Di Xu

Di Xu

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.

Di Xu, Zhen Li, Yanning Zhang et al.

This paper presents an illumination estimation method for virtual objects in real environment by learning. While previous works tackled this problem by reconstructing high dynamic range (HDR) environment maps or the corresponding spherical harmonics, we do not seek to recover the lighting environment of the entire scene. Given a single RGB image, our method directly infers the relit virtual object by transferring the illumination features extracted from planar surfaces in the scene to the desired geometries. Compared to previous works, our approach is more robust as it works in both indoor and outdoor environments with spatially-varying illumination. Experiments and evaluation results show that our approach outperforms the state-of-the-art quantitatively and qualitatively, achieving realistic augmented experience.