Evidence map›Paper›PMID 28086888›Full record

ArticleBiomedical engineering online2017

Non-rigid MR-TRUS image registration for image-guided prostate biopsy using correlation ratio-based mutual information.

Lun Gong, Haifeng Wang, Chengtao Peng, Yakang Dai, Min Ding, Yinghao Sun, Xiaodong Yang, Jian Zheng

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biomedical engineering online, 2017. 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
0.9field-weighted citation impact, top 17% 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, 17 citations in OpenAlex.

  1. Article
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  3. Deep learning in medical image registration: a review.Physics in medicine and biology · 2020
    Review
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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

8 authors at 5 institutions in 1 country.

Lun GongSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Haifeng WangDepartment of Urology, Shanghai Changhai Hospital, Shanghai, 200433, China.
Chengtao PengDepartment of Electronic Science and Technology, University of Science and Technology of China, Hefei, 230061, China.
Yakang DaiSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Min DingSchool of Science, Nanjing University of Science and Technology, Nanjing, 210094, China.
Yinghao SunDepartment of Urology, Shanghai Changhai Hospital, Shanghai, 200433, China.
Xiaodong YangSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Jian ZhengSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China. zhengj@sibet.ac.cn.
Chinese Academy of Sciences · CNSecond Military Medical University · CNSuzhou Institute of Biomedical Engineering and Technology · CNNanjing University of Science and Technology · CNUniversity of Science and Technology of China · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo improve the accuracy of ultrasound-guided biopsy of the prostate, the non-rigid registration of magnetic resonance (MR) images onto transrectal ultrasound (TRUS) images has gained increasing attention. Mutual information (MI) is a widely used similarity criterion in MR-TRUS image registration. However, the use of MI has been challenged because of intensity distortion, noise and down-sampling. Hence, we need to improve the MI measure to get better registration effect.

methodsWe present a novel two-dimensional non-rigid MR-TRUS registration algorithm that uses correlation ratio-based mutual information (CRMI) as the similarity criterion. CRMI includes a functional mapping of intensity values on the basis of a generalized version of intensity class correspondence. We also analytically acquire the derivative of CRMI with respect to deformation parameters. Furthermore, we propose an improved stochastic gradient descent (ISGD) optimization method based on the Metropolis acceptance criteria to improve the global optimization ability and decrease the registration time.

resultsThe performance of the proposed method is tested on synthetic images and 12 pairs of clinical prostate TRUS and MR images. By comparing label map registration frame (LMRF) and conditional mutual information (CMI), the proposed algorithm has a significant improvement in the average values of Hausdorff distance and target registration error. Although the average Dice Similarity coefficient is not significantly better than CMI, it still has a crucial increase over LMRF. The average computation time consumed by the proposed method is similar to LMRF, which is 16 times less than CMI.

conclusionWith more accurate matching performance and lower sensitivity to noise and down-sampling, the proposed algorithm of minimizing CRMI by ISGD is more robust and has the potential for use in aligning TRUS and MR images for needle biopsy.

Indexed as

Image Processing, Computer-AssistedMagnetic Resonance ImagingRectumBiopsyHumansMaleProstateProstatic NeoplasmsSurgery, Computer-AssistedUltrasonographyCRMIISGDNeedle biopsyNon-rigid registrationProstate

Identifiers

PMID28086888
PMCPMC5234261
OpenAlexW2573046029

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.