Evidence map›Paper›PMID 33163358›Full record

ReviewPhotoacoustics2020

Photoacoustic imaging with fiber optic technology: A review.

Jingcheng Zhou, Jesse V Jokerst

Abstract readReview
In one paragraph

Review in Photoacoustics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 1 pooled it
–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

38 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
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  5. Review
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  9. Review
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  14. Article
  15. Article
  16. Speckle-correlation imaging through a kaleidoscopic multimode fiber.Proceedings of the National Academy of Sciences of the United States of America · 2023
    Article
  17. Article
  18. Review
  19. Review
  20. 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

2 authors.

Jingcheng ZhouDepartment of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92092, USA.
Jesse V JokerstDepartment of NanoEngineering, University of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92092, USA.

Funding

Therapeutic Drug Monitoring with a Wearable Ultrasound-based SensorDP2HL137187 · NHLBI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JOKERST, JESSE VINCENT · 2016 to 2019
$2.7M
Non-Invasive Detection and Staging of Decubitus and Diabetic UlcersR21AG065776 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JOKERST, JESSE VINCENT · 2020 to 2021
$575k
A Miniaturized Tool for Ultrasound Quantification of Periodontal DiseaseR21DE029025 · NIDCR · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JOKERST, JESSE VINCENT · 2019 to 2020
$409k
NHLBI NIH HHS DP2 HL137187NIA NIH HHS R21 AG065776NIDCR NIH HHS R21 DE029025
6 · The paper itself

Abstract

Photoacoustic imaging (PAI) has achieved remarkable growth in the past few decades since it takes advantage of both optical and ultrasound (US) imaging. In order to better promote the wide clinical applications of PAI, many miniaturized and portable PAI systems have recently been proposed. Most of these systems utilize fiber optic technologies. Here, we overview the fiber optic technologies used in PAI. This paper discusses three different fiber optic technologies: fiber optic light transmission, fiber optic US transmission, and fiber optic US detection. These fiber optic technologies are analyzed in different PAI modalities including photoacoustic microscopy (PAM), photoacoustic computed tomography (PACT), and minimally invasive photoacoustic imaging (MIPAI).

Indexed as

BiosensorsFiber opticPhotoacoustic imagingUltrasound

Identifiers

PMID33163358
PMCPMC7606844

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.