Sinogram Inpainting with Physics-Guided Latent Diffusion Model for Synchrotron Light Sources
Srutarshi Banerjee, E Jiaze, Bin Ren, Tekin Biçer · 2025
X-ray Computed Tomography (XCT) is widely used for imaging materials at microscopic or sub-microscopic lengths in synchrotrons. During experiments, often limited XCT data is collected, compromising the reconstruction quality. Here, we develop a foundation model for XCT with inpainting as downstream task. Our model integrates a Generative AI-based Latent Diffusion Model (LDM) with physics domain knowledge. We incorporate additional loss functions into the autoencoder of the LDM to accurately capture the physical properties of the XCT data acquisition. This loss function and a pre-training step improve the autoencoder’s performance. Lastly, we introduce a novel image blending method to combine the LDM’s output with the original, extremely sparse sinogram data. On real-world test dataset, with 80% randomly masked data, we demonstrate mean SSIM of 0.8699 and 0.7290 for sinogram and reconstructed object respectively. Additionally, we obtain mean PSNR of 36.13 dB and 32.26 dB for sinogram and reconstructed object respectively.