Takahide Ohkami

Simulating arthroscopic knee surgery using volumetric object representations, real-time volume rendering and haptic feedback

A system for simulating arthroscopic knee surgery that is based on volumetric object models derived from 3D Magnetic Resonance Imaging is presented. Feedback is provided to the user via real-time volume rendering and force feedback for haptic exploration. The system is the result of a unique collaboration between an industrial research laboratory, two major universities, and a leading research hospital. In this paper, components of the system are detailed and the current state of the integrated system is presented. Issues related to future research and plans for expanding the current system are discussed.

EM-Cube: an architecture for low-cost real-time volume rendering

EM-Cube is a VLSI architecture for low-cost, high quality volume rendering at full video frame rates. Derived from the Cube4 architecture developed at SUNY at Stony Brook, EM-Cube computes sample points and gradients on-the-fly to project 3-dimensional volume dnta onto 2-dimensional images with realistic lighting and shading. A modest rendering system based on EM-Cube consists of a PC1 card with four rendering chips (ASICs), four 64Mbit SDRAMs to hold the volume data, and four SRAMs to capture the rendered image. The performance target for this configuration is to render images from a 25G3 x 16 bit data set at 30 fmmes/sec. The EM-Cube architecture can be scaled to larger volume data-sets and/or higher frame rates by adding additional ASKS, SDRAMs, and SRAMs. This paper addresses three major challenges encountered developing EM-Cube into a pm&al product: exploiting the bandwidth inherent in the SDRAMs containing the volume data, keeping the pin-count between adjacent ASICs at a tractable level, and reducing the on-chip stomge required to hold the intermediate results of rendering.

A real-time volume rendering architecture using an adaptive resampling scheme for parallel and perspective projections

The paper describes an object order real time volume rendering architecture using an adaptive resampling scheme to perform resampling operations in a unified parallel pipeline manner for both parallel and perspective projections. Unlike parallel projections, perspective projections require a variable resampling structure due to diverging perspective rays. In order to address this issue, we propose an adaptive pipelined convolution block for resampling operations using the level of resolution to keep the parallel pipeline structure regular. We also propose to use multi resolution datasets prepared for different levels of grid resolution to bound the convolution operations. The proposed convolution block is organized using a systolic array structure, which works well with a distributed skewed memory for conflict free accesses of voxels. We present the results of some experiments with our software simulators of the proposed architecture and discuss important technical issues.