GPU‐based efficient realistic techniques for bleeding and smoke generation in surgical simulators

Abstract

Background In actual surgery, smoke and bleeding due to cautery processes, provide important visual cues to the surgeon which have been proposed as factors in surgical skill assessment. While several virtual reality (VR)-based surgical simulators have incorporated effects of bleeding and smoke generation, they are not realistic due to the requirement of real time performance. To be interactive, visual update must be performed at least 30 Hz and haptic (touch) information must be refreshed at 1 kHz. Simulation of smoke and bleeding is, therefore, either ignored or simulated using highly simplified techniques since other computationally intensive processes compete for the available CPU resources. Methods In this work, we develop a novel low-cost method to generate realistic bleeding and smoke in VR-based surgical simulators which outsources the computations to the graphical processing unit (GPU), thus freeing up the CPU for other time-critical tasks. This method is independent of the complexity of the organ models in the virtual environment. User studies were performed using 20 subjects to determine the visual quality of the simulations compared to real surgical videos. Results The smoke and bleeding simulation were implemented as part of a Laparoscopic Adjustable Gastric Banding (LAGB) simulator. For the bleeding simulation, the original implementation using the shader did not incur in noticeable overhead. However, for smoke generation, an I/O (Input/Output) bottleneck was observed and two different methods were developed to overcome this limitation. Based on our benchmark results, a buffered approach performed better than a pipelined approach and could support up to 15 video streams in real time. Human subject studies showed that the visual realism of the simulations were as good as in real surgery (median rating of 4 on a 5-point Likert scale). Conclusions Based on the performance results and subject study, both bleeding and smoke simulations were concluded to be efficient, highly realistic and well suited in VR-based surgical simulators.

Developing an Anterior Cervical Diskectomy and Fusion Simulator for Neurosurgical Resident Training

“…Multiple reports on neurosurgery simulation models have provided promising 28-31 evidence that repetitive exposure and learned psychomotor skills reliably transfer to improve the workflow for real surgical procedures. 32 Previous spine surgical simulators have focused largely on techniques for posterior thoracolumbar spinal instrumentation. 6,8 The 2012 CNS Annual Meeting was the first pilot implementation and assessment of an ACDF training module as part of the CNS simulation course curriculum.…”

Appearance Modelling of Living Human Tissues

“…It seems, however, that biophysics‐based models of living internal human parts will attract a wider audience in health applications. Today's functional models already impact the way surgeons practice and plan surgery [BCHSS99, HSD10, BMPG11, HWCH12, BPBG13, UF15, BTM17]. Virtual patients are an alternative to cadaver or animal models for the learning of anatomy and surgical techniques.…”

“…Basdogan [BCHSS99], for instance, used Navier–Stokes equations to deform a continuous polyhedral surface that is drawn with a red colour over the anatomy to simulate bleeding in real time. Besides, blood often played a secondary role in surgery simulations, where any red fluid is enough to convey the presence of blood and enrich the visual of a scalpel cutting through flesh [HSD10, BMPG11, BPBG13, UF15, BTM17]. In realistic rendering, models of light interaction with human skin sometimes account for the presence of blood through the incorporation of absorption coefficients of blood‐borne pigments [DJ06, BK10].…”

Real-Time Simulation of Bleeding and Smoke Effects in Virtual Reality Environment

“…For image-based methods, [6] used texture mapping techniques and sprites as blood droplets to simulate blood flow down the surface of the organ, and [7] painted the blood trails as two dimensional textured lines along the organ model. Subsequently, [8] computed the intensity of the blood flow at each vertex node, and used the intensity value to shade the surface of the nearby polygon accordingly. More recently, [9] computed the blood flow in the texture space before mapping it back for rendering.…”