Combined tract segmentation and orientation mapping for bundle-specific tractography

Wasserthal, Jakob; Neher, Peter; Hirjak, Dušan; Maier‐Hein, Klaus H.

doi:10.1016/j.media.2019.101559

Cited by 125 publications

(135 citation statements)

References 35 publications

(55 reference statements)

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“…We also calculate multi-shell multi-tissue constrained spherical deconvolution (CSD) data, followed by FOD peak extraction with a maximum number of three peaks per voxel, using MRtrix 4 [60], [61]. The extracted FOD peaks are subsequently provided to TractSeg 5 to compute TOMs of 72 white matter tracts [47]. In our method, to Fig.…”

Section: Experimental Datasetsmentioning

confidence: 99%

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

Wells

2021

Preprint

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In this paper, we present a deep learning method, DDMReg, for fast and accurate registration between diffusion MRI (dMRI) datasets. In dMRI registration, the goal is to spatially align brain anatomical structures while ensuring that local fiber orientations remain consistent with the underlying white matter fiber tract anatomy. To the best of our knowledge, DDMReg is the first deep-learning-based dMRI registration method. DDMReg is a fully unsupervised method for deformable registration between pairs of dMRI datasets. We propose a novel registration architecture that leverages not only whole brain information but also tract-specific fiber orientation information. We perform comparisons with four state-of-the-art registration methods. We evaluate the registration performance by assessing the ability to align anatomically corresponding brain structures and ensure fiber spatial agreement between different subjects after registration. Experimental results show that DDMReg obtains significantly improved registration performance. In addition, DDMReg leverages deep learning techniques and provides a fast and efficient tool for dMRI registration.

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Section: Experimental Datasetsmentioning

confidence: 99%

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

Wells

2021

Preprint

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“…In order to estimate AFD along various commonly investigated white matter fibre pathways, white matter tract segmentation was performed. We applied the automated TractSeg technique (Wasserthal, et al, 2018;Wasserthal, et al, 2019) in population template space, as this technique provides a balance between manual dissection and atlas-based tracking approaches. Of the existing library of 72 tracts, we chose to delineate 38 commonly investigated fibre pathways bilaterally for the left (L) and right (R) hemisphere ( Figure S2).…”

Section: Image Processing and Analysismentioning

confidence: 99%

Impact of b-value on estimates of apparent fibre density

Genc

Tax

Raven

et al. 2020

Preprint

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Recent advances in diffusion magnetic resonance imaging (dMRI) analysis techniques have improved our understanding of fibre-specific variations in white matter microstructure.Increasingly, studies are adopting multi-shell dMRI acquisitions to improve the robustness of dMRI-based inferences. However, the impact of b-value choice on the estimation of dMRI measures such as apparent fibre density (AFD) derived from spherical deconvolution is not known. Here, we investigate the impact of b-value sampling scheme on estimates of AFD.First, we performed simulations to assess the correspondence between AFD and simulated intra-axonal signal fraction across multiple b-value sampling schemes. We then studied the impact of sampling scheme on the relationship between AFD and age in a developmental population (n=78) aged 8-18 (mean=12.4, SD=2.9 years) using hierarchical clustering and whole brain fixel-based analyses. Multi-shell dMRI data were collected at 3.0T using ultrastrong gradients (300 mT/m), using 6 diffusion-weighted shells ranging from 0 -6000 s/mm 2 . Simulations revealed that the correspondence between estimated AFD and simulated intra-axonal signal fraction was improved with high b-value shells due to increased suppression of the extra-axonal signal. These results were supported by in vivo data, as sensitivity to developmental age-relationships was improved with increasing bvalue (b=6000 s/mm 2 , median R 2 = .34; b=4000 s/mm 2 , median R 2 = .29; b=2400 s/mm 2 , median R 2 = .21; b=1200 s/mm 2 , median R 2 = .17) in a tract-specific fashion. Overall, estimates of AFD and age-related microstructural development were better characterised at high diffusion-weightings due to improved correspondence with intra-axonal properties.

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“…Briefly, TractSeg is based on convolutional neural networks and performs bundle-specific tractography based on a field of estimated fiber orientations [50,53], and delineates 72 bundles. We implemented the dockerized version at (https://github.com/MIC-DKFZ/TractSeg).…”

Section: Subject-level Processing: Tractographymentioning

confidence: 99%

Pandora: 4-D white matter bundle population-based atlases derived from diffusion MRI fiber tractography

Hansen

Yang

Lyu

et al. 2020

Preprint

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Brain atlases have proven to be valuable neuroscience tools for localizing regions of interest and performing statistical inferences on populations. Although many human brain atlases exist, most do not contain information about white matter structures, often neglecting them completely or labelling all white matter as a single homogenous substrate. While few white matter atlases do exist based on diffusion MRI fiber tractography, they are often limited to descriptions of white matter as spatially separate "regions" rather than as white matter "bundles" or fascicles, which are well-known to overlap throughout the brain. Additional limitations include small sample sizes, few white matter pathways, and the use of outdated diffusion models and techniques. Here, we present a new population-based collection of white matter atlases represented in both volumetric and surface coordinates in a standard space. These atlases are based on 2443 subjects, and include 216 white matter bundles derived from 6 different state-of-the-art tractography techniques. This atlas is freely available and will be a useful resource for parcellation and segmentation.

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Combined tract segmentation and orientation mapping for bundle-specific tractography

Cited by 125 publications

References 35 publications

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

Deep Diffusion MRI Registration (DDMReg): A Deep Learning Method for Diffusion MRI Registration

Impact of b-value on estimates of apparent fibre density

Pandora: 4-D white matter bundle population-based atlases derived from diffusion MRI fiber tractography

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