Evidence map›Paper›PMID 31527505›Full record

ArticleSensors (Basel, Switzerland)2019

Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy.

Zhiqiang Xu, Yiming Wang, Naidi Sun, Zhengying Li, Song Hu, Quan Liu

Open access · goldAbstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. 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
0.5field-weighted citation impact, top 37% 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, 10 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Recovery of photoacoustic images based on accurate ultrasound positioning.Visual computing for industry, biomedicine, and art · 2021
    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

6 authors at 2 institutions in 2 countries.

Zhiqiang XuSchool of Information Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China. xuzhiqiang01@whut.edu.cn.ORCID 0000-0001-7740-8360
Yiming WangSchool of Information Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China. yw6q@virginia.edu.
Naidi SunDepartment of Biomedical Engineering, University of Virginia, 415 Lane Road, Charlottesville, VA 22908, USA. ns3gs@virginia.edu.
Zhengying LiSchool of Information Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China. zhyli@whut.edu.cn.ORCID 0000-0002-4600-2795
Song HuDepartment of Biomedical Engineering, University of Virginia, 415 Lane Road, Charlottesville, VA 22908, USA. songhu@virginia.edu.ORCID 0000-0002-5760-8012
Quan LiuSchool of Information Engineering, Wuhan University of Technology, 122 Luoshi Road, Wuhan 430070, China. quanliu@whut.edu.cn.
Wuhan University of Technology · CNUniversity of Virginia · US

Funding

Photoacoustic Microscopy of the Awake Mouse BrainR01NS099261 · NINDS · WASHINGTON UNIVERSITY · PI HU, SONG · 2017 to 2021
$1.7M
American Heart Association 15SDG25960005China Scholarship Council 201706950020Fundamental Research Funds for the Central Universities 2016-JL-010NIH HHS NS099261NINDS NIH HHS R01 NS099261
6 · The paper itself

Abstract

Photoacoustic microscopy (PAM) is an emerging biomedical imaging technology capable of quantitative measurement of the microvascular blood flow by correlation analysis. However, the computational cost is high, limiting its applications. Here, we report a parallel computation design based on graphics processing unit (GPU) for high-speed quantification of blood flow in PAM. Two strategies were utilized to improve the computational efficiency. First, the correlation method in the algorithm was optimized to avoid redundant computation and a parallel computing structure was designed. Second, the parallel design was realized on GPU and optimized by maximizing the utilization of computing resource in GPU. The detailed timings and speedup for each calculation step were given and the MATLAB and C/C++ code versions based on CPU were presented as a comparison. Full performance test shows that a stable speedup of ~80-fold could be achieved with the same calculation accuracy and the computation time could be reduced from minutes to just several seconds with the imaging size ranging from 1 × 1 mm

Indexed as

Imaging, Three-DimensionalMicroscopyPhotoacoustic TechniquesAlgorithmsAnimalsComputer GraphicsMaleMiceRegional Blood Flowblood flowcorrelation analysisGPUparallel computingphotoacoustic microscopy

Identifiers

PMID31527505
PMCPMC6767147
OpenAlexW2972483404

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.