Abstract
A major limitation of diffusion MRI tractography is its tendency to reconstruct anatomically implausible fiber trajectories. To address this issue, the integration of anatomical constraints has been suggested. In this work, we present a novel adaptive viral tracing informed tractography (AdaViT) approach. AdaViT integrates information from viral tracing experiments from the Allen Mouse Brain Connectivity Atlas to add more global context to local tractography. Starting from a list of tracking masks estimated from viral tracing projection density maps, AdaViT dynamically updates the effective tracking mask based on the previous positions of the streamline being reconstructed. This way, AdaViT constrains streamline reconstruction to regions that have greater chances of resulting in an anatomically valid connection. We first validate AdaViT on the diffusion-simulated connectivity (DiSCo) dataset and show that AdaViT reduces the occurrence of invalid connections while achieving good agreement with ground truth connectivity. We also apply AdaViT on a 50-$\mu m$ isotropic resolution ex vivo mouse brain to disentangle a major bottleneck region located across the bridge of the corpus callosum. By integrating more global context into the local tractography algorithm, we show that AdaViT reduces the occurrence of invalid streamlines and increases the proportion of valid connections.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/cdmri_007.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=wlHUlCO3AJ
BibTex
@InProceedings{PoiCha_AdaViT_MICCAISAT2026,
author = { Poirier, Charles AND Petit, Laurent AND Lefebvre, Joël AND Descoteaux, Maxime},
title = { { AdaViT: Adaptive Viral Tracing Informed Tractography } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17278},
month = {pending},
page = {pending}
}