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International Journal of Computer Assisted Radiology and Surgery

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28-04-2020 | Original Article

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

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

Published in: International Journal of Computer Assisted Radiology and Surgery | Issue 8/2020

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Abstract

Purpose

Lung 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.

Methods

The 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.

Results

The 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.

Conclusions

The 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.

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Title: A statistical weighted sparse-based local lung motion modelling approach for model-driven lung biopsy
Authors: Dong Chen
Hongzhi Xie
Lixu Gu
Wei Guo
Liang Tian
Jing Liu
Publication date: 28-04-2020
Publisher: Springer International Publishing
Published in: International Journal of Computer Assisted Radiology and Surgery / Issue 8/2020
Print ISSN: 1861-6410
Electronic ISSN: 1861-6429
DOI: https://doi.org/10.1007/s11548-020-02154-7

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Abstract

Purpose

Methods

Results

Conclusions

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