Evidence map›Paper›PMID 41438683›Full record

ArticleInformation processing in medical imaging : proceedings of the ... conference2026

Brightness-Invariant Tracking Estimation in Tagged MRI.

Zhangxing Bian, Shuwen Wei, Xiao Liang, Yuan-Chiao Lu, Samuel W Remedios, Fangxu Xing, Jonghye Woo, Dzung L Pham, Aaron Carass, Philip V Bayly and 3 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Information processing in medical imaging : proceedings of the ... conference, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Multimodal characterization of intracranial biomechanics in a 3D biofidelic head surrogate.Journal of the mechanical behavior of biomedical materials · 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

13 authors.

Zhangxing BianJohns Hopkins University, Baltimore, MD, USA.
Shuwen WeiJohns Hopkins University, Baltimore, MD, USA.
Xiao LiangUniversity of Maryland School of Medicine, Baltimore, MD, USA.
Yuan-Chiao LuUniformed Services University of the Health Sciences, Bethesda, MD, USA.
Samuel W RemediosJohns Hopkins University, Baltimore, MD, USA.
Fangxu XingMassachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Jonghye WooMassachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
Dzung L PhamJohns Hopkins University, Baltimore, MD, USA.
Aaron CarassJohns Hopkins University, Baltimore, MD, USA.
Philip V BaylyWashington University, St. Louis, MO, USA.
Jiachen ZhuoUniversity of Maryland School of Medicine, Baltimore, MD, USA.
Ahmed AlshareefUniversity of South Carolina, Columbia, SC, USA.
Jerry L PrinceJohns Hopkins University, Baltimore, MD, USA.

Funding

MRI Measurement of the Mechanical Vulnerability of the BrainU01NS112120 · NINDS · WASHINGTON UNIVERSITY · PI BAYLY, PHILIP V · 2019 to 2023
$3.7M
A holistic approach to identifying functional units of tongue motion during speechR01DC018511 · NIDCD · MASSACHUSETTS GENERAL HOSPITAL · PI WOO, JONGHYE · 2020 to 2024
$2.6M
Biomechanics of the Human Brain During High-Severity Impacts: A Multimodal ApproachR01NS136056 · NINDS · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI Ahmed Alshareef, Matthew Brian Panzer · 2025 to 2026
$1.1M
NIDCD NIH HHS R01 DC018511NINDS NIH HHS R01 NS136056NINDS NIH HHS U01 NS112120
6 · The paper itself

Abstract

Magnetic resonance (MR) tagging is an imaging technique for noninvasively tracking tissue motion in vivo by creating a visible pattern of magnetization saturation (tags) that deforms with the tissue. Due to longitudinal relaxation and progression to steady-state, the tags and tissue brightnesses change over time, which makes tracking with optical flow methods error-prone. Although Fourier methods can alleviate these problems, they are also sensitive to brightness changes as well as spectral spreading due to motion. To address these problems, we introduce the brightness-invariant tracking estimation (BRITE) technique for tagged MRI. BRITE disentangles the anatomy from the tag pattern in the observed tagged image sequence and simultaneously estimates the Lagrangian motion. The inherent ill-posedness of this problem is addressed by leveraging the expressive power of denoising diffusion probabilistic models to represent the probabilistic distribution of the underlying anatomy and the flexibility of physics-informed neural networks to estimate biologically-plausible motion. A set of tagged MR images of a gel phantom was acquired with various tag periods and imaging flip angles to demonstrate the impact of brightness variations and to validate our method. The results show that BRITE achieves more accurate motion and strain estimates as compared to other state of the art methods, while also being resistant to tag fading.

Indexed as

Motion trackingMR taggingSpectral overlapStrain

Identifiers

PMID41438683
PMCPMC12720491

What OpenQuestion holds

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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.