Evidence map›Paper›PMID 38471184›Full record

ArticlePhysics in medicine and biology2024

Hybrid-supervised deep learning for domain transfer 3D protoacoustic image reconstruction.

Yankun Lang, Zhuoran Jiang, Leshan Sun, Liangzhong Xiang, Lei Ren

Abstract read
In one paragraph

Article in Physics in medicine and biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing 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

13 citing papers in PubMed.

  1. Review
  2. Article
  3. Volumetric FLASH dosimetryPhysics in medicine and biology · 2026
    Article
  4. Article
  5. Review
  6. Automatic specific absorption rate (SAR) prediction for hyperthermia treatment planning using deep learning method.International journal of hyperthermia : the official journal of European Society for Hyperthermic Oncology, North American Hyperthermia Group · 2025
    Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. 4DPhysics in medicine and biology · 2024
    Article
  12. Article
  13. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Yankun LangDepartment of Radiation Oncology Physics, University of Maryland, Baltimore, Baltimore, MD 21201, United States of America.
Zhuoran JiangDepartment of Radiation Oncology, Duke University, Durham, NC 27710, United States of America.
Leshan SunDepartment of Biomedical Engineering and Radiology, University of California, Irvine, Irnive, CA, 92617, United States of America.
Liangzhong XiangDepartment of Biomedical Engineering and Radiology, University of California, Irvine, Irnive, CA, 92617, United States of America.
Lei RenDepartment of Radiation Oncology Physics, University of Maryland, Baltimore, Baltimore, MD 21201, United States of America.

Funding

eXtended Modular ANthropomorphic (XMAN) phantom for Imaging and Treatment Optimization in Radiotherapy.R01EB032680 · NIBIB · UNIVERSITY OF MARYLAND BALTIMORE · PI REN, LEI, YANG, XIAOFENG · 2022 to 2025
$2.4M
Hybrid virtual-MRI/CBCT: A new paradigm for image guidance in liver SBRTR01EB028324 · NIBIB · UNIVERSITY OF MARYLAND BALTIMORE · PI REN, LEI, YIN, FANG-FANG · 2019 to 2022
$2.1M
3-dimensional prompt gamma imaging for online proton beam dose verificationR01CA279013 · NCI · UNIVERSITY OF MARYLAND BALTIMORE · PI Jerimy C. Polf, Lei Ren · 2023 to 2026
$1.9M
NCI NIH HHS R01 CA279013NIBIB NIH HHS R01 EB028324NIBIB NIH HHS R01 EB032680
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Deep LearningProton TherapyHumansImage Processing, Computer-AssistedImaging, Three-DimensionalMaleProstateProtonsProtonsdose verificationprotoacoustic reconstructionproton therapyself-supervised deep learning

Identifiers

PMID38471184
PMCPMC11076107

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.