Fast digitally reconstructed radiograph generation using particle-based statistical shape and intensity model.

Purpose: Statistical shape and intensity models (SSIMs) and digitally reconstructed radiographs (DRRs) were introduced for non-rigid 2D-3D registration and skeletal geometry/density reconstruction studies. The computation of DRRs takes most of the time during registration or reconstruction. The goal of this study is to propose a particle-based method for composing an SSIM and a DRR image generation scheme and analyze the quality of the images compared with previous DRR generation methods. Approach: Particle-based SSIMs consist of densely scattered particles on the surface and inside of an object, with each particle having an intensity value. Generating the DRR resembles ray tracing, which counts the particles that are binned with each ray and calculates the radiation attenuation. The distance between adjacent particles was considered to be the radiologic path during attenuation integration, and the mean linear attenuation coefficient of the two particles was multiplied. The proposed method was compared with the DRR of CT projection. The mean squared error and peak signal-to-noise ratio (PSNR) were calculated between the DRR images from the proposed method and those of existing methods of projecting tetrahedral-based SSIMs or computed tomography (CT) images to verify the accuracy of the proposed scheme. Results: The suggested method was about 600 times faster than the tetrahedral-based SSIM without using the hardware acceleration technique. The PSNR was 37.59 dB, and the root mean squared error of the normalized pixel intensities was 0.0136. Conclusions: The proposed SSIM and DRR generation procedure showed high temporal performance while maintaining image quality, and particle-based SSIM is a feasible form for representing a 3D volume and generating the DRR images.