Evidence map›Paper›PMID 32361857›Full record

ArticleInternational journal of computer assisted radiology and surgery2020

Assessment and application of the coherent point drift algorithm to augmented reality surgical navigation for laparoscopic partial nephrectomy.

Xiaohui Zhang, Tianmiao Wang, Xuebin Zhang, Yinghao Zhang, Junchen Wang

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Article in International journal of computer assisted radiology and surgery, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

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2citing papers in PubMed, 1 pooled it
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1 · What the graph read from it

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3 · Its place in the literature

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2 citing papers in PubMed, 1 synthesis or guideline pooled it.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Xiaohui ZhangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Tianmiao WangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Xuebin ZhangDepartment of Urology, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China.
Yinghao ZhangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Junchen WangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China. wangjunchen@buaa.edu.cn.ORCID http://orcid.org/0000-0002-0916-9932

Funding

National Natural Science Foundation of China 61701014National Natural Science Foundation of China 61911540075
6 · The paper itself

Abstract

purposeThe surface-based registration approach to laparoscopic augmented reality (AR) has clear advantages. Nonrigid point-set registration paves the way for surface-based registration. Among current non-rigid point set registration methods, the coherent point drift (CPD) algorithm is rarely used because of two challenges: (1) volumetric deformation is difficult to predict, and (2) registration from intraoperative visible tissue surface to whole anatomical preoperative model is a "part-to-whole" registration that CPD cannot be applied directly to. We preliminarily applied CPD on surgical navigation for laparoscopic partial nephrectomy (LPN). However, it introduces normalization errors and lacks navigation robustness. This paper presents important advances for more effectively applying CPD to LPN surgical navigation while attempting to quantitatively evaluate the accuracy of CPD-based surgical navigation.

methodsFirst, an optimized volumetric deformation (Op-VD) algorithm is proposed to achieve accurate prediction of volume deformation. Then, a projection-based partial selection method is presented to conveniently and robustly apply the CPD to LPN surgical navigation. Finally, kidneys with different deformations in vitro, phantom and in vivo experiments are performed to evaluate the accuracy and effectiveness of our approach.

resultsThe average root-mean-square error of volume deformation was refined to 0.84 mm. The mean target registration error (TRE) of the surface and inside markers in the in vitro experiments decreased to 1.51 mm and 1.29 mm, respectively. The robustness and precision of CPD-based navigation were validated in phantom and in vivo experiments, and the mean navigation TRE of the phantom experiments was found to be [Formula: see text] mm.

conclusionAccurate volumetric deformation and robust navigation results can be achieved in AR navigation of LPN by using surface-based registration with CPD. Evaluation results demonstrate the effectiveness of our proposed methods while showing the clinical application potential of CPD. This work has important guiding significance for the application of the CPD in laparoscopic AR.

Indexed as

Augmented RealityAlgorithmsHumansKidneyLaparoscopyNephrectomyPhantoms, ImagingSurgery, Computer-AssistedCoherent point drift (CPD)Nonrigid registrationSurface-based registrationSurgical navigation

Identifiers

PMID32361857

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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.