Material Decomposition

Material decomposition aims to separate and identify individual materials within complex objects from imaging data, primarily focusing on X-ray computed tomography (CT) and other spectral imaging modalities. Current research emphasizes developing advanced deep learning models, including neural networks incorporating physical models of light and material interaction, to improve the accuracy and efficiency of material separation, often employing techniques like diffusion posterior sampling and vector quantization for enhanced performance. These advancements hold significant promise for improving medical imaging diagnostics (e.g., identifying tissue types in CT scans) and advancing 3D scene reconstruction in computer graphics and related fields by enabling more realistic and detailed material representation.