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Lung deformation between preoperative CT and intraoperative CBCT for thoracoscopic surgery: a case study

Abstract : Video-Assisted Thoracoscopic Surgery (VATS) is a promising surgical treatment for early-stage lung cancer. With respect to standard thoracotomy, it is less invasive and provides better and faster patient recovery. However, a main issue is the accurate localization of small, subsolid nodules. While intraoperative Cone-Beam CT (CBCT) images can be acquired, they cannot be directly compared with preoperative CT images due to very large lung deformations occurring before and during surgery. This paper focuses on the quantification of deformations due to the change of positioning of the patient, from supine during CT acquisition to lateral decubitus in the operating room. A method is first introduced to segment the lung cavity in both CT and CBCT. The images are then registered in three steps: an initial alignment, followed by rigid registration and finally non-rigid registration, from which deformations are measured. Accuracy of the registration is quantified based on the Target Registration Error (TRE) between paired anatomical landmarks. Results of the registration process are on the order of 1.01 mm in median, with minimum and maximum errors 0.35 mm and 2.34 mm. Deformations on the parenchyma were mesured to be up to 14 mm and approximately 7 mm in average for the whole lung structure. While this study is only a first step towards image-guided therapy, it highlights the importance of accounting for lung deformation between preoperative and intraoperative images, which is crucial for the intraoperative nodule localization.
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Submitted on : Monday, August 6, 2018 - 1:38:04 AM
Last modification on : Wednesday, September 14, 2022 - 10:20:04 AM
Long-term archiving on: : Wednesday, November 7, 2018 - 12:58:29 PM


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Pablo Alvarez, Matthieu Chabanas, Simon Rouzé, Miguel Castro, Yohan Payan, et al.. Lung deformation between preoperative CT and intraoperative CBCT for thoracoscopic surgery: a case study. SPIE Medical Imaging: Image-Guided Procedures, Robotic Interventions, and Modeling, Feb 2018, Houston, United States. ⟨10.1117/12.2293938⟩. ⟨inserm-01853906⟩



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