scholarly journals Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI

2020 ◽  
Author(s):  
Jianzhong He ◽  
Fan Zhang ◽  
Guoqiang Xie ◽  
Shun Yao ◽  
Yuanjing Feng ◽  
...  
Keyword(s):  
Visual Information ◽  
Diffusion Mri ◽  
Unscented Kalman Filter ◽  
Visual Pathway ◽  
Resonance Imaging ◽  
Spherical Deconvolution ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri ◽  
Reconstruction Rate ◽  
Tracking Strategy

AbstractThe retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. Anatomically, the RGVP can be separated into four subdivisions, including two decussating and two non-decussating fiber pathways, which cannot be identified by conventional magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the anatomy of the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for tractographic reconstruction of the RGVP. In this study, four different tractography algorithms, including constrained spherical deconvolution (CSD) model based probabilistic (iFOD1) and deterministic (SD-Stream) methods, and multi-fiber (UKF-2T) and single-fiber (UKF-1T) unscented Kalman filter (UKF) tractography methods, are compared for reconstruction of the RGVP. Experiments are performed using diffusion MRI data of 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Anatomical measurements are used to assess the advantages and limitations of the four tracking strategies, including the reconstruction rate of the four RGVP subdivisions, the percentage of decussating fibers, the correlation between volumes of the traced RGVPs and a T1w-based RGVP segmentation, and an expert judgment to rank the anatomical appearance of the reconstructed RGVPs. Overall, we conclude that UKF-2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF-2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy.

scholarly journals Diffusion-weighted Magnetic Resonance Imaging in the Early Diagnosis of Neonatal Adrenoleukodystrophy

2011 ◽  
Vol 1 ◽  
pp. 20 ◽  
Author(s):  
R Nuri Sener ◽  
Mehmet H Atalar
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Diffusion Mri ◽  
Resonance Imaging ◽  
Baby Girl ◽  
Laboratory Findings ◽  
Neonatal Adrenoleukodystrophy ◽  
First Case ◽  
Weighted Magnetic Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

A newborn baby girl developed seizures right after birth. On the fourth day, the baby was examined using diffusion sequence magnetic resonance imaging (MRI) and diagnosed to have neonatal adrenoleukodystrophy. Laboratory findings confirmed the diagnosis. This is the first case of neonatal adrenoleukodystrophy (NALD) where diffusion MRI sequence helped in the diagnosis. We find association of NALD with seizures at birth is an extremely rare occurrence, and so far, only one case has been mentioned in the literature.

The Value of Quantitative Musculoskeletal Imaging

2020 ◽  
Vol 24 (04) ◽  
pp. 460-474
Author(s):  
Jacob J. Visser ◽  
Stacy K. Goergen ◽  
Stefan Klein ◽  
Teodoro Martín Noguerol ◽  
Perry J. Pickhardt ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Clinical Utility ◽  
T2 Mapping ◽  
Quantitative Imaging ◽  
Musculoskeletal Imaging ◽  
Perfusion Magnetic Resonance Imaging ◽  
Imaging Data ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri ◽  
Patient Prognosis

AbstractMusculoskeletal imaging is mainly based on the subjective and qualitative analysis of imaging examinations. However, integration of quantitative assessment of imaging data could increase the value of imaging in both research and clinical practice. Some imaging modalities, such as perfusion magnetic resonance imaging (MRI), diffusion MRI, or T2 mapping, are intrinsically quantitative. But conventional morphological imaging can also be analyzed through the quantification of various parameters. The quantitative data retrieved from imaging examinations can serve as biomarkers and be used to support diagnosis, determine patient prognosis, or monitor therapy.We focus on the value, or clinical utility, of quantitative imaging in the musculoskeletal field. There is currently a trend to move from volume- to value-based payments. This review contains definitions and examines the role that quantitative imaging may play in the implementation of value-based health care. The influence of artificial intelligence on the value of quantitative musculoskeletal imaging is also discussed.

