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  4. Study of Lung Deflation Prediction with Biomechanical Modeling for Minimally Invasive Surgery

Study of Lung Deflation Prediction with Biomechanical Modeling for Minimally Invasive Surgery

A Lesage1*, A Tam2 , K Brock3 , D Rice4 , G Cazoulat5 , (1)Department of Imaging Physics Morfeus Laboratory, University of Texas MD Anderson Cancer Center, Houston, TX, (2)Department of Interventional Radiology, University of Texas MD Anderson Cancer Center, Houston, (3)Department of Imaging Physics Morfeus Laboratory, University of Texas MD Anderson Cancer Center, Houston, TX, (4) Department of Thoracic and Cardiovascular Surgery, University of Texas MD Anderson Cancer Center, Houston, TX,(5)Department of Imaging Physics Morfeus Laboratory, The University of Texas MD Anderson Cancer Center, Houston, TX

Presentations

(Sunday, 7/14/2019) 4:00 PM - 5:00 PM

Room: 221AB

Purpose: Video-assisted thoracoscopic surgery (VATS) is a modern procedure for lung tumor resection which notably reduces post-operative burden and morbidity compared to open thoracotomy. However, tumor localization relies on an intraoperative camera. Therefore, this procedure eligibility is limited to patients with tumors located close to the lung surface or undergoing existing tumor localization techniques involving a prior surgical procedure. The study aim is to predict patient anatomy and tumor displacements during surgery from presurgical medical images (Computed Tomography). Thus potentially allowing to expand the VATS eligibility criteria.

Methods: For three patients, two CT scans, showing respectively the deflated lung during lung biopsy and the reinflated lung after chest tube insertion, were gathered. The shapes of the inflated lungs and patient body were segmented to create a first order tetrahedral mesh with elastic and Ogden hyperelastic properties for the lung parenchyma (Young’s modulus of 4kPa and Poisson’s ratio of 0.3) and with sliding boundaries of the lung relative to the pleura surface. Simulations of the lung deflation under gravity in supine position and constant pressure applied on the lung surface (800 Pa) were completed with the explicit Altair RADIOSS Finite Element Modeling solver. The predicted deflation state presenting a lung volume close to the volume measured on the CT of the deflated lung was selected for comparison.

Results: The group mean dice score was 0.86±0.04 and 0.85 ±0.07 for the elastic and hyperelastic model respectively. However, the elastic computations are less stable to the large deformations occurring during deflation.

Conclusion: Our novel method to predict lung deflation displacements will potentially offer the option of minimally invasive surgery to more patients. Model inclusion of airways and vessel trees as stiffer structures or lobe segmentation or the use of different mesh options or mechanical models are being investigated.

Funding Support, Disclosures, and Conflict of Interest: This research was supported by the University Cancer Foundation via the Institutional Research Grant program at The University of Texas MD Anderson Cancer Center and RaySearch Laboratories.

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