The evaluation and trial of computer-assisted surgery systems is an important part of the development process. Since human and animal trials are difficult to perform and have a high ethical value artificial organs and phantoms have become a key component for testing clinical systems. For soft-tissue phantoms like the liver it is important to match its biomechanical properties as close as possible. Organ phantoms are often created from silicone that is shaped in casting molds. Silicone is relatively cheap and the method doesn’t rely on expensive equipment. One big disadvantage of silicone phantoms is their high rigidity. To this end, we propose a new method for the generation of silicon phantoms with a softer and mechanically more accurate structure. Since we can’t change the rigidity of silicone we developed a new and easy method to weaken the structure of the silicone phantom. The key component is the misappropriation of water-soluble support material from 3D FDM-printing. We designed casting molds with an internal grid structure to reduce the rigidity of the structure. The molds are printed with an FDM (Fused Deposition Modeling) printer and entirely from water-soluble PVA (Polyvinyl Alcohol) material. After the silicone is hardened, the mold with the internal structure can be dissolved in water. The silicone phantom is then pervaded with a grid of cavities. Our experiments have shown that we can control the rigidity of the model up to a 70% reduction of its original value. The rigidity of our silicon models is simply controlled with the size of the internal grid structure.
Proc. SPIE. 9784, Medical Imaging 2016: Image Processing
KEYWORDS: Endoscopy, 3D acquisition, Data modeling, Surgery, Imaging systems, Cameras, 3D modeling, Image registration, Endoscopes, Personal digital assistants, Stereoscopic cameras, Augmented reality, Filtering (signal processing)
The number of minimally invasive procedures is growing every year. These procedures are highly complex and very demanding for the surgeons. It is therefore important to provide intraoperative assistance to alleviate these difficulties. For most computer-assistance systems, like visualizing target structures with augmented reality, a registration step is required to map preoperative data (e.g. CT images) to the ongoing intraoperative scene. Without additional hardware, the (stereo-) endoscope is the prime intraoperative data source and with it, stereo reconstruction methods can be used to obtain 3D models from target structures. To link reconstructed parts from different frames (mosaicking), the endoscope movement has to be known. In this paper, we present a camera tracking method that uses dense depth and feature registration which are combined with a Kalman Filter scheme. It provides a robust position estimation that shows promising results in ex vivo and in silico experiments.
The goal of computer-assisted surgery is to provide the surgeon with guidance during an intervention, e.g., using augmented reality. To display preoperative data, soft tissue deformations that occur during surgery have to be taken into consideration. Laparoscopic sensors, such as stereo endoscopes, can be used to create a three-dimensional reconstruction of stereo frames for registration. Due to the small field of view and the homogeneous structure of tissue, reconstructing just one frame, in general, will not provide enough detail to register preoperative data, since every frame only contains a part of an organ surface. A correct assignment to the preoperative model is possible only if the patch geometry can be unambiguously matched to a part of the preoperative surface. We propose and evaluate a system that combines multiple smaller reconstructions from different viewpoints to segment and reconstruct a large model of an organ. Using graphics processing unit-based methods, we achieved four frames per second. We evaluated the system with in silico, phantom, ex vivo, and in vivo (porcine) data, using different methods for estimating the camera pose (optical tracking, iterative closest point, and a combination). The results indicate that the proposed method is promising for on-the-fly organ reconstruction and registration.
The goal of computer-assisted surgery is to provide the surgeon with guidance during an intervention using augmented reality (AR). To display preoperative data correctly, soft tissue deformations that occur during surgery have to be taken into consideration. Optical laparoscopic sensors, such as stereo endoscopes, can produce a 3D reconstruction of single stereo frames for registration. Due to the small field of view and the homogeneous structure of tissue, reconstructing just a single frame in general will not provide enough detail to register and update preoperative data due to ambiguities. In this paper, we propose and evaluate a system that combines multiple smaller reconstructions from different viewpoints to segment and reconstruct a large model of an organ. By using GPU-based methods we achieve near real-time performance. We evaluated the system on an ex-vivo porcine liver (4.21mm± 0.63) and on two synthetic silicone livers (3.64mm ± 0.31 and 1.89mm ± 0.19) using three different methods for estimating the camera pose (no tracking, optical tracking and a combination).