Evidence map›Paper›PMID 32347465›Full record

ArticleInternational journal of computer assisted radiology and surgery2020

A statistical weighted sparse-based local lung motion modelling approach for model-driven lung biopsy.

Dong Chen, Hongzhi Xie, Lixu Gu, Wei Guo, Liang Tian, Jing Liu

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In one paragraph

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 1 paper.

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

What it found

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Dong ChenCollege of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China.ORCID http://orcid.org/0000-0003-0363-7712
Hongzhi XieDepartment of Cardiothoracic Surgery, Peking Union Medical College Hospital, Beijing, China.
Lixu GuLaboratory of Image Guided Surgery and Therapy (IGST), Shanghai Jiao Tong University, Shanghai, China.
Wei GuoCollege of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China.
Liang TianCollege of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China. tianliang@chmiot.net.
Jing LiuCollege of Computer and Cyber Security, Hebei Normal University, Shijiazhuang, China. liujing001@ict.ac.cn.

Funding

the National Natural Science Foundation of China 61902109the Natural Science Foundation of Hebei Province F2019205070the Science Foundation of Hebei Normal University L2019B01
6 · The paper itself

Abstract

purposeLung biopsy is currently the most effective procedure for cancer diagnosis. However, respiration-induced location uncertainty presents a challenge in precise lung biopsy. To reduce the medical image requirements for motion modelling, in this study, local lung motion information in the region of interest (ROI) is extracted from whole chest computed tomography (CT) and CT-fluoroscopy scans to predict the motion of potentially cancerous tissue and important vessels during the model-driven lung biopsy process.

methodsThe motion prior of the ROI was generated via a sparse linear combination of a subset of motion information from a respiratory motion repository, and a weighted sparse-based statistical model was used to preserve the local respiratory motion details. We also employed a motion prior-based registration method to improve the motion estimation accuracy in the ROI and designed adaptive variable coefficients to interactively weigh the relative influence of the prior knowledge and image intensity information during the registration process.

resultsThe proposed method was applied to ten test subjects for the estimation of the respiratory motion field. The quantitative analysis resulted in a mean target registration error of 1.5 (0.8) mm and an average symmetric surface distance of 1.4 (0.6) mm.

conclusionsThe proposed method shows remarkable advantages over traditional methods in preserving local motion details and reducing the estimation error in the ROI. These results also provide a benchmark for lung respiratory motion modelling in the literature.

Indexed as

Models, StatisticalRespirationAlgorithmsBiopsyHumansLungOrgan MotionRadionuclide ImagingTomography, X-Ray ComputedComputed tomographyMotion prior-based registrationStatistical respiratory motion modelWeighted sparse algorithm

Identifiers

PMID32347465

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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.