Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery

Abstract

Laparoscopic liver surgery is challenging to perform due to a compromised ability of the surgeon to localize subsurface anatomy in the constrained environment. While image guidance has the potential to address this barrier, intraoperative factors, such as insufflation and variable degrees of organ mobilization from supporting ligaments, may generate substantial deformation. The severity of laparoscopic deformation in humans has not been characterized, and current laparoscopic correction methods do not account for the mechanics of how intraoperative deformation is applied to the liver. We first measure the degree of laparoscopic deformation at two insufflation pressures over the course of laparoscopic-to-open conversion in 25 patients. With this clinical data alongside a mock laparoscopic phantom setup, we report a biomechanical correction approach that leverages anatomically load-bearing support surfaces from ligament attachments to iteratively reconstruct and account for intraoperative deformations. Laparoscopic deformations were significantly larger than deformations associated with open surgery, and our correction approach yielded subsurface target error of [Formula: see text] and surface error of [Formula: see text] using only sparse surface data with realistic surgical extent. Laparoscopic surface data extents were examined and found to impact registration accuracy. Finally, we demonstrate viability of the correction method with clinical data.

Physics-Based Deep Neural Network for Augmented Reality During Liver Surgery

“…Without lack of generality, let us consider the scenario where an augmented view of the liver has to be provided during hepatic surgery. The intraoperative data is provided as a point cloud, reconstructed from the laparoscopic images [14,13] or direct camera view [9] of the operative field. Images are usually acquired at 20Hz or more, and latency between data acquisition and display of the augmented image needs to be minimized.…”

The Effect of Discretization on Parameter Identification. Application to Patient-Specific Simulations

“…Being aware of these challenges, many of which have been studied in the literature [Marchesseau et al, 2010, Rifai et al, 2011, Wittek et al, 2009, we focus solely on the problematic of parameter identification due to coarse discretization which is necessary in real-time context. To this end, we propose a methodology based on numerical experiments which were designed to be generic while maintaining the essential features related to real-time parameter identification in coarse meshes, as explained below.…”

Multiphysics modeling toward enhanced guidance in hepatic microwave ablation: a preliminary framework

“…Rucker et al 26 described an inverse approach that optimizes boundary conditions described by a parameterized posterior displacement field, based on the reality of organ deformation during operative mobilization, to minimize the residual error between the intraoperatively collected anterior surface digitization and the deformed model surface. More recently, Heiselman et al 27 expanded upon the work of Rucker et al by reformulating the application of boundary conditions to a control point strategy, which allows for multiple independent support surfaces to be designated. Both surface-based methods have demonstrated effective correction of soft-tissue deformation in phantom and clinical applications for hepatic resection.…”

“…Both surface-based methods have demonstrated effective correction of soft-tissue deformation in phantom and clinical applications for hepatic resection. [26][27][28][29][30] With respect to thermal dose guidance, predictive, biophysical modeling of MWA presents a strong alternative to the manufacturer-provided estimates of ablation outcome by utilizing numerical approaches to solve the physical governing equations defining energy deposition and heat transfer. Other direct thermographic measurement strategies such as MR 31 and US 32 thermography are on the horizon, but these also have high technical and economic hurdles for practical use in the operating room or interventional suite.…”

“…When specifically considering the open and laparoscopic settings, as is the primary focus of this paper, advanced navigation approaches have been used to create a spatial mapping between surgical instrumentation and imaging data to improve visualization of anatomical structures. Based on our existing image-guidance work for surgical resection, [26][27][28][29][30] the work here uses a hepatic deformation phantom setup designed to evaluate the accuracy of ablation probe localization when using the deformation correction method of Heiselman et al 27 as compared to a clinically relevant rigid registration method. 43 With respect to presentation, only a portion of the anterior surface of the liver is available for organ-to-organ registration and assumes deformation conditions that are associated with surgical packing, i.e., the presented liver for surgery is first mobilized from the ligamenture, and then immobilized with surgical packing placed beneath the posterior surface of the organ.…”