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A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery.

ABSTRACT: The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model with allowance for air evacuation. The difficult problem of estimating intraoperative lung deformations is decomposed into two more tractable sub-problems: (i) estimating the deformation due the change of patient pose from preoperative CT (supine) to intraoperative CBCT (lateral decubitus); and (ii) estimating the pneumothorax deformation, i.e. a collapse of the lung within the thoracic cage. We were able to demonstrate the feasibility of our localization framework with a retrospective validation study on 5 VATS clinical cases. Average initial errors in the range of 22 to 38 mm were reduced to the range of 4 to 14 mm, corresponding to an error correction in the range of 63 to 85%. To our knowledge, this is the first markerless lung deformation compensation method dedicated to VATS and validated on actual clinical data.

SUBMITTER: Alvarez P

PROVIDER: S-EPMC9549472 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature

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A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery.

Alvarez Pablo P Rouzé Simon S Miga Michael I MI Payan Yohan Y Dillenseger Jean-Louis JL Chabanas Matthieu M

Medical image analysis 20210130

The resection of small, low-dense or deep lung nodules during video-assisted thoracoscopic surgery (VATS) is surgically challenging. Nodule localization methods in clinical practice typically rely on the preoperative placement of markers, which may lead to clinical complications. We propose a markerless lung nodule localization framework for VATS based on a hybrid method combining intraoperative cone-beam CT (CBCT) imaging, free-form deformation image registration, and a poroelastic lung model w ...[more]

PMID: 33588119

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Project description:BackgroundIn the process of video-assisted thoracoscopic surgery (VATS) for lung nodule resection, lung is leaded to atelectasis. However, preoperative computed tomography (CT) images are taken during inspiration, which means they differ significantly from the lung status observed during surgery. Consequently, this discrepancy can make the localization of small or subsolid nodules challenging during the operation. This study aimed to develop a CT-based virtual atelectasis simulation system for noninvasive lung nodule localization. By accurately simulating atelectasis, this study aimed to improve the precision of presurgical planning from lung nodule resections.MethodsThis study retrospectively examined 20 patients who had either subsolid nodules or small nodules less than 3 cm in size, selected from a cohort of 279 patients who underwent VATS surgery for lung nodules in Korea University Ansan Hospital between June 28, 2021, and January 22, 2024. Chest CT images of the lungs of 20 patients were acquired, and image data were converted three-dimensional models. The mesh points extracted from these lung models were manipulated to simulate the effects of gravity, by adjusting the lung shapes and nodule locations to align with the respective surgical postures of the patients. Subsequently, we assessed the similarity of the simulation by comparing the resulting deformed lung shapes and nodule locations with the corresponding perspectives observed in the surgical videos.ResultsThe average volume of the entire lung among the patients was 2,336 cm3 (±588). After atelectasis simulation, the average lung shrinkage rate was 48.6% (±12.9%). Evaluations of an average of 15 pairs of images per case revealed significant conformity between atelectasis simulation images and surgical video snapshots, with average Dice and Jaccard similarity coefficient values of 90.27 and 88.25, respectively. Furthermore, the alignment of nodule locations between the simulations and surgical anticipation demonstrated notable accuracy, with an average Hausdorff distance of 6.39 mm.ConclusionsWe successfully developed a simulation of lung atelectasis based on preoperative CT scans that closely resembled actual surgical videos. The integration of this presurgical atelectasis simulation is anticipated to enhance the accuracy of nodule locations, thus contributing to more efficient and precise surgical planning.

Dataset Information

A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery.

Publications

A hybrid, image-based and biomechanics-based registration approach to markerless intraoperative nodule localization during video-assisted thoracoscopic surgery.

Similar Datasets

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