scholarly journals Imaging the Visual Pathway in Neuromyelitis Optica

2011 ◽  
Vol 2011 ◽  
pp. 1-5 ◽  
Author(s):  
Caspar F. Pfueller ◽  
Friedemann Paul
Keyword(s):  
Magnetic Resonance Imaging ◽  
Optical Coherence Tomography ◽  
Magnetic Resonance ◽  
Neuromyelitis Optica ◽  
Current Knowledge ◽  
Visual Pathway ◽  
Optical Coherence ◽  
Resonance Imaging ◽  
Magnetic Resonance Imaging Mri

The focus of this paper is to summarize the current knowledge on visual pathway damage in neuromyelitis optica (NMO) assessed by magnetic resonance imaging (MRI) and optical coherence tomography (OCT).

scholarly journals An atlas of white matter anatomy, its variability, and reproducibility based on Constrained Spherical Deconvolution of diffusion MRI

2021 ◽  
Author(s):  
Ahmed M. Radwan ◽  
Stefan Sunaert ◽  
Kurt G. Schilling ◽  
Maxime Descoteaux ◽  
Bennett A. Landman ◽  
...  
Keyword(s):  
White Matter ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Single Subject ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Pyramidal Tracts ◽  
Human Connectome Project ◽  
Subject Specific ◽  
Virtual Dissection

Virtual dissection of white matter (WM) using diffusion MRI tractography is confounded by its poor reproducibility. Despite the increased adoption of advanced reconstruction models, early region-of-interest driven protocols based on diffusion tensor imaging (DTI) remain the dominant reference for virtual dissection protocols. Here we bridge this gap by providing a comprehensive description of typical WM anatomy reconstructed using a reproducible automated subject-specific parcellation-based approach based on probabilistic constrained-spherical deconvolution (CSD) tractography. We complement this with a WM template in MNI space comprising 68 bundles, including all associated anatomical tract selection labels and associated automated workflows. Additionally, we demonstrate bundle inter- and intra-subject variability using 40 (20 test-retest) datasets from the human connectome project (HCP) and 5 sessions with varying b-values and number of b-shells from the single-subject Multiple Acquisitions for Standardization of Structural Imaging Validation and Evaluation (MASSIVE) dataset. The most reliably reconstructed bundles were the whole pyramidal tracts, primary corticospinal tracts, whole superior longitudinal fasciculi, frontal, parietal and occipital segments of the corpus callosum and middle cerebellar peduncles. More variability was found in less dense bundles, e.g., the first segment of the superior longitudinal fasciculus, fornix, dentato-rubro-thalamic tract (DRTT), and premotor pyramidal tract. Using the DRTT as an example, we show that this variability can be reduced by using a higher number of seeding attempts. Overall inter-session similarity was high for HCP test-retest data (median weighted-dice = 0.963, stdev = 0.201 and IQR = 0.099). Compared to the HCP-template bundles there was a high level of agreement for the HCP test-retest data (median weighted-dice = 0.747, stdev = 0.220 and IQR = 0.277) and for the MASSIVE data (median weighted-dice = 0.767, stdev = 0.255 and IQR = 0.338). In summary, this WM atlas provides an overview of the capabilities and limitations of automated subject-specific probabilistic CSD tractography for mapping white matter fasciculi in healthy adults. It will be most useful in applications requiring a highly reproducible parcellation-based dissection protocol, as well as being an educational resource for applied neuroimaging and clinical professionals.

scholarly journals The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking

Diagnostics ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 115 ◽  
Author(s):  
Fernando Calamante
Keyword(s):  
Diffusion Mri ◽  
Structural Connectivity ◽  
Fibre Tracking ◽  
Seven Deadly Sins ◽  
Disease States ◽  
Tracking Process ◽  
Key Issues ◽  
Structural Connections ◽  
Magnetic Resonance Imaging Mri ◽  
Tracking Strategy

