Volume Reconstruction

Volume reconstruction aims to create three-dimensional models from various two-dimensional or incomplete data sources, such as X-ray images, ultrasound scans, or cryo-EM micrographs. Current research emphasizes deep learning approaches, including generative models (e.g., score-based models, GANs) and multi-task learning frameworks, often incorporating auxiliary information like anatomical features or scanning protocols to improve accuracy and robustness. These advancements have significant implications for medical imaging (e.g., improved diagnostics and treatment planning), materials science (e.g., high-resolution 3D structural analysis), and biological research (e.g., detailed molecular structure determination).