Kernel-based modeling of pneumothorax deformation using intraoperative cone-beam CT images

Megumi Nakao; Hinako Maekawa; Katsutaka Mineura; Toyofumi F. Chen-Yoshikawa; Tetsuya Matsuda

doi:10.1117/12.2581388

15 February 2021 Kernel-based modeling of pneumothorax deformation using intraoperative cone-beam CT images

Megumi Nakao, Hinako Maekawa, Katsutaka Mineura, Toyofumi F. Chen-Yoshikawa, Tetsuya Matsuda

Proceedings Volume 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling; 115980P (2021) https://doi.org/10.1117/12.2581388
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

In this study, we introduce statistical modeling methods for pneumothorax deformation using paired cone-beam computed tomography (CT) images. We designed a deformable mesh registration framework for shape changes involving non-linear deformation and rotation of the lungs. The registered meshes with local correspondences are available for both surgical guidance in thoracoscopic surgery and building statistical deformation models with inter-patient variations. In addition, a kernel-based deformation learning framework is proposed to reconstruct intraoperative dfl ated states of the lung from the preoperative CT models. This paper reports the findings of pneumothorax deformation and evaluation results of the kernel-based deformation framework.

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Citation Download Citation

Megumi Nakao, Hinako Maekawa, Katsutaka Mineura, Toyofumi F. Chen-Yoshikawa, and Tetsuya Matsuda "Kernel-based modeling of pneumothorax deformation using intraoperative cone-beam CT images", Proc. SPIE 11598, Medical Imaging 2021: Image-Guided Procedures, Robotic Interventions, and Modeling, 115980P (15 February 2021); https://doi.org/10.1117/12.2581388

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