diffusion MRI for multi-shell HARDI. The development included 2 algorithms: multi-layer perceptron (MLP) and convolutional neural network (CNN). Both followed a voxel-based approach for model training (70%), validation (15%), and testing (15%). The investigations involved 2 multi-shell HARDI datasets: 1) 11 healthy subjects from the Human Connectome Project (HCP); and 2) 10 local subjects with multiple sclerosis (MS). To assess outcomes, we conducted neurite orientation dispersion and density imaging using both predicted and original data and compared their orientation dispersion index (ODI) and neurite density index (NDI) in different brain tissues with 2 measures: peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). Results showed that both models achieved robust predictions, which provided competitive ODI and NDI, especially in brain white matter. The CNN outperformed MLP with the HCP data on both PSNR (p < 0.001) and SSIM (p < 0.01). With the MS data, the models performed similarly. Overall, the optimized neural networks can help generate non-acquired brain diffusion MRI, which will make advanced HARDI analysis possible in clinical practice following further validation. Enabling detailed characterization of brain microstructure will allow enhanced understanding of brain function in both health and disease.

HYDI-DSI revisited: Constrained non-parametric EAP imaging without q-space re-gridding.

Hybrid Diffusion Imaging (HYDI) was one of the first attempts to use multi-shell samplings of the q-space to infer diffusion properties beyond Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI). HYDI was intended as a flexible protocol embedding both DTI (for lower b-values) and HARDI (for higher b-values) processing, as well as Diffusion Spectrum Imaging (DSI) when the entire data set was exploited. In the latter case, the spherical sampling of the q-space is re-gridded by interpolation to a Cartesian lattice whose extent covers the range of acquired b-values, hence being acquisition-dependent. The Discrete Fourier Transform (DFT) is afterwards used to compute the corresponding Cartesian sampling of the Ensemble Average Propagator (EAP) in an entirely non-parametric way. From this lattice, diffusion markers such as the Return To Origin Probability (RTOP) or the Mean Squared Displacement (MSD) can be numerically estimated. We aim at re-formulating this scheme by means of a Fourier Transform encoding matrix that eliminates the need for q-space re-gridding at the same time it preserves the non-parametric nature of HYDI-DSI. The encoding matrix is adaptively designed at each voxel according to the underlying DTI approximation, so that an optimal sampling of the EAP can be pursued without being conditioned by the particular acquisition protocol. The estimation of the EAP is afterwards carried out as a regularized Quadratic Programming (QP) problem, which allows to impose positivity constraints that cannot be trivially embedded within the conventional HYDI-DSI. We demonstrate that the definition of the encoding matrix in the adaptive space allows to analytically (as opposed to numerically) compute several popular descriptors of diffusion with the unique source of error being the cropping of high frequency harmonics in the Fourier analysis of the attenuation signal. They include not only RTOP and MSD, but also Return to Axis/Plane Probabilities (RTAP/RTPP), which are defined in terms of specific spatial directions and are not available with the former HYDI-DSI. We report extensive experiments that suggest the benefits of our proposal in terms of accuracy, robustness and computational efficiency, especially when only standard, non-dedicated q-space samplings are available.

Lack of effects of four-week theta burst stimulation on white matter macro/microstructure in children and adolescents with autism

Following the published behavioral and cognitive results of this single-blind parallel sham-controlled randomized clinical trial, the current study aimed to explore the impact of intermittent theta burst stimulation (iTBS), a variant of excitatory transcranial magnetic stimulation, over the bilateral posterior superior temporal sulci (pSTS) on white matter macro/microstructure in intellectually able children and adolescents with autism. Participants were randomized and blindly received active or sham iTBS for 4 weeks (the single-blind sham-controlled phase). Then, all participants continued to receive active iTBS for another 4 weeks (the open-label phase). The clinical results were published elsewhere. Here, we present diffusion magnetic resonance imaging data on potential changes in white matter measures after iTBS. Twenty-two participants in Active-Active group and 27 participants in Sham-Active group underwent multi-shell high angular resolution diffusion imaging (64-direction for b = 2000 & 1000 s/mm2, respectively) at baseline, week 4, and week 8. With longitudinal fixel-based analysis, we found no white matter changes following iTBS from baseline to week 4 (a null treatment by time interaction and a null within-group paired comparison in the Active-Active group), nor from baseline to week 8 (null within-group paired comparisons in both Active-Active and Sham-Active groups). As for the brain-symptoms relationship, we did not find baseline white matter metrics associated with symptom changes at week 4 in either group. Our results raise the question of what the minimal cumulative stimulation dose required to induce the white matter plasticity is.

Mapping white matter structural and network alterations in betel quid-dependent chewers using high angular resolution diffusion imaging.

