GPU Based Physical Cut in Interactive Haptic Simulations

PURPOSE: Interactive, physics based, simulations of deformable bodies are a growing research area with possible applications to computer-aided surgery. Their aim is to create virtual environments where surgeons are free to practice. To ensure the needed realism, the simulations must be performed with deformable bodies. The goal of this paper is to describe the approach to the development of a physics-based surgical simulator with haptic feedback. METHOD:The main development issue is the representation of the organ behavior at the high rates required by haptic realism. Since even high-end computers have inadequate performance, our approach exploits the parallelism of modern Graphics Processing Units (GPU). Particular attention is paid to the simulation of cuts because of their great importance in the surgical practice and the difficulty in handling topological changes in real time. RESULTS: To prove the correctness of our approach, we simulated an interactive, physically based, virtual abdomen. The simulation allows the user to interact with deformable models. Deformable models are updated in real time, thus allowing the rendering of force feedback to the user. The method is optimized to handle high quality scenes: we report results of interactive simulation of two virtual tools interacting with a complex model. CONCLUSIONS: The integration of physics-based deformable models in simulations greatly increases the realism of the virtual environment, taking into account real tissue properties and allowing the user to feel the actual forces exerted by organs on virtual tools. Our method proves the feasibility of exploiting GPU to simulate deformable models in interactive virtual environments

A 120-dB dynamic range CMOS image sensor with programmable power responsivity

A high dynamic range CMOS image sensor providing a user-programmable power responsivity curve is presented. Each pixel integrates, besides a 4T active pixel structure, a voltage comparator and an analog memory to implement a time-to-saturation scheme while also providing the standard integrated photo-current signal. The sensor generates two 10-bit analog outputs allowing a typical dynamic range exceeding 120 dB with a temporal noise lower than 0.13% and a fixed pattern noise of 0.4% (1.7%) of the total signal swing (2 V) at low (high) irradiance without any external calibration procedures. A 140 140-pixel array has been fabricated in a 0.35- m, two-poly four-metal (2P4M), 3.3-V standard CMOS technology. The chip measures 3.9 4.6 mm2 with a pixel pitch of 15 m and a fill factor of 20%

Neural Spike Digital Detector on FPGA

This paper presents a multidisciplinary experiment where a population of neurons, dissociated from rat hippocampi, has been cultivated over a CMOS-based micro-electrode array (MEA) and its electrical activity has been detected and mapped by an advanced spike-sorting algorithm implemented on FPGA. MEAs are characterized by low signal-to-noise ratios caused by both the contactless sensing of weak extracellular voltages and the high noise power coming from cells and analog electronics signal processing. This low SNR forces to utilize advanced noise rejection algorithms to separate relevant neural activity from noise, which are usually implemented via software/off-line. However, off-line detection of neural spikes cannot be obviously used for real-time electrical stimulation. In this scenario, this paper presents a proper FPGA-based system capable to detect in real-time neural spikes from background noise. The output signals of the proposed system provide real-time spatial and temporal information about the culture electrical activity and the noise power distribution with a minimum latency of 165 ns. The output bit-stream can be further utilized to detect synchronous activity within the neural network