Training shortest-path tractography: Automatic learning of spatial priors

Kasenburg, Niklas; Liptrot, Matthew G.; Reislev, Nina Linde; Ørting, Silas Nyboe; Nielsen, Mads; Garde, Ellen; Feragen, Aasa

doi:10.1016/j.neuroimage.2016.01.031

Cited by 11 publications

(6 citation statements)

References 41 publications

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“…The large number of invalid tract reconstructions we observed in our study, in line with a recent study on human tract reconstructions (Maier-Hein et al 2016 ), stresses the relatively ill-posed nature of current tractography approaches and the need for methodological progression. Use of alternative streamline filtering techniques such as SIFT2 (Smith et al 2015b ), inclusion of anatomical constraints (Smith et al 2012 ; Lemkaddem et al 2014 ) or priors (Yendiki et al 2011 ; Christiaens et al 2014 , 2015a ), or application of Bayesian connectomics (Hinne et al 2012 ; Kasenburg et al 2016 ) may lead to further improvement of connectome reconstruction accuracy. Furthermore, the increasing availability of neuronal tracer databases may aid in fine-tuning of diffusion-based tractography settings and applications, which will contribute to the progress of the rising field of connectomics.…”

Section: Discussionmentioning

confidence: 99%

Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics

et al. 2018

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Diffusion MRI (dMRI)-based tractography offers unique abilities to map whole-brain structural connections in human and animal brains. However, dMRI-based tractography indirectly measures white matter tracts, with suboptimal accuracy and reliability. Recently, sophisticated methods including constrained spherical deconvolution (CSD) and global tractography have been developed to improve tract reconstructions through modeling of more complex fiber orientations. Our study aimed to determine the accuracy of connectome reconstruction for three dMRI-based tractography approaches: diffusion tensor (DT)-based, CSD-based and global tractography. Therefore, we validated whole brain structural connectome reconstructions based on ten ultrahigh-resolution dMRI rat brain scans and 106 cortical regions, from which varying tractography parameters were compared against standardized neuronal tracer data. All tested tractography methods generated considerable numbers of false positive and false negative connections. There was a parameter range trade-off between sensitivity: 0.06–0.63 interhemispherically and 0.22–0.86 intrahemispherically; and specificity: 0.99–0.60 interhemispherically and 0.99–0.23 intrahemispherically. Furthermore, performance of all tractography methods decreased with increasing spatial distance between connected regions. Similar patterns and trade-offs were found, when we applied spherical deconvolution informed filtering of tractograms, streamline thresholding and group-based average network thresholding. Despite the potential of CSD-based and global tractography to handle complex fiber orientations at voxel level, reconstruction accuracy, especially for long-distance connections, remains a challenge. Hence, connectome reconstruction benefits from varying parameter settings and combination of tractography methods to account for anatomical variation of neuronal pathways.Electronic supplementary materialThe online version of this article (10.1007/s00429-018-1628-y) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics

et al. 2018

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“…Moreover, while some tractography methods do include anatomical priors in the formulation (e.g. [26,54]), in practice, the resulting tractograms usually contain false positives.…”

Section: Inclusion and Exclusion Roismentioning

confidence: 99%

Challenges for Tractogram Filtering

Jörgens

Descoteaux

Moreno

2021

Mathematics and Visualization

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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 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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“…MAGNET does not generate artificial directions but instead, selects diffusion orientations present in the underlying data. Additionally, integrating priors is most commonly done at multiple levels during tractography (e.g., tracking in WM masks, stopping in the GM, refraining from entering the ventricles, starting at the interface between WM/GM, adding ROIs to select streamlines) [Girard et al, 2014;Kasenburg et al, 2016;Smith et al, 2012]. Connectomes built from pairs of ROIs and atlases also represent an integration of prior knowledge where connectivity is directly imposed between cortical parcels.…”

Section: Inference Of Connectivitymentioning

confidence: 99%

Active delineation of Meyer's loop using oriented priors through MAGNEtic tractography (MAGNET)

Chamberland

Scherrer

Prabhu

et al. 2016

Human Brain Mapping

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Streamline tractography algorithms infer connectivity from diffusion MRI (dMRI) by following diffusion directions which are similarly aligned between neighbouring voxels. However, not all white matter (WM) fascicles are organized in this manner. For example, Meyer’s loop is a highly curved portion of the optic radiation (OR) that exhibits a narrow turn, kissing and crossing pathways, and changes in fascicle dispersion. From a neurosurgical perspective, damage to Meyer’s loop carries a potential risk of inducing vision deficits to the patient, especially during temporal lobe resection surgery. To prevent such impairment, achieving an accurate delineation of Meyer’s loop with tractography is thus of utmost importance. However, current algorithms tend to under-estimate the full extent of Meyer’s loop, mainly attributed to the aforementioned rule for connectivity which requires a direction to be chosen across a field of orientations. In this article, we demonstrate that MAGNEtic Tractography (MAGNET) can benefit Meyer’s loop delineation by incorporating anatomical knowledge of the expected fiber orientation to overcome local ambiguities. We propose a new ROI-mechanism which supplies additional information to streamline reconstruction algorithms by the means of oriented priors. Our results show that MAGNET can accurately generate Meyer’s loop in all of our 15 child subjects (8 males; mean age 10.2 years ± 3.1). It effectively improved streamline coverage when compared with deterministic tractography, and significantly reduced the distance between the anterior-most portion of Meyer’s loop and the temporal pole by 16.7 mm on average, a crucial landmark used for preoperative planning of temporal lobe surgery.

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Training shortest-path tractography: Automatic learning of spatial priors

Cited by 11 publications

References 41 publications

Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics

Diffusion MRI-based cortical connectome reconstruction: dependency on tractography procedures and neuroanatomical characteristics

Challenges for Tractogram Filtering

Active delineation of Meyer's loop using oriented priors through MAGNEtic tractography (MAGNET)

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