Evidence map›Paper›PMID 41126760›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026

A Hybrid Diffusion Model Enhances Multiparametric 3D Photoacoustic Computed Tomography.

Hyunsu Jeong, Seunghun Oh, Seongwook Choi, Jiwoong Kim, Jinge Yang, Chulhong Kim

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Article
  7. A Hybrid Diffusion Model Enhances Multiparametric 3D Photoacoustic Computed Tomography.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  8. Review
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

6 authors.

Hyunsu JeongGraduate School of Artificial Intelligence, Departments of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.ORCID https://orcid.org/0000-0002-7446-0478
Seunghun OhGraduate School of Artificial Intelligence, Departments of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.ORCID https://orcid.org/0009-0002-9395-1165
Seongwook ChoiDepartment of Radiology, Molecular Imaging Program at Stanford (MIPS), School of Medicine, Stanford University, California, 94034, USA.ORCID https://orcid.org/0000-0001-9774-058X
Jiwoong KimGraduate School of Artificial Intelligence, Departments of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
Jinge YangDepartments of Medical Engineering, California Institute of Technology, California, 91125, USA.
Chulhong KimGraduate School of Artificial Intelligence, Departments of Electrical Engineering, Convergence IT Engineering, Mechanical Engineering, Medical Science and Engineering and Medical Device Innovation Center, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.ORCID https://orcid.org/0000-0001-7249-1257

Funding

Basic Science Research Program, the National Research Foundation of Korea (NRF), Ministry of Education 2020R1A6A1A03047902Basic Science Research Program, the National Research Foundation of Korea (NRF), Ministry of Education RS-2024-00415450BK21 FOUR programCommercialization Promotion Agency for R&D Outcomes (COMPA), the Ministry of Science and ICT (MSIT) 2710006567Glocal 30 University ProjectInstitute of Information & communications Technology Planning & Evaluation (IITP), the Korea government(MSIT), Artificial Intelligence Graduate School Program RS-2019-II191906National Research Foundation of Korea (NRF), the Korea government(MSIT): 2023R1A2C3004880National Research Foundation of Korea (NRF), the Korea government(MSIT): 2024-00410679
6 · The paper itself

Abstract

Photoacoustic computed tomography (PACT) reveals biological structures, pharmacokinetics, and physiological functions. Although a premium PACT system with many ultrasound (US) transducers delivers high-quality volumetric imaging, it suffers from high system costs and slow temporal resolution. Here, using a limited number of US elements, a hybrid diffusion model (HD-PACT) is demonstrated that enhances dynamic multiparametric (structural, functional, and contrast-enhanced) 3D PACT. Using just 256 out of the 1024 elements in a premier hemispherical US array for PACT, HD-PACT improves structural images acquired in different planes, organisms, and wavelengths. In functional imaging, HD-PACT enables 256-element PACT to observe hypoxia, pharmacokinetics, and angiogenesis during tumor progression. Lastly, HD-PACT is transferable to low-end PACT (only 128 US elements), where it dynamically captures contrast-free/enhanced organs, oxygen-perturbed brains, and cardiac dynamics with high spatiotemporal resolution in live animals. It is believed that HD-PACT will be valuable in oncology, cardiology, pharmacology, and endocrinology.

Indexed as

Imaging, Three-DimensionalPhotoacoustic TechniquesTomography, X-Ray ComputedAnimalsDiffusionHumansMicedeep learningdiffusion modelmultiparametric imagingphotoacoustic computed tomographyphotoacoustic imaging

Identifiers

PMID41126760
PMCPMC12767131

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.