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A personalized image-guided intervention system for peripheral lung cancer on patient-specific respiratory motion model

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

Due to respiratory motion, precise tracking of lung nodule movement is a persistent challenge for guiding percutaneous lung biopsy during image-guided intervention. We developed an automated image-guided system incorporating effective and robust tracking algorithms to address this challenge. Accurate lung motion prediction and personalized image-guided intervention are the key technological contributions of this work.

Methods

A patient-specific respiratory motion model is developed to predict pulmonary movements of individual patients. It is based on the relation between the artificial 4D CT and corresponding positions tracked by position sensors attached on the chest using an electromagnetic (EM) tracking system. The 4D CT image of the thorax during breathing is calculated through deformable registration of two 3D CT scans acquired at inspiratory and expiratory breath-hold. The robustness and accuracy of the image-guided intervention system were assessed on a static thorax phantom under different clinical parametric combinations.

Results

Real 4D CT images of ten patients were used to evaluate the accuracy of the respiratory motion model. The mean error of the model in different breathing phases was 1.59 ± 0.66 mm. Using a static thorax phantom, we achieved an average targeting accuracy of 3.18 ± 1.2 mm across 50 independent tests with different intervention parameters. The positive results demonstrate the robustness and accuracy of our system for personalized lung cancer intervention.

Conclusions

The proposed system integrates a patient-specific respiratory motion compensation model to reduce the effect of respiratory motion during percutaneous lung biopsy and help interventional radiologists target the lesion efficiently. Our preclinical studies indicate that the image-guided system has the ability to accurately predict and track lung nodules of individual patients and has the potential for use in the diagnosis and treatment of early stage lung cancer.

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Acknowledgements

We would like to thank Kelvin Wong for his contribution of an early version of IGT system and thank Solomon S. Y. Wong for revising the article.

Funding

This study was funded by the National Key R&D Program of China (2017YFB1300204), Hefei Foreign Cooperation Project (ZR201801020002), the Natural Science Fund of Anhui Province (2008085MC69), Collaborative Innovation Program of Hefei Science Center (2020HSC-CIP001), CAS Anhui Province Key Laboratory of Medical Physics and Technology (LMPT201904) and Director’s Fund of Hefei Cancer Hospital of CAS (YZJJ2019C14, YZJJ2019A04) to TW, ZZ, QC, LZ, GX, LY, HW, HL) and Texas CPRIT RP110428 and John S Dunn Research Foundation to TCH and STCW.

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Author notes

  1. Tengfei Wang and Tiancheng He contributed equally to this work.

Authors and Affiliations

  1. Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, People’s Republic of China

    Tengfei Wang, Zhenglin Zhang, Lizhuang Yang, Hongzhi Wang & Hai Li

  2. Hefei Cancer Hospital, Chinese Academy of Sciences, Hefei, 230031, People’s Republic of China

    Tengfei Wang, Qi Chen, Liwei Zhang, Guoren Xia, Lizhuang Yang, Hongzhi Wang & Hai Li

  3. Systems Medicine and Bioengineering, Houston Methodist Cancer Center, Houston Methodist Hospital, Houston, TX, 77030, USA

    Tiancheng He & Stephen T. C. Wong

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  1. Tengfei Wang
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Correspondence to Stephen T. C. Wong or Hai Li.

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Wang, T., He, T., Zhang, Z. et al. A personalized image-guided intervention system for peripheral lung cancer on patient-specific respiratory motion model. Int J CARS 17, 1751–1764 (2022). https://doi.org/10.1007/s11548-022-02676-2

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  • DOI: https://doi.org/10.1007/s11548-022-02676-2

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