Evidence map›Paper›PMID 33515131›Full record

ArticleMedical & biological engineering & computing2021

A coarse-to-fine registration method for three-dimensional MR images.

Cuixia Li, Yuanyuan Zhou, Yinghao Li, Shanshan Yang

Abstract read
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In one paragraph

Article in Medical & biological engineering & computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

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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

4 authors.

Cuixia LiSchool of Software Academy, Zhengzhou University, Zhengzhou, 450000, China.
Yuanyuan ZhouSchool of Software Academy, Zhengzhou University, Zhengzhou, 450000, China.
Yinghao LiSchool of Software Academy, Zhengzhou University, Zhengzhou, 450000, China. yinghaoli@zzu.edu.cn.ORCID http://orcid.org/0000-0002-8830-4193
Shanshan YangSchool of Software Academy, Zhengzhou University, Zhengzhou, 450000, China.

Funding

Education Department of Henan Province 2018A03003Education Department of Henan Province 21A520042Science and Technology Department of Henan Province 172102210003Startup Research Fund of Zhengzhou University F0001297
6 · The paper itself

Abstract

Three-dimensional (3D) multimodal magnetic resonance (MR) image registration aims to align similar things in different MR images spatially. Such a technology is useful in auxiliary disease diagnosis and surgical treatment. However, inconsistent intensity correspondence and large initial displacement contribute to the difficulty in registering multimodal MR volumes. A coarse-to-fine method is proposed in this study for pairwise 3D MR image rigid registration. Firstly, the proposed method extracts image feature points to form unregistered point sets and performs coarse registration based on point set registration to reduce the initial displacements of offset images effectively. Then, this method calculates a grey histogram based on voxels in the adaptive region of interest and further improves registration accuracy by maximizing mutual information of coarse-registered images. Some representative registration methods are compared on the basis of three MR image datasets to evaluate the performance of the proposed method. Experimental results show that the proposed method improved more in registration success rate and accuracy compared with conventional registration methods, especially when initial displacements are large.

Indexed as

Imaging, Three-DimensionalMagnetic Resonance ImagingAlgorithmsCoarse-to-fineFeature points extractionImage registrationMaximizing mutual information

Identifiers

What OpenQuestion holds

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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.