To evaluate brain white matter diffusion characteristics and anatomical network alterations in betel quid dependence (BQD) chewers using high angular resolution diffusion imaging (HARDI). The current study recruited 53 BQD chewers and 37 healthy controls (HC) in two groups. We explored regional diffusion metrics alternations in the BQD group compared with the HC group using automated fiber quantification (AFQ). We further employed the white matter (WM) anatomical network of HARDI to explore connectivity alterations in BQD chewers using graph theory. BQD chewers presented significantly lower FA values in the left and right cingulum cingulate, the left and right thalamic radiation, and the right uncinate. The BQD has a significantly higher RD value in the right uncinate fasciculus than the HC group. At the global WM anatomical network level, global network efficiency (p = 0.008) was poorer and Lp (p = 0.016) was greater in the BQD group. At the nodal WM anatomical network level, nodal efficiency (p < 0.05) was lower in the BQD group. Our findings provide novel morphometric evidence that brain structural changes in BQD are characterized by white matter diffusivity and anatomical network connectivity among regions of the brain, potentially leading to the enhanced reward system and impaired inhibitory control.

Methods to Improve Fiber Reconstruction at DTI-Based Tractography in the Area of Brain Tumor: Case Illustration and Literature Review

Background and Aim: Diffusion Tensor Imaging (DTI)-based tractography can help us visualize the spatial relation of fiber tracts to brain lesions. Several factors may interfere with the procedure of diffusion-based tractography, especially in brain tumors. The current study aims to discuss several solutions to improve the procedure of fiber reconstruction adjacent to or inside brain lesions. Illustrative cases are also presented. Methods and Materials/Patients: The paper is a narrative review of methods that can improve DTI-based fiber reconstruction in the area of brain tumors. To provide up-to-date information, we briefly reviewed related articles extracted from Google Scholar, Medline, and PubMed. Results: We proposed five techniques to improve fiber reconstruction. Technique 1 is a very low Fractional Anisotropy (FA) application. Technique 2 includes resampling techniques, such as q-ball and High Angular Resolution Diffusion Imaging (HARDI). Technique 3 is the reconstruction of fiber tracts by defining the separated Region of Interest (ROIs). Technique 4 explains the selection of the ROIs according to functional Magnetic Resonance Imaging (fMRI) since the anatomical configuration is distorted by neoplasm. Technique 5 consists of using unprocessed images for preoperative planning and correlation with the clinical situation. Conclusion: DTI tractography is highly sensitive to noise and artifacts. The application of tractography techniques can improve fiber imaging in the area of brain tumors and edema.

CSR-PERT: Joint framework for MRI and HARDI data reconstruction using perturbed radial trajectory estimated from compressively sensed measurements

Radial sampling pattern is an important signal acquisition strategy in magnetic resonance imaging (MRI) owing to better immunity to motion-induced artifacts and less pronounced aliasing due to undersampling compared to the Cartesian sampling. These advantages of radial sampling can be exploited in acquisition of multidimensional signals such as High Angular Resolution Diffusion Imaging (HARDI), with tremendous scope of acceleration. Despite such benefits, gradient delays lead to samples being acquired from unknown miscentered radial trajectories, severely degrading the image reconstruction quality. In the present work, we propose Csr-Pert that is a joint framework, wherein these perturbed radial trajectories are estimated and utilized for image reconstruction from the compressively sensed measurements of (i) MRI data and (ii) HARDI data. The proposed Csr-Pert method is tested on one real MRI dataset with trajectory deviations and is observed to perform better than the existing state-of-the-art method at high acceleration factors up to 8. To the best of our knowledge, this is the first work to address the problem of estimating perturbed trajectories using the compressively sensed MRI and HARDI data. The method is also tested for varying combinations of trajectory deviations and sampling proportions. It is observed to yield very good quality HARDI reconstruction for a wide variety of scenarios. We have also demonstrated the robustness of the proposed method on real datasets in clinical settings assuming perturbed as well as noisy trajectories.

Validation of Muscle Fiber Architecture of the Human Tongue Revealed by Diffusion Magnetic Resonance Imaging With Histology Verification.

