Challenges for Tractogram Filtering

Jörgens, Daniel; Descoteaux, Maxime; Moreno, Rodrigo

doi:10.1007/978-3-030-56215-1_7

Cited by 2 publications

(4 citation statements)

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“…Tractogram filtering approaches remove streamlines from tractograms by evaluating their plausibility, for example, by considering the geometrical properties of streamlines (e.g., [12]), anatomical constraints (e.g., [13]), through clustering approaches (e.g., [14]) or by correspondence to the underlying DW-MRI data (e.g., [15]). A review of methods is presented in [16].…”

Section: Introductionmentioning

confidence: 99%

“…One popular tractogram filtering method is Spherical-deconvolution Informed Filtering of Tractograms (SIFT) [15]. This approach belongs to the family of methods that assess tractogram quality by comparing the acquired DW-MRI data to the expected one from the tractogram [16]. In particular, SIFT removes streamlines to increase the consistency of the tractogram with respect to the acquired data based on a global optimization approach.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering

Hain¹,

Jörgens²,

Moreno³

2022

Preprint

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Tractography has become an indispensable part of brain connectivity studies. However, it is currently facing problems with reliability. In particular, a substantial amount of nerve fiber reconstructions (streamlines) in tractograms produced by state-of-the-art tractography methods are anatomically implausible. To address this problem, tractogram filtering methods have been developed to remove faulty connections in a postprocessing step. This study takes a closer look at one such method, Spherical-deconvolution Informed Filtering of Tractograms (SIFT), which uses a global optimization approach to improve the agreement between the remaining streamlines after filtering and the underlying diffusion magnetic resonance imaging data. SIFT is not suitable to judge the plausibility of individual streamlines since its results depend on the size and composition of the surrounding tractogram. To tackle this problem, we propose applying SIFT to randomly selected tractogram subsets in order to retrieve multiple assessments for each streamline. This approach makes it possible to identify streamlines with very consistent filtering results, which were used as pseudo ground truths for training classifiers. The trained classifier is able to distinguish the obtained groups of plausible and implausible streamlines with accuracy above 80%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering

Hain¹,

Jörgens²,

Moreno³

2022

Preprint

Self Cite

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“…A number of tractogram filtering methods were proposed in the literature (Jörgens et al, 2021 ). The majority of approaches involve selecting the most relevant fibers based on a quality measure.…”

Section: Introductionmentioning

confidence: 99%

“…This quality metric can be defined by the mean values of a diffusion metric along the fiber path (Yeh et al, 2021 ). This diffusion metric can be obtained from the diffusion tensor model (e.g., FA, axial, and radial diffusivities) (Everts et al, 2009 ), or from more sophisticated models such as constrained spherical deconvolution (CSD) (Smith et al, 2013 , 2015 ) or other methods combining multiple approaches (Jörgens et al, 2021 ). Once the quality measure is defined, one way to filter tractograms consists of applying thresholds on the quality measure to filter out weak connections.…”

Section: Introductionmentioning

confidence: 99%

Automation of Cranial Nerve Tractography by Filtering Tractograms for Skull Base Surgery

Decroocq

Ligneris

Poquillon

et al. 2022

Front. Neuroimaging

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Fiber tractography enables the in vivo reconstruction of white matter fibers in 3 dimensions using data collected by diffusion tensor imaging, thereby helping to understand functional neuroanatomy. In a pre-operative context, it provides essential information on the trajectory of fiber bundles of medical interest, such as cranial nerves. However, the optimization of tractography parameters is a time-consuming process and requires expert neuroanatomical knowledge, making the use of tractography difficult in clinical routine. Tractogram filtering is a method used to isolate the most relevant fibers. In this work, we propose to use filtering as a post-processing of tractography to avoid the manual optimization of tracking parameters and therefore making a step forward automation of tractography. To question the feasibility of automated tractography of cranial nerves, we perform an analysis of main cranial nerves on a series of patients with skull base tumors. A quantitative evaluation of the filtering performance of two state-of-the-art and a new entropy-based methods is carried out on the basis of reference tractograms produced by experts. Our approach proves to be more stable in the selection of the optimal filtering threshold and turns out to be interesting in terms of computational time complexity.

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Challenges for Tractogram Filtering

Cited by 2 publications

References 73 publications

Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering

Assessing Streamline Plausibility Through Randomized Iterative Spherical-Deconvolution Informed Tractogram Filtering

Automation of Cranial Nerve Tractography by Filtering Tractograms for Skull Base Surgery

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