Evidence map›Paper›PMID 33563996›Full record

ArticleNature communications2021

High-speed three-dimensional photoacoustic computed tomography for preclinical research and clinical translation.

Li Lin, Peng Hu, Xin Tong, Shuai Na, Rui Cao, Xiaoyun Yuan, David C Garrett, Junhui Shi, Konstantin Maslov, Lihong V Wang

Open access · goldAbstract read
In one paragraph

Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.

0numbers the graph read from it
0cells of the map it votes in
100citing papers in PubMed
11.6field-weighted citation impact, top 1% of its field
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

100 citing papers in PubMed, 192 citations in OpenAlex.

  1. Article
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  10. Volumetric FLASH dosimetryPhysics in medicine and biology · 2026
    Article
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40 more citing papers are in PubMed but not listed here.

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

10 authors at 1 institution in 2 countries.

Li Lin *Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-0517-8436
Peng Hu *Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-2933-1239
Xin Tong *Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.
Shuai Na *Caltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.
Rui CaoCaltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.
Xiaoyun YuanCaltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-7914-3658
David C GarrettCaltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.
Junhui ShiCaltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-5741-2781
Konstantin MaslovCaltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.
Lihong V WangCaltech Optical Imaging Laboratory, Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA. LVW@caltech.edu.ORCID http://orcid.org/0000-0001-9783-4383
California Institute of Technology · US

Funding

High-speed panoramic 3D photoacoustic computed tomography of the breastR35CA220436 · NCI · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI WANG, LIHONG · 2018 to 2024
$6.4M
IN VIVO IMAGING OF SINGLE CIRCULATING CELLSR01CA186567 · NCI · WASHINGTON UNIVERSITY · PI WANG, LIHONG · 2013 to 2017
$3.6M
Deep brain photoacoustic tomography at single-neuron resolution using arrays of photonic emitters and high-frequency ultrasound transducersU01NS099717 · NINDS · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI ROUKES, MICHAEL L, SHEPARD, KENNETH L · 2016 to 2018
$3.0M
NCI NIH HHS R01 CA186567NCI NIH HHS R35 CA220436NINDS NIH HHS U01 NS099717
6 · The paper itself

Abstract

Photoacoustic computed tomography (PACT) has generated increasing interest for uses in preclinical research and clinical translation. However, the imaging depth, speed, and quality of existing PACT systems have previously limited the potential applications of this technology. To overcome these issues, we developed a three-dimensional photoacoustic computed tomography (3D-PACT) system that features large imaging depth, scalable field of view with isotropic spatial resolution, high imaging speed, and superior image quality. 3D-PACT allows for multipurpose imaging to reveal detailed angiographic information in biological tissues ranging from the rodent brain to the human breast. In the rat brain, we visualize whole brain vasculatures and hemodynamics. In the human breast, an in vivo imaging depth of 4 cm is achieved by scanning the breast within a single breath hold of 10 s. Here, we introduce the 3D-PACT system to provide a unique tool for preclinical research and an appealing prototype for clinical translation.

Indexed as

AngiographyAnimalsBrainBreastEquipment DesignFemaleFunctional NeuroimagingHumansImaging, Three-DimensionalPhotoacoustic TechniquesRatsTomography, X-Ray Computed

Identifiers

PMID33563996
PMCPMC7873071
OpenAlexW3126411932

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.