The goal of this study is to validate the muscle architecture derived from both ex vivo and in vivo diffusion-weighted magnetic resonance imaging (dMRI) of the human tongue with histology of an ex vivo tongue. dMRI was acquired with a 200-direction high angular resolution diffusion imaging (HARDI) diffusion scheme for both a postmortem head (imaged within 48 hr after death) and a healthy volunteer. After MRI, the postmortem head was fixed and the tongue excised for hematoxylin and eosin (H&amp;E) staining and histology imaging. Structure tensor images were generated from the stained images to better demonstrate muscle fiber orientations. The tongue muscle fiber orientations, estimated from dMRI, were visualized using the tractogram, a novel representation of crossing fiber orientations, and compared against the histology images of the ex vivo tongue. Muscle fibers identified in the tractograms showed good correspondence with those appearing in the histology images. We further demonstrated tongue muscle architecture in in vivo tractograms for the entire tongue. The study demonstrates that dMRI can accurately reveal the complex muscle architecture of the human tongue and may potentially benefit planning and evaluation of oral surgery and research on speech and swallowing.

Cortical and thalamic connections of the human globus pallidus: Implications for disorders of consciousness.

A dominant framework for understanding loss and recovery of consciousness in the context of severe brain injury, the mesocircuit hypothesis, focuses on the role of cortico-subcortical recurrent interactions, with a strong emphasis on excitatory thalamofugal projections. According to this view, excess inhibition from the internal globus pallidus (GPi) on central thalamic nuclei is key to understanding prolonged disorders of consciousness (DOC) and their characteristic, brain-wide metabolic depression. Recent work in healthy volunteers and patients, however, suggests a previously unappreciated role for the external globus pallidus (GPe) in maintaining a state of consciousness. This view is consistent with empirical findings demonstrating the existence of “direct” (i.e., not mediated by GPi/substantia nigra pars reticulata) GPe connections with cortex and thalamus in animal models, as well as their involvement in modulating arousal and sleep, and with theoretical work underscoring the role of GABA dysfunction in prolonged DOC. Leveraging 50 healthy subjects' high angular resolution diffusion imaging (HARDI) dataset from the Human Connectome Project, which provides a more accurate representation of intravoxel water diffusion than conventional diffusion tensor imaging approaches, we ran probabilistic tractography using extensive a priori exclusion criteria to limit the influence of indirect connections in order to better characterize “direct” pallidal connections. We report the first in vivo evidence of highly probable “direct” GPe connections with prefrontal cortex (PFC) and central thalamic nuclei. Conversely, we find direct connections between the GPi and PFC to be sparse (i.e., less likely indicative of true “direct” connectivity) and restricted to the posterior border of PFC, thus reflecting an extension from the cortical motor zones (i.e., motor association areas). Consistent with GPi's preferential connections with sensorimotor cortices, the GPi appears to predominantly connect with the sensorimotor subregions of the thalamus. These findings are validated against existing animal tracer studies. These findings suggest that contemporary mechanistic models of loss and recovery of consciousness following brain injury must be updated to include the GPe and reflect the actual patterns of GPe and GPi connectivity within large-scale cortico-thalamo-cortical circuits.

Optimized Diffusion Imaging for Brain Structural Connectome Analysis.

High angular resolution diffusion imaging (HARDI) is a type of diffusion magnetic resonance imaging (dMRI) that measures diffusion signals on a sphere in q-space. It has been widely used in data acquisition for human brain structural connectome analysis. To more accurately estimate the structural connectome, dense samples in q-space are often acquired, potentially resulting in long scanning times and logistical challenges. This paper proposes a statistical method to select q-space directions optimally and estimate the local diffusion function from sparse observations. The proposed approach leverages relevant historical dMRI data to calculate a prior distribution to characterize local diffusion variability in each voxel in a template space. For a new subject to be scanned, the priors are mapped into the subject-specific coordinate and used to help select the best q-space samples. Simulation studies demonstrate big advantages over the existing HARDI sampling and analysis framework. We also applied the proposed method to the Human Connectome Project data and a dataset of aging adults with mild cognitive impairment. The results indicate that with very few q-space samples (e.g., 15 or 20), we can recover structural brain networks comparable to the ones estimated from 60 or more diffusion directions with the existing methods.

Bridging the gap between constrained spherical deconvolution and diffusional variance decomposition via tensor-valued diffusion MRI.

Diffusion tensor imaging (DTI) is widely used to extract valuable tissue measurements and white matter (WM) fiber orientations, even though its lack of specificity is now well-known, especially for WM fiber crossings. Models such as constrained spherical deconvolution (CSD) take advantage of high angular resolution diffusion imaging (HARDI) data to compute fiber orientation distribution functions (fODF) and tackle the orientational part of the DTI limitations. Furthermore, the recent introduction of tensor-valued diffusion MRI allows for diffusional variance decomposition (DIVIDE), enabling the computation of measures more specific to microstructure than DTI measures, such as microscopic fractional anisotropy (μFA). Recent work on making CSD compatible with tensor-valued diffusion MRI data opens the door for methods combining CSD and DIVIDE to get both fODFs and microstructure measures. However, the impacts of such atypical data on fODF reconstruction with CSD are yet to be fully known and understood. In this work, we use simulated data to explore the effects of various combinations of diffusion encodings on the angular resolution of extracted fOFDs and on the versatility of CSD in multiple realistic situations. We also compare the combinations with regards to their performance at producing accurate and precise μFA with DIVIDE, and present an optimized 10min protocol combining linear and spherical b-tensor encodings for both methods. We show that our proposed protocol enables the reconstruction of both fODFs and μFA on in vivo data.

