Artificial Intelligence Augmented Medical Imaging Reconstruction in Radiation Therapy
This work addresses the need for more efficient and precise imaging in radiation therapy, which is crucial for treatment planning and monitoring, but it appears incremental as it applies existing AI techniques to specific medical imaging tasks.
The paper tackles the problem of improving medical imaging reconstruction for radiation therapy by presenting AI-driven frameworks that enhance CT image quality and speed, refine dual-energy CT multi-material decomposition, and accelerate 4D MRI acquisition, with results including significant acceleration.
Efficiently acquired and precisely reconstructed imaging are crucial to the success of modern radiation therapy (RT). Computed tomography (CT) and magnetic resonance imaging (MRI) are two common modalities for providing RT treatment planning and delivery guidance/monitoring. In recent decades, artificial intelligence (AI) has emerged as a powerful and widely adopted technique across various fields, valued for its efficiency and convenience enabled by implicit function definition and data-driven feature representation learning. Here, we present a series of AI-driven medical imaging reconstruction frameworks for enhanced radiotherapy, designed to improve CT image reconstruction quality and speed, refine dual-energy CT (DECT) multi-material decomposition (MMD), and significantly accelerate 4D MRI acquisition.