Separating mixtures of single-fiber orientation distributions for increased sensitivity to crossing white matter fibers
Abstract
Intro: Diffusion MRI allows for in vivo probing of brain tissue architecture via micron-level diffusion aggregated at the millimeter scale, but crossing white matter fibers decrease the sensitivity of downstream tasks such as microstructural modeling and tractography. Current fixel-based methods for identifying the number, orientation, and spread of fibers rely on finding the peaks of fiber orientation distributions from constrained spherical deconvolution. However, this method is biased by the spreads of nearby fibers and fails at angular separations lower than approximately 40-50 degrees. Methods: In this work, we perform a constrained nonlinear optimization called fiber segmentation by signal splitting and iterative least squares estimation (FISSILE), modeling each voxel as a mixture of single-fiber distributions. We use FISSILE to fit axially symmetric fibers in simulations and on three in vivo human subjects from the Human Connectome Project, an ex vivo raccoon brain, as well as the same human subject scanned twice at two different signal-to-noise ratios (SNRs). Results: On simulated data, FISSILE successfully identifies fibers at an angular separation of 40° and a volume fraction of 0.1 at an SNR as low as 30, while MRtrix3's fod2fixel is limited to 60° separation and 0.1 volume fraction at the same SNR. On acquired data, FISSILE identified 85.8% of white matter voxels on average in human brains and 79.4% in the raccoon with at least one fiber crossing, whereas fod2fixel identified 30.1% and 44.5%. The number of white matter fiber crossings was mildly sensitive to different noise levels in the human subject with two acquisitions. Conclusion: A method that models the crossing fiber phenomenon as an unmixing problem increases sensitivity to individual fibers for more precise characterization of anatomy.
Related
- Ab initio CryoEM maps minimizing preferred orientation problems in heterogeneous mixtures: ReconSIREN
- Reduced baseline male fertility and increased sensitivity to hypoxic stress in high-elevation deer mice
- Higher rewards lead to more accurate flower detection and increased contrast sensitivity in the bumblebee Bombus terrestris
- A rank-conditioned α-stable perturbation for differential evolution: Separating perturbation frequency and tail heaviness
- Separating benchmark discrimination from experimental retrieval in synthetic-lethality prediction
BibTeX
@misc{saunders2026separating,
title = {{Separating mixtures of single-fiber orientation distributions for increased sensitivity to crossing white matter fibers}},
author = {Adam M. Saunders and Gaurav Rudravaram and Michael E. Kim and Elyssa M. McMaster and Yifan Shuai and Warren Boschen and Sophie Lin and Suzana Herculano-Houzel and Daniel C. Colvin and Yurui Gao and Kurt G. Schilling and Adam W. Anderson and Bennett Landman},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.20974},
eprint = {2610.20974},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.20974},
}
RIS
TY - UNPB
TI - Separating mixtures of single-fiber orientation distributions for increased sensitivity to crossing white matter fibers
AU - Adam M. Saunders
AU - Gaurav Rudravaram
AU - Michael E. Kim
AU - Elyssa M. McMaster
AU - Yifan Shuai
AU - Warren Boschen
AU - Sophie Lin
AU - Suzana Herculano-Houzel
AU - Daniel C. Colvin
AU - Yurui Gao
AU - Kurt G. Schilling
AU - Adam W. Anderson
AU - Bennett Landman
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2610.20974
UR - https://arxiv.org/abs/2610.20974
AB - Intro: Diffusion MRI allows for in vivo probing of brain tissue architecture via micron-level diffusion aggregated at the millimeter scale, but crossing white matter fibers decrease the sensitivity of downstream tasks such as microstructural modeling and tractography. Current fixel-based methods for identifying the number, orientation, and spread of fibers rely on finding the peaks of fiber orientation distributions from constrained spherical deconvolution. However, this method is biased by the spreads of nearby fibers and fails at angular separations lower than approximately 40-50 degrees. Methods: In this work, we perform a constrained nonlinear optimization called fiber segmentation by signal splitting and iterative least squares estimation (FISSILE), modeling each voxel as a mixture of single-fiber distributions. We use FISSILE to fit axially symmetric fibers in simulations and on three in vivo human subjects from the Human Connectome Project, an ex vivo raccoon brain, as well as the same human subject scanned twice at two different signal-to-noise ratios (SNRs). Results: On simulated data, FISSILE successfully identifies fibers at an angular separation of 40° and a volume fraction of 0.1 at an SNR as low as 30, while MRtrix3's fod2fixel is limited to 60° separation and 0.1 volume fraction at the same SNR. On acquired data, FISSILE identified 85.8% of white matter voxels on average in human brains and 79.4% in the raccoon with at least one fiber crossing, whereas fod2fixel identified 30.1% and 44.5%. The number of white matter fiber crossings was mildly sensitive to different noise levels in the human subject with two acquisitions. Conclusion: A method that models the crossing fiber phenomenon as an unmixing problem increases sensitivity to individual fibers for more precise characterization of anatomy.
ER -