scholarly journals DACO: Distortion/artefact correction for diffusion MRI data in an integrated framework

2021 ◽  
Author(s):  
Yung-Chin Hsu ◽  
Wen-Yih Isaac Tseng
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Mathematical Formulation ◽  
Magnetic Resonance Images ◽  
Model Parameters ◽  
Weighted Image ◽  
The Real ◽  
Integrated Framework ◽  
Human Connectome Project ◽  
Anatomical Images

In this paper we propose a registration-based algorithm to correct various distortions or artefacts (DACO) commonly observed in diffusion weighted (DW) magnetic resonance images (MRI). The registration in DACO is proceeded on the basis of a pseudo b_0 image, which is synthesized from the anatomical images such as T1-weighted image or T2-weighted image, and a pseudo diffusion MRI (dMRI) data, which is derived from the Gaussian model of diffusion tensor imaging (DTI) or the Hermite model of MAP-MRI. DACO corrects (1) the susceptibility-induced distortions, (2) the intensity inhomogeneity, and (3) the misalignment between the dMRI data and anatomical images by registering the real b_0 image to the pseudo b_0 image, and corrects (4) the eddy current (EC)-induced distortions and (5) the head motions by registering each of the DW images in the real dMRI data to the corresponding image in the pseudo dMRI data. As the above artefacts interact with each other, DACO models each type of artefact in an integrated framework and estimates these models in an interleaved and iterative manner. The mathematical formulation of the models and the comprehensive estimation procedures are detailed in this paper. The evaluation using the human connectome project data shows that DACO could estimate the model parameters accurately. Furthermore, the evaluation conducted on the real human data acquired from clinical MRI scanners reveals that the method could reduce the artefacts effectively. The DACO method leverages the anatomical image, which is routinely acquired in clinical practice, to correct the artefacts, minimizing the additional acquisitions needed to conduct the algorithm. Therefore, our method is beneficial to most dMRI data, particularly to those without acquiring the field map or blip-up and blip-down images.

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

Neurosurgery ◽  
2016 ◽  
Vol 79 (3) ◽  
pp. 437-455 ◽  
Author(s):  
Antonio Meola ◽  
Fang-Cheng Yeh ◽  
Wendy Fellows-Mayle ◽  
Jared Weed ◽  
Juan C. Fernandez-Miranda
Keyword(s):  
Spatial Resolution ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Population Based ◽  
Medial Longitudinal Fasciculus ◽  
Surgical Approaches ◽  
Medial Lemniscus ◽  
Spinothalamic Tract ◽  
Rubrospinal Tract ◽  
Human Connectome Project

Abstract BACKGROUND The brainstem is one of the most challenging areas for the neurosurgeon 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 fiber 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 validated them with histological analysis. The pathways included the cerebellar peduncles, corticospinal tract, corticopontine tracts, medial lemniscus, lateral lemniscus, spinothalamic 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 supracollicular, infracollicular, lateral mesencephalic, perioculomotor, peritrigeminal, anterolateral (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 TractoFlow: A robust, efficient and reproducible diffusion MRI pipeline leveraging Nextflow & Singularity

2019 ◽  
Author(s):  
Guillaume Theaud ◽  
Jean-Christophe Houde ◽  
Arnaud Boré ◽  
François Rheault ◽  
Felix Morency ◽  
...  
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Brain Connectivity ◽  
Reversed Phase ◽  
Fiber Density ◽  
Processing Pipeline ◽  
Diffusion Weighted Images ◽  
Brain Extraction ◽  
Human Connectome Project ◽  
Over Time

AbstractA diffusion MRI (dMRI) tractography processing pipeline should be: i) reproducible in immediate test-test, ii) reproducible in time, iii) efficient and iv) easy to use. Two runs of the same processing pipeline with the same input data should give the same output today, tomorrow and in 2 years. However, processing dMRI data requires a large number of steps (20+ steps) that, at this time, may not be reproducible between runs or over time. If parameters such as the number of threads or the random number generator are not carefully set in the brain extraction, registration and fiber tracking steps, the end tractography results obtained can be far from reproducible and limit brain connectivity studies. Moreover, processing can take several hours to days of computation for a large database, even more so if the steps are running sequentially.To handle these issues, we present TractoFlow, a fully automated pipeline that processes datasets from the raw diffusion weighted images (DWI) to tractography. It also outputs classical diffusion tensor imaging measures (fractional anisotropy (FA) and diffu-sivities) and several HARDI measures (Number of Fiber Orientation (NuFO), Apparent Fiber Density (AFD)). The pipeline requires a DWI and T1-weighted image as NIfTI files and b-values/b-vectors in FSL format. An optional reversed phase encoded b=0 image can also be used. This pipeline is based on two technologies: Nextflow and Singularity, as well as recommended pre-processing and processing steps from the dMRI community. In this work, the TractoFlow pipeline is evaluated on three databases and shown to be efficient and reproducible from 98% to 100% depending on parameter choices. For example, 105 subjects from the Human Connectome Project (HCP) were fully ran in twenty-five (25) hours to produce, for each subject, a whole-brain tractogram with 4 million streamlines. The contribution of this paper is to introduce the importance of a robust pipeline in terms of runtime and reproducibility over time. In the era of open data and open science, efficiency and reproducibility is critical in neuroimaging projects. Our TractoFlow pipeline is publicly available for academic research and is an important step forward for better structural brain connectivity mapping.