There is great interest in the study of brain structural connectivity, as white matter abnormalities have been implicated in many disease states. Diffusion magnetic resonance imaging (MRI) provides a powerful means to characterise structural connectivity non-invasively, by using a fibre-tracking algorithm. The most widely used fibre-tracking strategy is based on the step-wise generation of streamlines. Despite their popularity and widespread use, there are a number of practical considerations that must be taken into account in order to increase the robustness of streamlines tracking results, particularly when these methods are used to study brain structural connectivity, and the connectome. This review article describes what we consider the ‘seven deadly sins’ of mapping structural connections using diffusion MRI streamlines fibre-tracking, with particular emphasis on ‘sins’ that can be practically avoided and they can have an important impact in the results. It is shown that there are important ‘deadly sins’ to be avoided at every step of the pipeline, such as during data acquisition, during data modelling to estimate local fibre architecture, during the fibre-tracking process itself, and during quantification of the tracking results. The recommendations here are intended to inform users on potential important shortcomings of their current tracking protocols, as well as to guide future users on some of the key issues and decisions that must be faced when designing their processing pipelines.

scholarly journals Network alignment and similarity reveal atlas-based topological differences in structural connectomes

2020 ◽  
Author(s):  
Matteo Frigo ◽  
Emilio Cruciani ◽  
David Coudert ◽  
Rachid Deriche ◽  
Emanuele Natale ◽  
...  
Keyword(s):  
Structural Connectivity ◽  
Graph Isomorphism ◽  
Brain Regions ◽  
Jaccard Index ◽  
Brain Atlas ◽  
Resonance Imaging ◽  
Mathematical Properties ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri ◽  
A New Technique

The interactions between different brain regions can be modeled as a graph, called connectome, whose nodes correspond to parcels from a predefined brain atlas. The edges of the graph encode the strength of the axonal connectivity between regions of the atlas which can be estimated via diffusion Magnetic Resonance Imaging (MRI) tractography. Herein, we aim at providing a novel perspective on the problem of choosing a suitable atlas for structural connectivity studies by assessing how robustly an atlas captures the network topology across different subjects in a homogeneous cohort. We measure this robustness by assessing the alignability of the connectomes, namely the possibility to retrieve graph matchings that provide highly similar graphs. We introduce two novel concepts. First, the graph Jaccard index (GJI), a graph similarity measure based on the well-established Jaccard index between sets; the GJI exhibits natural mathematical properties that are not satisfied by previous approaches. Second, we devise WL-align, a new technique for aligning connectomes obtained by adapting the Weisfeiler-Lehman (WL) graph-isomorphism test. We validated the GJI and WL-align on data from the Human Connectome Project database, inferring a strategy for choosing a suitable parcellation for structural connectivity studies. Code and data are publicly available.

scholarly journals Discordant attributes of structural and functional connectivity in a two-layer multiplex network

2018 ◽  
Author(s):  
Sol Lim ◽  
Filippo Radicchi ◽  
Martijn P van den Heuvel ◽  
Olaf Sporns
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Structural Connectivity ◽  
Brain Regions ◽  
Resting State Networks ◽  
Structural Failure ◽  
Resonance Imaging ◽  
Multiplex Network ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri

AbstractSeveral studies have suggested that functional connectivity (FC) is constrained by the underlying structural connectivity (SC) and mutually correlated. However, not many studies have focused on differences in the network organization of SC and FC, and on how these differences may inform us about their mutual interaction. To explore this issue, we adopt a multi-layer framework, with SC and FC, constructed using Magnetic Resonance Imaging (MRI) data from the Human Connectome Project, forming a two-layer multiplex network. In particular, we examine whether node strength assortativity within and between the SC and FC layer may confer increased robustness against structural failure. We find that, in general, SC is organized assortatively, indicating brain regions are on average connected to other brain regions with similar node strengths. On the other hand, FC shows disassortative mixing. This discrepancy is apparent also among individual resting-state networks within SC and FC. In addition, these patterns show lateralization, with disassortative mixing within FC subnetworks mainly driven from the left hemisphere. We discuss our findings in the context of robustness to structural failure, and we suggest that discordant and lateralized patterns of associativity in SC and FC may explain laterality of some neurological dysfunctions and recovery.

