Simulation of Pneumoperitoneum for Laparoscopic Surgery Planning

The effect of intraoperative imaging on surgical navigation for laparoscopic liver resection surgery

Conventional surgical navigation systems rely on preoperative imaging to provide guidance. In laparoscopic liver surgery, insufflation of the abdomen (pneumoperitoneum) can cause deformations on the liver, introducing inaccuracies in the correspondence between the preoperative images and the intraoperative reality. This study evaluates the improvements provided by intraoperative imaging for laparoscopic liver surgical navigation, when displayed as augmented reality (AR). Significant differences were found in terms of accuracy of the AR, in favor of intraoperative imaging. In addition, results showed an effect of user-induced error: image-to-patient registration based on annotations performed by clinicians caused 33% more inaccuracy as compared to image-to-patient registration algorithms that do not depend on user annotations. Hence, to achieve accurate surgical navigation for laparoscopic liver surgery, intraoperative imaging is recommendable to compensate for deformation. Moreover, user annotation errors may lead to inaccuracies in registration processes.

Patient-Specific Simulation of Pneumoperitoneum for Laparoscopic Surgical Planning

Gas insufflation in laparoscopy deforms the abdomen and stretches the overlying skin. This limits the use of surgical image-guidance technologies and challenges the appropriate placement of trocars, which influences the operative ease and potential quality of laparoscopic surgery. This work describes the development of a platform that simulates pneumoperitoneum in a patient-specific manner, using preoperative CT scans as input data. This aims to provide a more realistic representation of the intraoperative scenario and guide trocar positioning to optimize the ergonomics of laparoscopic instrumentation. The simulation was developed by generating 3D reconstructions of insufflated and deflated porcine CT scans and simulating an artificial pneumoperitoneum on the deflated model. Simulation parameters were optimized by minimizing the discrepancy between the simulated pneumoperitoneum and the ground truth model extracted from insufflated porcine scans. Insufflation modeling in humans was investigated by correlating the simulation’s output to real post-insufflation measurements obtained from patients in theatre. The simulation returned an average error of 7.26 mm and 10.5 mm in the most and least accurate datasets respectively. In context of the initial discrepancy without simulation (23.8 mm and 19.6 mm), the methods proposed here provide a significantly improved picture of the intraoperative scenario. The framework was also demonstrated capable of simulating pneumoperitoneum in humans. This study proposes a method for realistically simulating pneumoperitoneum to achieve optimal ergonomics during laparoscopy. Although further studies to validate the simulation in humans are needed, there is the opportunity to provide a more realistic, interactive simulation platform for future image-guided minimally invasive surgery.

Subject-specific modelling of pneumoperitoneum: model implementation, validation and human feasibility assessment

PURPOSE

The aim of this study is to propose a model that simulates patient-specific anatomical changes resulting from pneumoperitoneum, using preoperative data as input. The framework can assist the surgeon through a real-time visualisation and interaction with the model. Such could further facilitate surgical planning preoperatively, by defining a surgical strategy, and intraoperatively to estimate port positions.

METHODS

The biomechanical model that simulates pneumoperitoneum was implemented within the GPU-accelerated NVIDIA FleX position-based dynamics framework. Datasets of multiple porcine subjects before and after abdominal insufflation were used to generate, calibrate and validate the model. The feasibility of modelling pneumoperitoneum in human subjects was assessed by comparing distances between specific landmarks from a patient abdominal wall, to the same landmark measurements on the simulated model.

RESULTS

The calibration of simulation parameters resulted in a successful estimation of an optimal set parameters. A correspondence between the simulation pressure parameter and the experimental insufflation pressure was determined. The simulation of pneumoperitoneum in a porcine subject resulted in a mean Hausdorff distance error of 5-6 mm. Feasibility of modelling pneumoperitoneum in humans was successfully demonstrated.

CONCLUSION

Simulation of pneumoperitoneum provides an accurate subject-specific 3D model of the inflated abdomen, which is a more realistic representation of the intraoperative scenario when compared to preoperative imaging alone. The simulation results in a stable and interactive framework that performs in real time, and supports patient-specific data, which can assist in surgical planning.

