Accelerating volume ray casting by empty space skipping used for Computer-Aided Therapy

Abstract

Volume ray casting (VRC) is one of image-based Direct Volume Rendering (DVR) techniques, a powerful tool for visualizing scalar data of three spatial dimensions, and can provide necessary visual perspective effect for volume data like CT or MRI. But the computational complexity becomes a bottle-neck for its application in Computer-Aided Therapy (CAT). Whatever, a parallel computing architecture, like Single-Instruction-Multiple-Data (SIMD), should be a good platform for implementing VRC. And yet, the computational speed depends on dataset, especially on the ratio of nonempty voxels and empty voxels which are defined by opacity value. This paper proposes an empty space skipping technique based on GPGPU for accelerating VRC. It includes two better strategies compared with other techniques: the encoding based pre-sampling from texture memory and disregard the empty voxels for reducing the generation of bounding box. The performance testing in term of frame per second, are done both on 4 general testing datasets and 2 brain tumor patients' datasets. The results show that the proposed ameliorative strategies contribute about 2 times speedup compared with the non-skipping VRC on CUDA.