Super-Resolution Track Density Imaging: Anatomic Detail versus Quantification

Calamante, Fernando

doi:10.3174/ajnr.a4721

Cited by 8 publications

(5 citation statements)

References 18 publications

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“…In healthy volunteers, using TM 60 revealed significantly more fascicles than using TM 30 , but no significant difference was observed between using TM 45 and TM 60 (the exception being in the case of the nasal fascicle, which was not linked to ocular laterality and dominance). The effect of varying the number of diffusion‐weighting gradient directions on the tractographic accuracy has previously been described for visualization of the optic radiation using CSD‐based tractography . In that study, tractography offered the clearest images between 40 and 60 directions, close to our findings.…”

Section: Discussionsupporting

confidence: 88%

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Track‐weighted imaging for neuroretina: Evaluations in healthy volunteers and ischemic optic neuropathy

Attyé

Jean

Remond

et al. 2018

Magnetic Resonance Imaging

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Section: Discussionsupporting

confidence: 88%

“…In a prior study, we did not collect quantitative data yielded from TWI voxels. However, that particular tractography postprocessing method was mainly developed for its high anatomical contrast and the ischemic process led to fascicle complete absence, allowing a reliable radiologic interpretation.…”

Section: Discussionmentioning

confidence: 99%

Track‐weighted imaging for neuroretina: Evaluations in healthy volunteers and ischemic optic neuropathy

Attyé

Jean

Remond

et al. 2018

Magnetic Resonance Imaging

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show abstract

“…Track-weighted imaging (TWI) represents information related to the streamlines in a 3D image format (which can have super-resolution properties); for example, TWI maps can be generated from various properties of the streamlines themselves, such as number of streamlines in each voxel or their average length (Calamante, 2016; Pannek et al, 2011). Alternatively, TWI maps can also be computed from track-weighted version (e.g.…”

Section: Methodsmentioning

confidence: 99%

TractLearn: a geodesic learning framework for quantitative analysis of brain bundles

Attyé

Renard

Baciu

et al. 2020

Preprint

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Deep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies on metrics either coming from the tractography process itself or from each voxel along the bundle. Statistical detection of abnormal voxels in the context of disease usually relies on univariate and multivariate statistics models, such as the General Linear Model (GLM). Yet in the case of high-dimensional low sample size data, the GLM often implies high standard deviation range in controls due to anatomical variability, despite the commonly used smoothing process. This can lead to difficulties to detect subtle quantitative alterations from a brain bundle at the voxel scale. Here we introduce TractLearn, a unified framework for brain fascicles quantitative analyses by using geodesic learning as a data-driven learning task. TractLearn allows a mapping between the image high-dimensional domain and the reduced latent space of brain fascicles using a Riemannian approach. We illustrate the robustness of this method on a healthy population with test-retest acquisition of multi-shell diffusion MRI data, demonstrating that it is possible to separately study the global effect due to different MRI sessions from the effect of local bundle alterations. We have then tested the efficiency of our algorithm on a sample of 5 age-matched subjects referred with mild traumatic brain injury. Our contributions are to propose an algorithm based on: 1/ A manifold approach to capture controls variability as standard reference instead of an atlas approach based on a Euclidean mean 2/ The ability to detect global variation of voxels quantitative values, which means that all the voxels interaction in a structure are considered rather than analyzing each voxel independently. With this regard, TractLearn is a ready-to-use algorithm for precision medicine.

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“…In addition, we generated track-weighted imaging (TWI) maps, containing streamline-related information ( Calamante, 2017 , Pannek et al, 2011 ). This allowed us to (a) reduce errors related to coregistration between subjects and minimize highly variable and artifact-sensitive cortical endings by retaining only 80 % of the values (see Attyé et al, 2021 ); and (b) consider additional contrast parameters for quantitative analyses that were shown to be sensitive and clinically valid ( Calamante, 2016 , Willats et al, 2014 ). The first contrast was based on the FODs amplitude along the direction of the tract (TWI-FOD or TWI; Willats et al, 2014 ).…”

Section: Diffusion Mrimentioning

confidence: 99%

Leveraging manifold learning techniques to explore white matter anomalies: An application of the TractLearn pipeline in epilepsy

Roger

Attyé

Renard³

et al. 2022

NeuroImage: Clinical

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Super-Resolution Track Density Imaging: Anatomic Detail versus Quantification

Cited by 8 publications

References 18 publications

Track‐weighted imaging for neuroretina: Evaluations in healthy volunteers and ischemic optic neuropathy

Track‐weighted imaging for neuroretina: Evaluations in healthy volunteers and ischemic optic neuropathy

TractLearn: a geodesic learning framework for quantitative analysis of brain bundles

Leveraging manifold learning techniques to explore white matter anomalies: An application of the TractLearn pipeline in epilepsy

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