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In this paper, we report a result of an experiment of a field trial of our network-based minimally invasive surgery simulator. In our previous paper, we proposed a network-based visuohaptic surgery training system for laparoscopic surgery. In addition, we proposed a volume-based haptic communication approach, which allows participants at remote sites on the network to simultaneously interact with the same target object in virtual environments presented by multi-level computer performance systems, by only exchanging a small set of manipulation parameters for the target object and additional packet for synchronization of status of binary tree and deformation of shared volume model. We implemented the approach into our network-based surgery simulator, and field trial of the simulator at three locations was performed.

Laparoscopic surgery has become a widely performed surgery as it is one of the most common minimally invasive surgeries. Doctors perform the surgery by manipulating thin and long surgical instruments precisely with the assistance of laparoscopic video with limited field of view. The power control of the instruments' tip is especially very important, because excessive power may damage internal organs. The training of this surgical technique is mainly supervised by an expert in hands-on coaching program. However, it is difficult for the experts to spend sufficient time for coaching. Therefore, we aim to teach the expert's hand movements in laparoscopic surgery to trainees using VR-based simulator, which is equipped with a guidance force display device. To realize the system, we propose two haptic training approaches for transferring the expert's hand movements to the trainee. One is active training, and the other is passive training. The former approach shows the expert's movements only when the trainee makes large errors while the latter shows the expert's movements continuously. In this study, we validate the applicability of these approaches through tasks in VR laparoscopic surgery training simulator, and identify the differences between these approaches.

Background: Our goal was to develop a three-dimensional finite element model that enables dynamic analysis of needle insertion for soft materials. To demonstrate large deformation and fracture, we used the arbitrary Lagrangian-Eulerian (ALE) method for fluid analysis. We performed ALE-based finite element analysis for 3% agar gel and three types of copper needle with bevel tips., Methods: To evaluate simulation results, we compared the needle deflection and insertion force with corresponding experimental results acquired with a uniaxial manipulator. We studied the shear stress distribution of agar gel on various time scales., Results: For 30°, 45°, and 60°, differences in deflections of each needle between both sets of results were 2.424, 2.981, and 3.737mm, respectively. For the insertion force, there was no significant difference for mismatching area error (p<0.05) between simulation and experimental results., Conclusions: Our results have the potential to be a stepping stone to develop pre-operative surgical planning to estimate an optimal needle insertion path for MR image-guided microwave coagulation therapy and for analyzing large deformation and fracture in biological tissues., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

In general, minimally invasive surgery is seen as the most difficult surgery because there is limited field of view with an endoscope and force sensation from surgical instruments such as forceps is poor. Especially in early clinical education for medical students, a virtual reality surgical simulator would be an effective tool. In this paper, we propose a visuohaptic surgery training system for laparoscopical techniques. We recorded a video from a first person point of view of the instructor (expert). And we also recorded operation information (i.e. trajectory) of surgical instruments of the instructor. Then, we displayed the recorded video and the guidance force to trainees. We constructed a prototype surgery training system and the effectiveness of our approach was confirmed.

A hybrid dynamic deformation model that is capable of haptic interaction with dynamically deformable objects is presented. This approach is also capable of taking into account large deformation and topological changes of objects. The amount of computation required in this approach is proportional to the sum of the square of the number of shared nodes and the number of whole nodes of an online-remesh model. We implemented the approach in our prototype system and confirmed the feasibility and performance through experiments.