Chinese adult brain atlas with functional and white matter parcellation

Brain atlases play important roles in studying anatomy and function of the brain. As increasing interests in multi-modal magnetic resonance imaging (MRI) approaches, such as combining structural MRI, diffusion weighted imaging (DWI), and resting-state functional MRI (rs-fMRI), there is a need to construct integrated brain atlases based on these three imaging modalities. This study constructed a multi-modal brain atlas for a Chinese aging population (n = 180, age: 22–79 years), which consists of a T1 atlas showing the brain morphology, a high angular resolution diffusion imaging (HARDI) atlas delineating the complex fiber architecture, and a rs-fMRI atlas reflecting brain intrinsic functional organization in one stereotaxic coordinate. We employed large deformation diffeomorphic metric mapping (LDDMM) and unbiased diffeomorphic atlas generation to simultaneously generate the T1 and HARDI atlases. Using spectral clustering, we generated 20 brain functional networks from rs-fMRI data. We demonstrated the use of the atlas to explore the coherent markers among the brain morphology, functional networks, and white matter tracts for aging and gender using joint independent component analysis.

Magnetic resonance tractogtaphy: possibilities and limitations, modern approach to data processing

Purpose: systematization of the knowledge about diffusion tensor magnetic resonance tomography; analysis of literature related to current limitations of this method and possibilities of overcoming these limitations.Materials and methods. We have analyzed 74 publications (6 Проанализировано 74 публикации (6 Russian, 68 foreign), published in the time period from 1986 to 2021years.  More, than half of these articles were published in the last ten years, 19 studies-in the time period from 2016 to 2021years.Results. In this article we  represent the physical basis of diffusion weighted techniques of magnetic resonance tomography, principles of obtaining diffusion weighted images and diffusion tensor, cover the specific features of the probabilistic and deterministic approaches of the diffusion tensor MRI data processing, describe methods of evaluation of the diffusion characteristics of tissues in clinical practice. Article provides a thorough introduction to the reasons of existing limitations of diffusion tensor MRI and systematization the main developed approaches of overcoming these limitations, such as multi-tensor model, high angular resolution diffusion imaging, diffusion kurtosis visualization. The article consistently reviews the stages of data processing of diffusion tensor magnetic resonance tomography (preprocessing, processing and post processing). We also describe the special aspects of the main approaches to the quantitative data analysis of diffusion tensor magnetic resonance tomography (such as analysis of the region of interest, analysis of the total data amount, quantitative tractography).Conclusion. Magnetic resonance tractography is a unique technique for noninvasive in vivo visualization of brain white matter tracts and assessment of the structural integrity of their constituent axons. In the meantime this technique, which has found applications in numerous pathologies of central nervous system, has a number of significant limitations, and the main of them are the inability to adequately visualize the crossing fibers and the relatively low reproducibility of the results. Standardization of the data postprocessing algorithms, further upgrading of the magnetic resonance scanners and implementation of the alternative tractography methods have the potential of partially reducing of the current limitations.

VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI

Diffusion MRI (dMRI) is one of the most popular techniques for studying the brain structure, mainly the white matter region. Among several sampling methods in dMRI, the high angular resolution diffusion imaging (HARDI) technique has attracted researchers due to its more accurate fiber orientation estimation. However, the current single-shell HARDI makes the intravoxel structure challenging to estimate accurately. While multi-shell acquisition can address this problem, it takes a longer scanning time, restricting its use in clinical applications. In addition, most existing dMRI scanners with low gradient-strengths often acquire single-shell up to b=1000s/mm2 because of signal-to-noise ratio issues and severe image artefacts. Hence, we propose a novel generative adversarial network, VRfRNet, for the reconstruction of multi-shell multi-tissue fiber orientation distribution function from single-shell HARDI volumes. Such a transformation learning is performed in the spherical harmonics (SH) space, as raw input HARDI volume is transformed to SH coefficients to soften gradient directions. The proposed VRfRNet consists of several modules, such as multi-context feature enrichment module, feature level attention, and softmax level attention. In addition, three loss functions have been used to optimize network learning, including L1, adversarial, and total variation. The network is trained and tested using standard qualitative and quantitative performance metrics on the publicly available HCP data-set.