Artificial Intelligence Augmented Medical Imaging Reconstruction in Radiation Therapy

Di Xu
4/10/2025
eess.IVcs.AI

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Cite this paper

@article{xu2025artificial,
  title  = {Artificial Intelligence Augmented Medical Imaging Reconstruction in Radiation Therapy},
  author = {Di Xu},
  year   = {2025},
  eprint = {2504.08844},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2504.08844v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2504.08844.svg)](https://codeofpaper.com/paper/2504.08844)

Discussion