Cone-Beam CT (CBCT) is a valuable imaging modality for the intraoperative localization of pulmonary nodules during Video-Assisted Thoracoscopic Surgery (VATS). However, inferring the nodule position from the CBCT to the operative field remains challenging and could greatly benefit from computer-aided guiding. As a first step towards an Augmented Endoscopy guiding system, we propose to register 2D monocular endoscopic views into the 3D CBCT space. Ribs and wound protectors are segmented in both imaging modalities, then registered using an image-to-cloud Iterative Closest Point variant. The method is evaluated qualitatively on clinical VATS video sequences from three patients. The promising results validate this first step towards a seamless monocular VATS navigation.
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 measured 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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