Reconstruction Framework

Reconstruction frameworks encompass diverse computational methods aiming to create complete and accurate representations from incomplete or noisy data. Current research focuses on improving efficiency and robustness, particularly through the integration of deep learning architectures like neural radiance fields and neural ordinary/delay differential equations, often coupled with techniques such as active learning and motion correction. These advancements are significantly impacting fields ranging from 3D modeling and medical imaging (e.g., accelerated MRI and PET) to natural language processing applications like cognitive distortion analysis, enabling faster, higher-quality reconstructions and improved diagnostic capabilities.