Challenges for Tractogram Filtering

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 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.

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

“…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].…”

“…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.…”

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

“…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.…”

“…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.…”