376 Human Connectome-Based Tractographic Atlas of the Brainstem Connections and Surgical Approaches

Neurosurgery ◽  
2017 ◽  
Vol 64 (CN_suppl_1) ◽  
pp. 289-289
Author(s):  
Antonio Meola ◽  
Fang-Cheng Yeh ◽  
Wendy Fellows-Mayle ◽  
Jared Weed ◽  
Juan Carlos Fernandez-Miranda
Keyword(s):  
Spatial Resolution ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Population Based ◽  
Medial Longitudinal Fasciculus ◽  
Surgical Approaches ◽  
Medial Lemniscus ◽  
Rubrospinal Tract ◽  
Human Connectome Project ◽  
Magnetic Resonance Imaging Mri

Abstract INTRODUCTION The brainstem is one of the most challenging areas for the neuro- surgeon because of the limited space between gray matter nuclei and white matter pathways. Diffusion tensor imaging based tractography has been used to study the brainstem structure, but the angular and spatial resolution could be improved further with advanced diffusion magnetic resonance imaging (MRI). Objective: To construct a high angular/spatial resolution, wide-population based, comprehensive tractography atlas that presents an anatomical review of the surgical approaches to the brainstem. METHODS We applied advanced diffusion MRI finer tractography to a population-based atlas constructed with data from a total of 488 subjects from the Human Connectome Project-488. Five formalin-fixed brains were studied for surgical landmarks. Luxol Fast Blue stained histological sections were used to validate the results of tractography. RESULTS >We acquired the tractography of the major brainstem pathways and vali- dated them with histological analysis. The pathways included the cerebellar peduncles, corticospinal tract, corticopontine tracts, medial lemniscus, lateral lemniscus, spino- thalamic tract, rubrospinal tract, central tegmental tract, medial longitudinal fasciculus, and dorsal longitudinal fasciculus. Then, the reconstructed 3-dimensional brainstem structure was sectioned at the level of classic surgical approaches, namely supra- collicular, infracollicular, lateral mesencephalic, perioculomotor, peritrigeminal, antero- lateral (to the medulla), and retro-olivary approaches. CONCLUSION The advanced diffusion MRI fiber tracking is a powerful tool to explore the brainstem neuroanatomy and to achieve a better understanding of surgical approaches.

scholarly journals The braingraph.org database with more than 1000 robust human connectomes in five resolutions

2021 ◽  
Author(s):  
Bálint Varga ◽  
Vince Grolmusz
Keyword(s):  
Human Brain ◽  
Complex Object ◽  
Resonance Imaging ◽  
Present Contribution ◽  
The Public ◽  
Human Connectome Project ◽  
Recent Advancement ◽  
Magnetic Resonance Imaging Mri ◽  
Object Of Study ◽  
The Brain

AbstractThe human brain is the most complex object of study we encounter today. Mapping the neuronal-level connections between the more than 80 billion neurons in the brain is a hopeless task for science. By the recent advancement of magnetic resonance imaging (MRI), we are able to map the macroscopic connections between about 1000 brain areas. The MRI data acquisition and the subsequent algorithmic workflow contain several complex steps, where errors can occur. In the present contribution we describe and publish 1064 human connectomes, computed from the public release of the Human Connectome Project. Each connectome is available in 5 resolutions, with 83, 129, 234, 463 and 1015 anatomically labeled nodes. For error correction we follow an averaging and extreme value deleting strategy for each edge and for each connectome. The resulting 5320 braingraphs can be downloaded from the https://braingraph.org site. This dataset makes possible the access to this graphs for scientists unfamiliar with neuroimaging- and connectome-related tools: mathematicians, physicists and engineers can use their expertize and ideas in the analysis of the connections of the human brain. Brain scientists and computational neuroscientists also have a robust and large, multi-resolution set for connectomical studies.

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