Development of a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport

Monte Carlo simulation is the most accurate method for absorbed dose calculations in radiotherapy. Its efficiency still requires improvement for routine clinical applications, especially for online adaptive radiotherapy. In this paper, we report our recent development on a GPU-based Monte Carlo dose calculation code for coupled electron-photon transport. We have implemented the Dose Planning Method (DPM) Monte Carlo dose calculation package (Sempau et al, Phys. Med. Biol., 45(2000)2263-2291) on GPU architecture under CUDA platform. The implementation has been tested with respect to the original sequential DPM code on CPU in phantoms with water-lung-water or water-bone-water slab geometry. A 20 MeV mono-energetic electron point source or a 6 MV photon point source is used in our validation. The results demonstrate adequate accuracy of our GPU implementation for both electron and photon beams in radiotherapy energy range. Speed up factors of about 5.0 ~ 6.6 times have been observed, using an NVIDIA Tesla C1060 GPU card against a 2.27GHz Intel Xeon CPU processor.Comment: 13 pages, 3 figures, and 1 table. Paper revised. Figures update

Virtual 16 Bit Precise Operations on RGBA8 Textures

There is a growing demand for high precision texture formats fed by the increasing number of textures per pixel and multi-pass algorithms in dynamic texturing and visualization. Therefore support for wider data formats in graphics hardware is evolving. The existing functionality of current graphics cards, however, can already be used to provide higher precision textures. This paper shows how to emulate a 16 bit precise signed format by use of RGBA8 textures and existing shader and register operations. Thereby a 16 bit number is stored in two unsigned 8 bit color channels. The focus lies on a 16 bit signed number format which generalizes existing 8 bit formats allowing lossless format expansions, and which has an exact representation of 1, 0 and allowing stable long-lasting dynamic texture updates. Implementations of basic arithmetic operations and dependent texture loop-ups in this format are presented and example algorithms dealing with 16 bit precise dynamic updates of displacement maps, normal textures and filters demonstrate some of the resulting application areas

Image Registration by a Regularized Gradient Flow A Streaming Implementation in DX9 Graphics Hardware

The presented image registration method uses a regularized gradient flow to correlate the intensities in two images. Thereby, an energy functional is successively minimized by descending along its regularized gradient. The gradient flow formulation makes use of a robust multi-scale regularization, an e#cient multi-grid solver and an e#ective time-step control