Evidence map›Paper›PMID 35704238›Full record

ArticleInternational journal of computer assisted radiology and surgery2022

Augmented reality navigation with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors.

Longfei Ma, Hanying Liang, Boxuan Han, Shizhong Yang, Xinran Zhang, Hongen Liao

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Article in International journal of computer assisted radiology and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 1 pooled it
2.9field-weighted citation impact, top 8% 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

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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

8 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.

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  8. 3D reconstruction for maxillary anterior tooth crown based on shape and pose estimation networks.International journal of computer assisted radiology and surgery · 2023
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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 2 institutions in 1 country.

Longfei Ma *Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
Hanying Liang *Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
Boxuan HanDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
Shizhong YangHepato-Pancreato-Biliary Center, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing, 102218, China.
Xinran ZhangDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China.
Hongen LiaoDepartment of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China. liao@tsinghua.edu.cn.ORCID http://orcid.org/0000-0003-3847-9347
Tsinghua University · CNBeijing Tsinghua Chang Gung Hospital · CN

Funding

Beijing Municipal Natural Science Foundation 7212202Beijing Municipal Natural Science Foundation L192013National Natural Science Foundation of China 81901844National Natural Science Foundation of China 81930119National Natural Science Foundation of China 82027807National Natural Science Foundation of China 82090052
6 · The paper itself

Abstract

purposeWe present a novel augmented reality (AR) surgical navigation method with ultrasound-assisted point cloud registration for percutaneous ablation of liver tumors. A preliminary study is carried out to verify its feasibility.

methodsTwo three-dimensional (3D) point clouds of the liver surface are derived from the preoperative images and intraoperative tracked US images, respectively. To compensate for the soft tissue deformation, the point cloud registration between the preoperative images and the liver is performed using the non-rigid iterative closest point (ICP) algorithm. A 3D AR device based on integral videography technology is designed to accurately display naked-eye 3D images for surgical navigation. Based on the above registration, naked-eye 3D images of the liver surface, planning path, entry points, and tumor can be overlaid in situ through our 3D AR device. Finally, the AR-guided targeting accuracy is evaluated through entry point positioning.

resultsExperiments on both the liver phantom and in vitro pork liver were conducted. Several entry points on the liver surface were used to evaluate the targeting accuracy. The preliminary validation on the liver phantom showed average entry-point errors (EPEs) of 2.34 ± 0.45 mm, 2.25 ± 0.72 mm, 2.71 ± 0.82 mm, and 2.50 ± 1.11 mm at distinct US point cloud coverage rates of 100%, 75%, 50%, and 25%, respectively. The average EPEs of the deformed pork liver were 4.49 ± 1.88 mm and 5.02 ± 2.03 mm at the coverage rates of 100% and 75%, and the average covered-entry-point errors (CEPEs) were 4.96 ± 2.05 mm and 2.97 ± 1.37 mm at 50% and 25%, respectively.

conclusionExperimental outcomes demonstrate that the proposed AR navigation method based on US-assisted point cloud registration has achieved an acceptable targeting accuracy on the liver surface even in the case of liver deformation.

Indexed as

Augmented RealityCatheter AblationLiver NeoplasmsSurgery, Computer-AssistedHumansImaging, Three-DimensionalAugmented realityNon-rigid registrationPercutaneous ablationPoint cloudSurgical navigationUltrasound

Identifiers

PMID35704238
OpenAlexW4282977623

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.