Evidence map›Paper›PMID 41463648›Full record

ReviewBioengineering (Basel, Switzerland)2025

A Review on Registration Techniques for Cardiac Computed Tomography and Ultrasound Images.

Zongyang Li, Huijing He, Qi Wang, Luyu Li, Hongjian Gao, Jiehui Li

Abstract readReview
In one paragraph

Review in Bioengineering (Basel, Switzerland), 2025. 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.

Zongyang LiDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
Huijing HeDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
Qi WangDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
Luyu LiDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.
Hongjian GaoDepartment of Biomedical Engineering, College of Chemistry and Life Science, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0002-9902-893X
Jiehui LiState Key Laboratory of Cardiovascular Disease, Department of Cardiac Surgery, National Center for Cardiovascular Diseases, Fuwai Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100037, China.

Funding

the National Natural Science Foundation of China 82171941
6 · The paper itself

Abstract

With the rapid development of medical imaging technology, the early diagnosis and treatment of heart disease have been significantly improved. Cardiac CT (Computed Tomography) and ultrasound images are often used in combination to provide more comprehensive information on cardiac structure and function due to their respective advantages and limitations. However, due to the significant differences in imaging principles, resolutions, and viewing angles between these two imaging modalities, how to effectively register cardiac CT and ultrasound images has become an important research topic in imaging and clinical applications. This article summarizes the research progress of cardiac CT and ultrasound image registration, and analyzes the existing registration methods and their advantages and disadvantages. Firstly, this article summarizes traditional registration methods based on image intensity, feature points, and regions, and explores the application of rigid and non-rigid registration algorithms. Secondly, in view of common challenges in cardiac CT and ultrasound image registration, such as image noise, deformation, and differences in imaging time, this article discusses the recent advances in multimodal registration technology in cardiac imaging and forecasts the potential of deep learning methods in registration. In addition, this article also evaluates the application effects and limitations of these methods in clinical practice, and finally looks forward to the future development direction of cardiac image registration technology, especially its potential applications in personalized medicine and real-time monitoring. Through a comprehensive review of the current research status of cardiac CT and ultrasound image registration, this article provides a systematic theoretical framework for researchers in related fields and provides a reference for future technological breakthroughs and clinical translation.

Indexed as

deep learningdeformation fieldimage registration

Identifiers

PMID41463648
PMCPMC12729628

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.