scholarly journals Higher order spherical harmonics reconstruction of fetal diffusion MRI with intensity correction

2018 ◽  
Author(s):  
Maria Deprez ◽  
Anthony Price ◽  
Daan Christiaens ◽  
Georgia Lockwood Estrin ◽  
Lucilio Cordero-Grande ◽  
...  
Keyword(s):  
Spherical Harmonics ◽  
Diffusion Mri ◽  
Brain Connectivity ◽  
In Utero ◽  
Higher Order ◽  
Developing Brain ◽  
Whole Brain ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Comprehensive Method

AbstractWe present a comprehensive method for reconstruction of fetal diffusion MRI signal using a higher order spherical harmonics representation, that includes motion, distortion and intensity correction. By applying constrained spherical deconvolution and whole brain tractography to reconstructed fetal diffusion MRI we are able to identify main WM tracts and anatomically plausible fiber crossings. The proposed methodology facilitates detailed investigation of developing brain connectivity and microstructure in-utero.

Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data

NeuroImage ◽  
2014 ◽  
Vol 103 ◽  
pp. 411-426 ◽  
Author(s):  
Ben Jeurissen ◽  
Jacques-Donald Tournier ◽  
Thijs Dhollander ◽  
Alan Connelly ◽  
Jan Sijbers
Keyword(s):  
Diffusion Mri ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution

scholarly journals Higher Order Spherical Harmonics Reconstruction of Fetal Diffusion MRI With Intensity Correction

2020 ◽  
Vol 39 (4) ◽  
pp. 1104-1113 ◽  
Author(s):  
Maria Deprez ◽  
Anthony Price ◽  
Daan Christiaens ◽  
Georgia Lockwood Estrin ◽  
Lucilio Cordero-Grande ◽  
...  
Keyword(s):  
Spherical Harmonics ◽  
Diffusion Mri ◽  
Higher Order

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 Enabling constrained spherical deconvolution and diffusional variance decomposition with tensor-valued diffusion MRI

2021 ◽  
Author(s):  
Philippe Karan ◽  
Alexis Reymbaut ◽  
Guillaume Gilbert ◽  
Maxime Descoteaux
Keyword(s):  
Diffusion Mri ◽  
Diffusion Tensor ◽  
Simulated Data ◽  
Orientation Distribution ◽  
Variance Decomposition ◽  
Distribution Functions ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution ◽  
Fiber Orientation Distribution

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 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), opening the door to the computation of measures more specific to microstructure than DTI measures, such as microscopic fractional anisotropy (μFA). However, tensor-valued diffusion MRI data is not compatible with latest versions of CSD and the impacts of such atypical data on fODF reconstruction with CSD are yet to be studied. In this work, we lay down the mathematical and computational foundations of a tensor-valued CSD and use simulated data to explore the effects of various combinations of diffusion encodings on the angular resolution of extracted fOFDs. We also compare the combinations with regards to their performance at producing accurate and precise μFA with DIVIDE, and present an optimised protocol for both methods. We show that our proposed protocol enables the reconstruction of both fODFs and μFA on in vivo data.

scholarly journals Constrained spherical deconvolution of nonspherically sampled diffusion MRI data

2020 ◽  
Vol 42 (2) ◽  
pp. 521-538
Author(s):  
Jan Morez ◽  
Jan Sijbers ◽  
Floris Vanhevel ◽  
Ben Jeurissen
Keyword(s):  
Diffusion Mri ◽  
Constrained Spherical Deconvolution ◽  
Spherical Deconvolution

Tracking whole-brain connectivity dynamics in the resting-state fMRI with post-facial paralysis synkinesis

2021 ◽  
Author(s):  
Zhen-Zhen Ma ◽  
Jia-Jia Wu ◽  
Xu-Yun Hua ◽  
Mou-Xiong Zheng ◽  
Xiang-Xin Xing ◽  
...  
Keyword(s):  
Facial Paralysis ◽  
Resting State ◽  
Brain Connectivity ◽  
Resting State Fmri ◽  
Whole Brain
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