AI提升放疗影像重建质量与速度，加速CT/MRI获取
Artificial Intelligence Augmented Medical Imaging Reconstruction in Radiation Therapy
- 用AI框架优化CT重建质量与速度，提升成像效率
- 改进双能CT多物质分解精度，增强组织区分能力
- 显著加快4D MRI采集，适合放疗实时成像需求
高效获取与精准重建影像对现代放射治疗的成功至关重要。计算机断层扫描（CT）和磁共振成像（MRI）是提供放疗计划制定与实施引导/监测的常用模态。近几十年来，人工智能（AI）作为一种强大且广泛应用的技术，在多个领域展现出优势，其通过隐式函数定义和数据驱动特征表示学习实现高效便捷。本文提出一系列基于AI的医学影像重建框架，旨在提升放疗中的CT图像重建质量与速度，优化双能CT（DECT）多物质分解（MMD）性能，并显著加速4D MRI的采集过程。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。