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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: We sought to evaluate a new regional segmentation method for use with three-dimensional (3D) non-contrast abdominal CT images and to report the preliminary results., Methods: The proposed method was evaluated in ten cases. Manually segmented areas were used as the gold standard for evaluation. To compare the standard and the extracted liver regions, the degree of coincidence R% was redefined by transforming a volumetric overlap error. We also evaluated the influence of varying the density window size in terms of setting the starting points., Results: We confirmed in ten cases that our method could segment the liver region more precisely than the conventional method. A size of window 15 voxels was optimal as the starting point in all cases., Conclusions: We demonstrated the accuracy of a 3D semiautomatic liver segmentation method for non-contrast CT. This method promises to offer radiologists a time-efficient segmentation aid., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

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 this paper, we propose a rectangular tetrahedral adaptive mesh based corotated finite element model for interactive soft tissue simulation. Our approach consists of several computation reduction techniques. They are as follows: 1) an efficient calculation approach for computing internal forces of nodes of elastic objects to take advantage of the rectangularity of the tetrahedral adaptive mesh; 2) fast shape matching approach by using a new scaling of polar decomposition; 3) an approach for the reduction of the number of times of shape matching by using the hierarchical structure. We implemented the approach into our surgery simulator and compared the accuracy of the deformation and the computation time among 1) proposed approach, 2) L-FE), and 3) NL-FEM. Finally, we show the effectiveness of our proposed approach.

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.

In this paper, we performed needle insertion simulation considering needle tip shape by Arbitrary Lagrangian-Eulerian (ALE) method. ALE method is suitable for the large deformation and a fracture. To evaluate developed model, we compared the needle deflection between experimental results and simulation results. As a result, errors in each needle between both results were less than 3 mm.

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.

We have developed a novel volume-based haptic communication system. It allows participants at remote sites on a network to simultaneously interact with the same target object in virtual environments presented by multi-level computer performance systems. It does this by only exchanging a small set of manipulation parameters for the target object and an additional packet to synchronize the status of the binary tree and the deformation of the local region of the shared volume model. We first developed online-remesh volume models, which we call dynamic adaptive grids, to simulate deformable objects such as soft tissues at remote sites. Then, haptic sensation during interaction with the target object was achieved by rendering the reflection force from the object, which was simulated with the online-remesh volume model from the manipulation parameters and additional packets exchanged among all remote sites. We investigated the efficiency of our system via experiments at remote locations on a WAN between Tokyo, Osaka, and Kyoto.