Soft Tissue Resection for Prostatectomy Simulation

Castañeda, M.; Cosı́o, Fernando Arámbula

doi:10.1007/978-3-540-30136-3_70

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Accurate target localization is a critical aspect of surgical procedures, and ensuring precision in target localization becomes particularly challenging due to tissue deformation during surgical manipulation, mainly when tissue separation occurs. Efforts have been made in the literature to model the phenomena of tissue separation [1][2][3][4][5] , mainly for applications in developing surgical simulators. Despite some work in the literature investigating the modeling of resection and retraction for surgical guidance applications [6][7][8][9][10] , to this date, it still needs to be solved.…”

Section: Introductionmentioning

confidence: 99%

Image reconstruction and tissue separation modeling with XFEM for surgical visualization and guidance

Pereira,

Ringel,

Miga

2024

Medical Imaging 2024: Image-Guided Procedures, Robotic Interventions, and Modeling

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Surgical procedures require precise target localization to ensure optimal outcomes and minimize patient risks. This paper presents an approach that combines eXtended Finite Element Method (XFEM) for retraction modeling with medical image updating to improve target visualization and localization accuracy in surgical guidance. XFEM is employed to simulate tissue retraction, capturing the complex mechanical behavior of tissue separation during surgery. By incorporating XFEMderived displacement fields, a strategy for updating preoperative medical images is introduced, enabling adjustments to better visualize the tissue deformation state. XFEM's ability to model discontinuities in mechanical behavior provides a realistic representation of tissue retraction. To validate the effectiveness of the approach, experiments were conducted on tissue phantom samples. The average displacement error between the ground-truth measurements and the reconstruction using the proposed method ranged from 1.5 to 2.1 mm, with an overall average error of 1.8 ± 1.3 mm across different phantom samples. This demonstrates improvements in target localization compared to traditional methods that do not account for tissue retraction. The integration of XFEM-based retraction modeling and displacement field-driven image updating offers an interesting tool for improving surgical guidance systems, ultimately leading to more precise and successful interventions.

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