Evidence map›Paper›PMID 32584867›Full record

ArticlePloS one2020

A super-resolution scanning algorithm for lensless microfluidic imaging using the dual-line array image sensor.

Dian Tian, Ningmei Yu, Zhengpeng Li, Shuaijun Li, Na Li

Abstract read
In one paragraph

Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Dian TianSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi Province, China.ORCID 0000-0002-8626-0561
Ningmei YuSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi Province, China.ORCID 0000-0002-4219-8364
Zhengpeng LiSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi Province, China.
Shuaijun LiSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi Province, China.
Na LiSchool of Automation and Information Engineering, Xi'an University of Technology, Xi'an, Shaanxi Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The lensless optical fluid microscopy is of great significance to the miniaturization, portability and low cost development of cell detection instruments. However, the resolution of the cell image collected directly is low, because the physical pixel size of the image sensor is the same order of magnitude as the cell size. To solve this problem, this paper proposes a super-resolution scanning algorithm using a dual-line array sensor and a microfluidic chip. For dual-line array sensor images, the multi-group velocity and acceleration of cells flowing through the line array sensor are calculated. Then the reconstruction model of the super-resolution image is constructed with variable acceleration. By changing the angle between the line array image sensor and the direction of cell flow, the super-resolution image scanning and reconstruction are achieved in both horizontal and vertical directions. In addition, it is necessary to study the row by row extraction algorithm for cell foreground image. In this paper, the dual-line array sensor is implemented by adjusting the acquisition window of the image sensor with a pixel size of 2.2μm. When the tilt angle is 21 degrees, the equivalent pixel size is 0.79μm, improved 2.8 times, and after de-diffraction its average size error was 3.249%. As the angle decreases, the image resolution is higher, but the amount of information is less. This super-resolution scanning algorithm can be integrated on the chip and used with a microfluidic chip to realize on-chip instrument.

Indexed as

AlgorithmsCell Culture TechniquesImage Processing, Computer-AssistedLab-On-A-Chip DevicesMicroscopyAnimalsHumans

Identifiers

PMID32584867
PMCPMC7316336

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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.