Evidence map›Paper›PMID 41445918›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2025

Approximate diffusion tractography from FLAIR MRI and anatomical context using recurrent neural networks.

Zhiyuan Li, Michael E Kim, Tian Yu, Praitayini Kanakaraj, Tianyuan Yao, Chenyu Gao, Susan M Resnick, Lori L Beason-Held, Mohamad Habes, Leon Y Cai and 1 more

Abstract read
In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Modeling a Shared Reality of Tractography through Varied Structural Imaging.Proceedings of SPIE--the International Society for Optical Engineering · 2026
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Zhiyuan LiDept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Michael E KimDept. of Computer Science, Vanderbilt University, Nashville, TN, USA.
Tian YuDept. of Computer Science, Vanderbilt University, Nashville, TN, USA.
Praitayini KanakarajDept. of Computer Science, Vanderbilt University, Nashville, TN, USA.
Tianyuan YaoDept. of Computer Science, Vanderbilt University, Nashville, TN, USA.
Chenyu GaoDept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.
Susan M ResnickLaboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA.
Lori L Beason-HeldLaboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD, USA.
Mohamad HabesNeuroimage Analytics Laboratory and Glenn Biggs Institute Neuroimaging Core, Glenn Biggs Institute for Neurodegenerative Diseases, University of Texas Health San Antonio, San Antonio, TX, USA.
Leon Y CaiDept. of Internal Medicine, Johns Hopkins Bayview Medical Center, Baltimore, MD, USA.
Bennett A LandmanDept. of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA.

Funding

Vanderbilt Institute for Clinical and Translational Research (VICTR) -Identifying correlates of functional immunity in SARS-CoV-2 convalescent plasmaUL1TR002243 · NCATS · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Paul A. Harris, Wesley H Self · 2017 to 2026
$130.7M
MVP Data Integration into the ADSP Phenotype Harmonization ConsortiumU24AG074855 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI CUCCARO, MICHAEL L, HOHMAN, TIMOTHY J · 2021 to 2025
$37.5M
Ultrascale Machine Learning to Empower Discovery in Alzheimers Disease BiobanksU01AG068057 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI Christos Davatzikos, Heng Huang · 2020 to 2026
$20.7M
Overall: Eunice Kennedy Shriver Intellectual and Developmental Disabilities Research Center at VanderbiltP50HD103537 · NICHD · VANDERBILT UNIVERSITY MEDICAL CENTER · PI Jeffrey L Neul · 2020 to 2026
$10.3M
Cerebral tau deposition and comorbid cerebrovascular disease across the Alzheimer's disease continuum in Mexican AmericansR01AG085571 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI GONZALES, MITZI MICHELLE, HABES, MOHAMAD · 2024 to 2025
$5.7M
Controlling Quality and Capturing Uncertainty in Advanced Diffusion Weighted MRIR01EB017230 · NIBIB · VANDERBILT UNIVERSITY · PI LANDMAN, BENNETT A. · 2015 to 2024
$5.1M
Genetic Drivers of Resilience to Alzheimer's DiseaseR01AG059716 · NIA · VANDERBILT UNIVERSITY MEDICAL CENTER · PI HOHMAN, TIMOTHY J · 2018 to 2022
$4.4M
Multiethnic machine learning brain signatures of ADRDR01AG080821 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI HABES, MOHAMAD · 2022 to 2025
$2.9M
Vascular Imaging Biomarker Relationships to Alzheimer’s disease (VIBRA)R01AG083865 · NIA · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · PI Mohamad Habes, Timothy M. Hughes · 2024 to 2026
$2.3M
High-Performance Computer Cluster for Biomedical ResearchS10OD023680 · OD · VANDERBILT UNIVERSITY · PI MEILER, JENS · 2017 to 2017
$587k
NCATS NIH HHS UL1 TR002243NIA NIH HHS R01 AG059716NIA NIH HHS R01 AG080821NIA NIH HHS R01 AG083865NIA NIH HHS R01 AG085571NIA NIH HHS U01 AG068057NIA NIH HHS U24 AG074855NIBIB NIH HHS R01 EB017230NICHD NIH HHS P50 HD103537NIH HHS S10 OD023680
6 · The paper itself

Abstract

Diffusion MRI (dMRI) tractography methods provide a valuable method for in-vivo estimation of whole-brain white matter pathways that are commonly assumed to rely on microstructure models derived from dMRI. However, recent pioneering works have demonstrated that the accuracy of white-matter measurements computed from T1-weighted (T1w) MRI tractography is on a similar level to scan-rescan variability in dMRI tractography. This revelation raises new questions about understanding tractography: Is it primarily a dMRI microstructural phenomenon, and how different can it be when estimated from other imaging modalities? In this study, we propose a framework to approximate tractography from fluid-attenuated inversion recovery (FLAIR) MRI and examine its performance compared to tractography based on diffusion and T1w MRI. We adapt the teacher-student recurrent neural network (RNN) model from existing work on T1w tractography. Additionally, we use brain segmentation maps as the anatomical context. We conduct white matter bundle analysis and compare various metrics with those from T1w tractography and the traditional dMRI tractography. FLAIR tractography achieved significant different performance compared to T1 tractography evaluated by Dice similarity coefficient (p=0.004) and bundle adjacency streamlines distance (p=0.012). An average absolute difference of 23% was observed in eight bundle shape measurements between FLAIR tractography and traditional dMRI tractography. Both qualitative and quantitative results suggest that tractography based on FLAIR MRI is feasible and underscore the need for comprehensive research to understand tractography in the broader context of multi-modality brain MRI.

Indexed as

diffusion MRIFLAIR MRIrecurrent neural networksTractographywhite matter bundles

Identifiers

PMID41445918
PMCPMC12726966

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.