Evidence map›Paper›PMID 32075006›Full record

ArticleSensors (Basel, Switzerland)2020

Rapid High-Resolution Mosaic Acquisition for Photoacoustic Remote Sensing.

Saad Abbasi, Kevan Bell, Parsin Haji Reza

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 46% 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

4 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Review
  3. Article
  4. 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

3 authors at 1 institution in 1 country.

Saad AbbasiPhotoMedicine Labs, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Kevan BellPhotoMedicine Labs, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.
Parsin Haji RezaPhotoMedicine Labs, Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.ORCID 0000-0002-4928-6244
University of Waterloo · CA

Funding

Canada Foundation for Innovation JELF #38000Centre for Bioengineering and Biotechnology CBB Seed fundillumiSonics SRA #083181Mitacs IT13594Natural Sciences and Engineering Research Council of Canada DGECR-2019-00143, RGPIN- 2019-06134
6 · The paper itself

Abstract

Mechanical stages are routinely used to scan large expanses of biological specimens in photoacoustic imaging. This is primarily due to the limited field of view (FOV) provided by optical scanning. However, stage scanning becomes impractical at higher scanning speeds, or potentially unfeasible with heavier samples. Also, the slow scan-rate of the stages makes high resolution scanning a time-consuming process. Some clinical applications such as microsurgery require submicron resolution in a reflection-mode configuration necessitating a method that can acquire large field of views with a small raster scanning step size. In this study, we describe a method that combines mechanical stages with optical scanning for the rapid acquisition of high-resolution large FOVs. Optical scanning is used to acquire small frames in a two-dimensional grid formed by the mechanical stages. These frames are captured with specific overlap for effective image registration. Using a step size of 200 nm, we demonstrate mosaics of carbon fiber networks with FOVs of 0.8 × 0.8 mm

Indexed as

Photoacoustic TechniquesRemote Sensing TechnologyBreastCarbon FiberFemaleHumansImaging, Three-DimensionalSignal Processing, Computer-AssistedTime FactorsCarbon FibermosaicPARSphotoacoustic

Identifiers

PMID32075006
PMCPMC7071083
OpenAlexW3005819879

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.