scholarly journals Bayesian non-central chi regression for neuroimaging

2016 ◽  
Author(s):  
Bertil Wegmann ◽  
Anders Eklund ◽  
Mattias Villani
Keyword(s):  
Regression Model ◽  
Diffusion Tensor ◽  
Brain Activity ◽  
Gaussian Model ◽  
Mean Diffusivity ◽  
Bayesian Variable Selection ◽  
Noise Model ◽  
Model Parameters ◽  
Tensor Model ◽  
Human Connectome Project

AbstractWe propose a regression model for non-central χ (NC-χ) distributed functional magnetic resonance imaging (fMRI) and diffusion weighted imaging (DWI) data, with the heteroscedastic Rician regression model as a prominent special case. The model allows both parameters in the NC-χ distribution to be linked to explanatory variables, with the relevant covariates automatically chosen by Bayesian variable selection. A highly efficient Markov chain Monte Carlo (MCMC) algorithm is proposed for simulating from the joint Bayesian posterior distribution of all model parameters and the binary covariate selection indicators. Simulated fMRI data is used to demonstrate that the Rician model is able to localize brain activity much more accurately than the traditionally used Gaussian model at low signal-to-noise ratios. Using a diffusion dataset from the Human Connectome Project, it is also shown that the commonly used approximate Gaussian noise model underestimates the mean diffusivity (MD) and the fractional anisotropy (FA) in the single-diffusion tensor model compared to the theoretically correct Rician model.

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 Through-plane super-resolution with autoencoders in diffusion magnetic resonance imaging of the developing human brain

2021 ◽  
Author(s):  
Hamza Kebiri ◽  
Erick J. Canales Rodríguez ◽  
Hélène Lajous ◽  
Priscille de Dumast ◽  
Gabriel Girard ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Diffusion Tensor ◽  
Learning Algorithm ◽  
Fetal Brain ◽  
Super Resolution ◽  
Magnetic Resonance Images ◽  
Brain Images ◽  
Ability To Act ◽  
Human Connectome Project ◽  
Term Newborns

ABSTRACTFetal brain diffusion magnetic resonance images are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network to enhance the through-plane resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and on the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize to fetal data with different levels of motion and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.

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 Parameter Optimization for Real-World ENSO Forecast in an Intermediate Coupled Model

2019 ◽  
Vol 147 (5) ◽  
pp. 1429-1445 ◽  
Author(s):  
Yuchu Zhao ◽  
Zhengyu Liu ◽  
Fei Zheng ◽  
Yishuai Jin
Keyword(s):  
Data Assimilation ◽  
Parameter Optimization ◽  
Real World ◽  
Climate Models ◽  
Coupled Model ◽  
Prediction Skill ◽  
Model Parameters ◽  
Real World Data ◽  
The Real ◽  
Advection Term

Abstract We performed parameter estimation in the Zebiak–Cane model for the real-world scenario using the approach of ensemble Kalman filter (EnKF) data assimilation and the observational data of sea surface temperature and wind stress analyses. With real-world data assimilation in the coupled model, our study shows that model parameters converge toward stable values. Furthermore, the new parameters improve the real-world ENSO prediction skill, with the skill improved most by the parameter of the highest climate sensitivity (gam2), which controls the strength of anomalous upwelling advection term in the SST equation. The improved prediction skill is found to be contributed mainly by the improvement in the model dynamics, and second by the improvement in the initial field. Finally, geographic-dependent parameter optimization further improves the prediction skill across all the regions. Our study suggests that parameter optimization using ensemble data assimilation may provide an effective strategy to improve climate models and their real-world climate predictions in the future.

