Dual Energy

Dual-energy computed tomography (DECT) aims to improve medical imaging by providing quantitative material composition information, surpassing the capabilities of conventional single-energy CT. Current research focuses on improving DECT accuracy and efficiency, particularly addressing temporal inconsistencies in sequential scanning and developing advanced reconstruction techniques, including deep learning models (e.g., convolutional neural networks, unrolled networks) and novel algorithms leveraging implicit neural representations. These advancements enhance material decomposition, reduce artifacts (like metal artifacts), and enable the generation of valuable parametric maps from single-energy data, ultimately leading to more precise diagnoses and personalized treatment planning.