Publications

This project utilizes Graphics Processing Units (GPUs) to compute radiograph simulations for arbitrary objects. The generation of radiographs, also known as the forward projection imaging model, is computationally intensive and not widely utilized. The goal of this research is to develop a massively parallel algorithm that can compute forward projections for objects with a trillion voxels (3D pixels). To achieve this end, the data are divided into blocks that can each t into GPU memory. The forward projected image is also divided into segments to allow for future parallelization and to avoid needless computations.

Abstract not provided.

This work describes a high-performance approach to radiograph (i.e. X-ray image for this work) simulation for arbitrary objects. The generation of radiographs is more generally known as the forward projection imaging model. The formation of radiographs is very computationally expensive and is not typically approached for large-scale applications such as industrial radiography. The approach described in this work revolves around a single GPU-based implementation that performs the attenuation calculation in a massively parallel environment. Additionally, further performance gains are realized by exploiting the GPU-specific hardware. Early results show that using a single GPU can increase computational performance by three orders-of- magnitude for volumes of 10003 voxels and images with 10002 pixels.