Evidence map›Paper›PMID 32152742›Full record

ArticleJournal of medical systems2020

Optimization Model for the Distribution of Fiducial Markers in Liver Intervention.

Qinyong Lin, Rongqian Yang, Lin Yang, Huazhou Chen, Bohan Li, Ken Cai

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of medical systems, 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

6 authors.

Qinyong LinCollege of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou, China.
Rongqian YangDepartment of Biomedical Engineering, South China University of Technology, Guangzhou, Guangdong, China.
Lin YangDepartment of Radiology, Guangdong General Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China.
Huazhou ChenCollege of Science, Guilin University of Technology, Guilin, China.
Bohan LiCollege of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Ken CaiCollege of Automation, Zhongkai University of Agriculture and Engineering, Guangzhou, China. kencaizhku@foxmail.com.ORCID http://orcid.org/0000-0002-5454-7613

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The distribution of fiducial markers is one of the main factors affected the accuracy of optical navigation system. However, many studies have been focused on improving the fiducial registration accuracy or the target registration accuracy, but few solutions involve optimization model for the distribution of fiducial markers. In this paper, we propose an optimization model for the distribution of fiducial markers to improve the optical navigation accuracy. The strategy of optimization model is reducing the distribution from three dimensional to two dimensional to obtain the 2D optimal distribution by using optimization algorithm in terms of the marker number and the expectation equation of target registration error (TRE), and then extend the 2D optimal distribution in two dimensional to three dimensional to calculate the optimal distribution according to the distance parameter and the expectation equation of TRE. The results of the experiments show that the averaged TRE for the human phantom is approximately 1.00 mm by applying the proposed optimization model, and the averaged TRE for the abdominal phantom is 0.59 mm. The experimental results of liver simulator model and ex-vivo porcine liver model show that the proposed optimization model can be effectively applied in liver intervention.

Indexed as

AlgorithmsFiducial MarkersHumansLiverSurgery, Computer-AssistedDistribution of fiducial markersImage-to-patient registrationNavigation accuracyOptical surgical navigationOptimization model

Identifiers

PMID32152742

What OpenQuestion holds

Textmetadata
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.