Abstract
Compton scatter tomography (CST) is an imaging technique that utilizes scattered x-ray radiation for 3D reconstruction of the electron density (ρe), which is otherwise not directly possible with transmission computed tomography (CT), thus providing additional information for material differentiation. In this study, we considered a CST scanning mechanism where a cone-beam source and an angularly-selective flat detector rotate in tandem around the sample, and applied a deep-learning diffusion model to reconstruct the electron density of the sample, with the 3D reconstruction performed slice-by-slice. This scanning mechanism could be readily implemented with current technologies for image-guided radiation therapy. To facilitate deep learning studies, 13,010 pairs of ground-truth 2D slice images of electron density and the corresponding detector readings were generated through numerical experiments. A conditional diffusion model was trained on a data subset to predict the velocity signal over a cosine noise schedule. We employed a U-Net architecture for the diffusion model, augmented with ConvNeXt blocks, and applied DDIM sampling over 100 timesteps. Evaluation on a testing data subset demonstrated mean PSNR ≈37.1 dB, SSIM ≈0.899, and RMSE ≈0.0160, with similar performance (2 to 7% deviation for all metrics) when evaluated under Poisson-noised detector readings.