Evidence map›Paper›PMID 36010363›Full record

ArticleDiagnostics (Basel, Switzerland)2022

Custom 3D fMRI Registration Template Construction Method Based on Time-Series Fusion.

Zhongyang Wang, Junchang Xin, Huixian Shen, Qi Chen, Zhiqiong Wang, Xinlei Wang

Open access · goldAbstract read
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.4field-weighted citation impact, top 40% 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

1 citing paper in PubMed, 5 citations in OpenAlex.

  1. 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 1 institution in 1 country.

Zhongyang WangSchool of Computer Science & Engineering, Northeastern University, Shenyang 110819, China.ORCID 0000-0002-9956-795X
Junchang XinSchool of Computer Science & Engineering, Northeastern University, Shenyang 110819, China.ORCID 0000-0003-2077-8269
Huixian ShenSchool of Computer Science & Engineering, Northeastern University, Shenyang 110819, China.
Qi ChenCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China.
Zhiqiong WangCollege of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China.
Xinlei WangSchool of Computer Science & Engineering, Northeastern University, Shenyang 110819, China.
Northeastern University · CN

Funding

Fundamental Research Funds for the Central Universities of China N2116016, N2104001, N2019007, N2224001-10National Key R&D Program of China 2021YFB3300900Neusoft Group Co., Ltd. Open Project support NCBETOP2102
6 · The paper itself

Abstract

As the brain standard template for medical image registration has only been constructed with an MRI template, there is no three-dimensional fMRI standard template for use, and when the subject's brain structure is quite different from the standard brain structure, the registration to the standard space will lead to large errors. Registration to an individual space can avoid this problem. However, in the current fMRI registration algorithm based on individual space, the reference image is often selected by researchers or randomly selected fMRI images at a certain time point. This makes the quality of the reference image very dependent on the experience and ability of the researchers and has great contingency. Whether the reference image is appropriate and reasonable affects the rationality and accuracy of the registration results to a great extent. Therefore, a method for constructing a 3D custom fMRI template is proposed. First, the data are preprocessed; second, by taking a group of two-dimensional slices corresponding to the same layer of the brain in three-dimensional fMRI images at multiple time points as image sequences, each group of slice sequences are registered and fused; and finally, a group of fused slices corresponding to different layers of the brain are obtained. In the process of registration, in order to make full use of the correlation information between the sequence data, the feature points of each two slices of adjacent time points in the sequence are matched, and then according to the transformation relationship between the adjacent images, they are recursively forwarded and mapped to the same space. Then, the fused slices are stacked in order to form a three-dimensional customized fMRI template with individual pertinence. Finally, in the classic registration algorithm, the difference in the registration accuracy between using a custom fMRI template and different standard spaces is compared, which proves that using a custom template can improve the registration effect to a certain extent.

Indexed as

customfMRI templatefused slicessequences

Identifiers

PMID36010363
PMCPMC9407088
OpenAlexW4293677747

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.