Validation of structure tensor analysis for orientation estimation in brain tissue microscopy

• Published in: Journal of neuroscience methods Vol. 423; p. 110539
• Main Authors: Gray, Bryson, Smith, Andrew W., MacKenzie-Graham, Allan, Shattuck, David W., Tward, Daniel J.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.11.2025
• Subjects: Anisotropy; Axon orientation; Brain - anatomy & histology; Brain - diagnostic imaging; Brain connectivity; Computer Simulation; Diffusion MRI; Diffusion Tensor Imaging - methods; Humans; Image Processing, Computer-Assisted - methods; Imaging, Three-Dimensional; Microscopy - methods; Structure tensor analysis; Tractography; Validation; White matter; White Matter - diagnostic imaging; Structure tensor analysis; Validation; Brain connectivity; Tractography; Axon orientation; Diffusion MRI; White matter
• ISSN: 0165-0270; 1872-678X; 1872-678X
• DOI: 10.1016/j.jneumeth.2025.110539

Accurate localization of white matter pathways using diffusion MRI is critical to investigating brain connectivity, but the accuracy of current methods is not thoroughly understood. A fruitful approach to validating accuracy is to consider microscopy data that have been co-registered with MRI of post mortem samples. In this setting, structure tensor analysis is a standard approach to computing local orientations. However, structure tensor analysis itself has not been well-validated and is subject to uncertainty in its angular resolution, and selectivity to specific spatial scales. Here, we conducted a simulation study to investigate the accuracy of using structure tensors to estimate the orientations of fibers arranged in configurations with and without crossings. We examined a range of simulated conditions, with a focus on investigating the method’s behavior on images with anisotropic resolution, which is particularly common in microscopy data acquisition. We also analyzed 2D and 3D optical microscopy data. Our results show that parameter choice in structure tensor analysis has relatively little effect on accuracy for estimating single orientations, although accuracy decreases with increasing resolution anisotropy. On the other hand, when estimating the orientations of crossing fibers, the choice of parameters becomes critical, and poor choices result in orientation estimates that are essentially random. This work provides a set of recommendations for researchers seeking to apply structure tensor analysis effectively in the study of axonal orientations in brain imaging data and quantifies the method’s limitations, particularly in the case of anisotropic data. •Structure tensor estimates of homogeneous orientations are robust to parameter choice.•Accuracy in crossing-fiber regions is highly sensitive to parameter choice.•Image resolution anisotropy has a large effect on accuracy in 3D images.•Simulations provide guidelines for choosing parameters based on image characteristics.