Evidence map›Paper›PMID 41364977›Full record

ArticlePhysics in medicine and biology2026

Band-limited implicit neural representations for diffusion-weighted imaging denoising.

Yunxiang Li, Yan Dai, Yen-Peng Liao, Jie Deng, You Zhang

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Yunxiang LiDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.ORCID 0000-0003-0622-4710
Yan DaiDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.ORCID 0000-0002-6285-3194
Yen-Peng LiaoDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.ORCID 0000-0001-5721-0357
Jie DengDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.ORCID 0000-0002-2518-7989
You ZhangDepartment of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.ORCID 0000-0002-8033-2755

Funding

Artificial Intelligence-Based Quality Assurance for Online Adaptive RadiotherapyR01CA258987 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Steve Bin Jiang, Dan Nguyen · 2022 to 2026
$2.8M
High-Precision Proton Therapy for Liver Cancer: Developing an End-to-end Strategy with Real-time Liver Tumor Localization and On-the-fly Plan Delivery AdaptationR01CA280135 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Weiguo Lu, You Zhang · 2024 to 2026
$1.8M
High-Precision Lung Radiotherapy by Intra-treatment Dynamic Cone-beam CT imaging and Dosimetry-guided Plan AdaptationR01EB034691 · NIBIB · UT SOUTHWESTERN MEDICAL CENTER · PI Weiguo Lu, You Zhang · 2024 to 2026
$1.5M
Accurate 4D Liver Tumor Localization for Radiotherapy using Contrast-Agent-Free X-ray Imaging and Liver Biomechanical ModelingR01CA240808 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI ZHANG, YOU · 2020 to 2024
$1.5M
NCI NIH HHS R01 CA240808NCI NIH HHS R01 CA258987NCI NIH HHS R01 CA280135NIBIB NIH HHS R01 EB034691
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Diffusion Magnetic Resonance ImagingImage Processing, Computer-AssistedSignal-To-Noise RatioHumansPhantoms, Imagingdenoisingdiffusion-weighted imagingimplicit neural representationself-supervised learning

Identifiers

PMID41364977
PMCPMC12780490

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.