Evidence map›Paper›PMID 34176070›Full record

ArticleInternational journal of computer assisted radiology and surgery2021

A hybrid feature-based patient-to-image registration method for robot-assisted long bone osteotomy.

Chunlei Zhang, Yu Liu, Yunguang Zhang, He Li

Abstract read
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In one paragraph

Article in International journal of computer assisted radiology and surgery, 2021. 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
0.5field-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, 8 citations in OpenAlex.

  1. Automatic system calibration for orthognathic robot system.International journal of computer assisted radiology and surgery · 2026
    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

4 authors at 1 institution in 1 country.

Chunlei ZhangSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang, China.
Yu LiuSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang, China. yuliu@me.neu.edu.cn.ORCID http://orcid.org/0000-0002-7804-4754
Yunguang ZhangShenyang Xingwen Technology Co., Ltd, Shenyang, China.
He LiSchool of Mechanical Engineering and Automation, Northeastern University, Shenyang, China.
Northeastern University · CN

Funding

Fundamental Research Funds for the Central Universities N2003011National Natural Science Foundation of China 51875094
6 · The paper itself

Abstract

purposeThe purpose of this study is to provide a simple, feasible and effective patient-to-image registration method for robot-assisted long bone osteotomy, which has rarely been systematically reported. The practical requirement is to meet the accuracy of 1 mm or even higher without bone-implanted markers.

methodsA hybrid feature-based registration method termed CR-RAMSICP is proposed. Point-based coarse registration (CR) is accomplished relying on the optical retro-reflective markers attached to the tracked rigid body fixed out of the bone. In surface-based fine registration, an improved iterative closest point (ICP) algorithm based on the range-adaptive matching strategy (termed RAMSICP) is presented to cope with the robust precise matching between the asymmetric patient and image point clouds, which avoids converging to a local minimum.

resultsA series of registration experiments based on the isolated porcine iliums are carried out. The results illustrate that CR-RAMSICP not only significantly outperforms CR and CR-ICP in the accuracy and reproducibility, but also exhibits better robustness to the CR errors and less sensitiveness to the distribution and number of fiducial points located in the patient point cloud than CR-ICP.

conclusionThe proposed registration method CR-RAMSICP can stably satisfy the desired registration accuracy without the use of bone-implanted markers like fiducial screws. Besides, the RAMSICP algorithm used in fine registration is convenient for programming because any complex metrics or models are not involved.

Indexed as

RoboticsSurgery, Computer-AssistedAlgorithmsAnimalsHumansImage Processing, Computer-AssistedOsteotomyPhantoms, ImagingReproducibility of ResultsSwineLong bone osteotomyOrthopedic robotPatient-to-image registrationRobot-assisted surgery

Identifiers

PMID34176070
OpenAlexW3173447349

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.