Pneumoperitoneum simulation based on mass-spring-damper models for laparoscopic surgical planning

Laparoscopic surgery, which is one minimally invasive surgical technique that is now widely performed, is done by making a working space (pneumoperitoneum) by infusing carbon dioxide ([Formula: see text]) gas into the abdominal cavity. A virtual pneumoperitoneum method that simulates the abdominal wall and viscera motion by the pneumoperitoneum based on mass-spring-damper models (MSDMs) with mechanical properties is proposed. Our proposed method simulates the pneumoperitoneum based on MSDMs and Newton's equations of motion. The parameters of MSDMs are determined by the anatomical knowledge of the mechanical properties of human tissues. Virtual [Formula: see text] gas pressure is applied to the boundary surface of the abdominal cavity. The abdominal shapes after creation of the pneumoperitoneum are computed by solving the equations of motion. The mean position errors of our proposed method using 10 mmHg virtual gas pressure were [Formula: see text], and the position error of the previous method proposed by Kitasaka et al. was 35.6 mm. The differences in the errors were statistically significant ([Formula: see text], Student's [Formula: see text]-test). The position error of the proposed method was reduced from [Formula: see text] to [Formula: see text] using 30 mmHg virtual gas pressure. The proposed method simulated abdominal wall motion by infused gas pressure and generated deformed volumetric images from a preoperative volumetric image. Our method predicted abdominal wall deformation by just giving the [Formula: see text] gas pressure and the tissue properties. Measurement of the visceral displacement will be required to validate the visceral motion.

Towards cybernetic surgery: robotic and augmented reality-assisted liver segmentectomy

BACKGROUND

Augmented reality (AR) in surgery consists in the fusion of synthetic computer-generated images (3D virtual model) obtained from medical imaging preoperative workup and real-time patient images in order to visualize unapparent anatomical details. The 3D model could be used for a preoperative planning of the procedure. The potential of AR navigation as a tool to improve safety of the surgical dissection is outlined for robotic hepatectomy.

MATERIALS AND METHODS

Three patients underwent a fully robotic and AR-assisted hepatic segmentectomy. The 3D virtual anatomical model was obtained using a thoracoabdominal CT scan with a customary software (VR-RENDER®, IRCAD). The model was then processed using a VR-RENDER® plug-in application, the Virtual Surgical Planning (VSP®, IRCAD), to delineate surgical resection planes including the elective ligature of vascular structures. Deformations associated with pneumoperitoneum were also simulated. The virtual model was superimposed to the operative field. A computer scientist manually registered virtual and real images using a video mixer (MX 70; Panasonic, Secaucus, NJ) in real time.

RESULTS

Two totally robotic AR segmentectomy V and one segmentectomy VI were performed. AR allowed for the precise and safe recognition of all major vascular structures during the procedure. Total time required to obtain AR was 8 min (range 6-10 min). Each registration (alignment of the vascular anatomy) required a few seconds. Hepatic pedicle clamping was never performed. At the end of the procedure, the remnant liver was correctly vascularized. Resection margins were negative in all cases. The postoperative period was uneventful without perioperative transfusion.

CONCLUSIONS

AR is a valuable navigation tool which may enhance the ability to achieve safe surgical resection during robotic hepatectomy.

Abdominal wall extraction using constrained deformable model and abdominal context

Information about abdominal wall can be used for many applications from organ segmentation, registration, and surgical simulation. The challenges exist in abdominal wall extraction due to its varieties in shapes, connection to the internal organs and anterior layer edge formed between the muscle and fascia/fatty layer, which may distract the shape model. In this paper we present an approach to the posterior abdominal wall extraction using the shape model and other abdominal context, particularly with the rib-spine bone information and the wall image features. The shape model is constructed based on the training abdominal walls that are delineated manually. After bone information being extracted, the wall shape deforms from the prior shape model using the snake, which is constrained by the bone context and guided by the processed image energy map with the aim of removing distracted image features of anterior abdominal wall and the outer region from the original map. Meanwhile, an overall convex shape is maintained by limiting the angles of the contour points. The proposed approach is tested on abdominal CT data which provides encouraging results.