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- Jörgens, Daniel, 1988-, et al.
(author)
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Challenges for tractogram filtering
- 2021
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In: Anisotropy AcrossFields and Scales. - Switzerland : Springer. ; , s. 149-168
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Book chapter (peer-reviewed)abstract
- Tractography aims at describing the most likely neural fiber paths in white matter. A general issue of current tractography methods is their large false-positive rate. An approach to deal with this problem is tractogram filtering in which anatomically implausible streamlines are discarded as a post-processing step after tractography. In this chapter, we review the main approaches and methods from the literature that are relevant for the application of tractogram filtering. Moreover, we give a perspective on the central challenges for the development of new methods, including modern machine learning techniques, in this field in the next few years.
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| 2. |
- Jörgens, Daniel, 1988-, et al.
(author)
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Merging label sources and multiple modalities in a deep neural network for tractogram filtering
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Other publication (other academic/artistic)abstract
- One of the main issues of current tractography methods is their high false-positive rate. Tractogram filtering is an option for removing false positive streamlines from tractography data in a post-processing step. In this paper, we train a deep neural network for filtering tractography data in which every streamline of a tractogram is classified as plausible, implausible or inconclusive. For this, we use four different tractogram filtering strategies as supervisors, whose outputs are combined to obtain the classification labels for the streamlines. We assessed the importance of different features of the streamlines for performing this classification task, including the coordinates of the streamlines, diffusion data, landmarks, T1-weighted information and a brain parcellation. We found that the streamline coordinates are the most relevant, followed by the diffusion data, in this particular classification task.
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| 3. |
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| 4. |
- Qiu, Ting, et al.
(author)
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Structural white matter properties and cognitive resilience to tau pathology
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In: Alzheimer's and Dementia. - 1552-5260.
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Journal article (peer-reviewed)abstract
- INTRODUCTION: We assessed whether macro- and/or micro-structural white matter properties are associated with cognitive resilience to Alzheimer's disease pathology years prior to clinical onset. METHODS: We examined whether global efficiency, an indicator of communication efficiency in brain networks, and diffusion measurements within the limbic network and default mode network moderate the association between amyloid-β/tau pathology and cognitive decline. We also investigated whether demographic and health/risk factors are associated with white matter properties. RESULTS: Higher global efficiency of the limbic network, as well as free-water corrected diffusion measures within the tracts of both networks, attenuated the impact of tau pathology on memory decline. Education, age, sex, white matter hyperintensities, and vascular risk factors were associated with white matter properties of both networks. DISCUSSION: White matter can influence cognitive resilience against tau pathology, and promoting education and vascular health may enhance optimal white matter properties. Highlights: Aβ and tau were associated with longitudinal memory change over ∼7.5 years. White matter properties attenuated the impact of tau pathology on memory change. Health/risk factors were associated with white matter properties.
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| 5. |
- Reymbaut, Alexis, et al.
(author)
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Magic DIAMOND : Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding
- 2021
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In: Medical Image Analysis. - : Elsevier BV. - 1361-8415. ; 70
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Journal article (peer-reviewed)abstract
- Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this “Magic DIAMOND” model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.
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