Segmentation of corpus callosum based on tensor fuzzy clustering algorithm

2021 ◽  
pp. 1-14
Author(s):  
Yujia Qu ◽  
Yuanjun Wang
Keyword(s):  
Corpus Callosum ◽  
Fuzzy Clustering ◽  
Clustering Algorithm ◽  
Diffusion Tensor ◽  
Region Of Interest ◽  
Midsagittal Plane ◽  
Fuzzy Clustering Algorithm ◽  
Human Connectome Project ◽  
Clustering Center ◽  
Fixed Region

BACKGROUND: The corpus callosum in the midsagittal plane plays a crucial role in the early diagnosis of diseases. When the anisotropy of the diffusion tensor in the midsagittal plane is calculated, the anisotropy of corpus callosum is close to that of the fornix, which leads to blurred boundary of the segmentation region. OBJECTIVE: To apply a fuzzy clustering algorithm combined with new spatial information to achieve accurate segmentation of the corpus callosum in the midsagittal plane in diffusion tensor images. METHODS: In this algorithm, a fixed region of interest is selected from the midsagittal plane, and the anisotropic filtering algorithm based on tensor is implemented by replacing the gradient direction of the structural tensor with an eigenvector, thus filtering the diffusion tensor of region of interest. Then, the iterative clustering center based on K-means clustering is used as the initial clustering center of tensor fuzzy clustering algorithm. Taking filtered diffusion tensor as input data and different metrics as similarity measures, the neighborhood diffusion tensor pixel calculation method of Log Euclidean framework is introduced in the membership function calculation, and tensor fuzzy clustering algorithm is proposed. In this study, MGH35 data from the Human Connectome Project (HCP) are tested and the variance, accuracy and specificity of the experimental results are discussed. RESULTS: Segmentation results of three groups of subjects in MGH35 data are reported. The average segmentation accuracy is 97.34%, and the average specificity is 98.43%. CONCLUSIONS: When segmenting the corpus callosum of diffusion tensor imaging, our method cannot only effective denoise images, but also achieve high accuracy and specificity.

scholarly journals Functional and diffusion MRI reveal the neurophysiological basis of neonates’ noxious-stimulus evoked brain activity

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Luke Baxter ◽  
Fiona Moultrie ◽  
Sean Fitzgibbon ◽  
Marianne Aspbury ◽  
Roshni Mansfield ◽  
...  
Keyword(s):  
White Matter ◽  
Prediction Model ◽  
Resting State ◽  
Early Life ◽  
Diffusion Mri ◽  
Brain Activity ◽  
Noxious Stimulus ◽  
Noxious Stimulation ◽  
Human Connectome Project ◽  
And Diffusion

AbstractUnderstanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.

scholarly journals Disruption of neurite morphology parallels MS progression

2018 ◽  
Vol 5 (6) ◽  
pp. e502 ◽  
Author(s):  
Barbara Spanò ◽  
Giovanni Giulietti ◽  
Valerio Pisani ◽  
Manuela Morreale ◽  
Elisa Tuzzi ◽  
...  
Keyword(s):  
Brain Tissue ◽  
Disease Severity ◽  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Compartment Model ◽  
Dispersion Index ◽  
Brain Areas ◽  
Neurite Density ◽  
Relapsing Remitting ◽  
Diffusion Tensor Imaging Dti

ObjectivesTo apply advanced diffusion MRI methods to the study of normal-appearing brain tissue in MS and examine their correlation with measures of clinical disability.MethodsA multi-compartment model of diffusion MRI called neurite orientation dispersion and density imaging (NODDI) was used to study 20 patients with relapsing-remitting MS (RRMS), 15 with secondary progressive MS (SPMS), and 20 healthy controls. Maps of NODDI were analyzed voxel-wise to assess the presence of abnormalities within the normal-appearing brain tissue and the association with disease severity. Standard diffusion tensor imaging (DTI) parameters were also computed for comparing the 2 techniques.ResultsPatients with MS showed reduced neurite density index (NDI) and increased orientation dispersion index (ODI) compared with controls in several brain areas (p < 0.05), with patients with SPMS having more widespread abnormalities. DTI indices were also sensitive to some changes. In addition, patients with SPMS showed reduced ODI in the thalamus and caudate nucleus. These abnormalities were associated with scores of disease severity (p < 0.05). The association with the MS functional composite score was higher in patients with SPMS compared with patients with RRMS.ConclusionsNODDI and DTI findings are largely overlapping. Nevertheless, NODDI helps interpret previous findings of increased anisotropy in the thalamus of patients with MS and are consistent with the degeneration of selective axon populations.

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