实现与CT相当的低剂量放疗计划图像质量：基于条件扩散的CBCT到CT合成及CBCT输入表示的影响
Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation
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中文总结 AI 辅助
本研究开发基于条件DDPM的CBCT到CT合成框架，探究CBCT输入表示对合成性能的影响，旨在实现低剂量放疗中与CT相当的图像质量。
中文摘要 AI 辅助
在标准放疗计划制定过程中，常需多次获取CT图像用于患者配准、验证和自适应计划制定，这会导致累积X射线剂量增加。为缓解该问题，治疗实施过程中常规采集低剂量锥形束CT（CBCT）。但由于散射、噪声、射线硬化及重建相关伪影增加，CBCT图像质量仍不足以支持准确的剂量计算和自适应放疗计划制定。本研究开发了一种基于监督深度学习的CBCT到CT合成框架，采用条件去噪扩散概率模型（DDPM），通过生成模型利用低剂量CBCT成像生成用于准确定位和剂量计算的CT类计划图像。除验证CBCT到CT合成的可行性外，本研究的主要目标是探究CBCT输入数据的两种表示形式——标准临床DICOM CBCT图像或原始投影数据经滤波反投影（FDK）重建得到的图像——对基于扩散的CT合成性能的影响。总体目标是评估在放疗流程中，物理感知的CBCT表示是否能在维持降低的成像剂量的同时，更好地支持与CT相当的图像质量。
英文摘要
During standard radiotherapy planning, repeated CT acquisitions are often required for patient registration, verification, and adaptive planning, resulting in increased cumulative X-ray dose. To mitigate this, low-dose cone-beam CT (CBCT) is routinely acquired during treatment delivery. However, CBCT image quality remains insufficient for accurate dose calculation and adaptive radiotherapy planning due to increased scatter, noise, beam hardening, and reconstruction related artifacts. This study develops a supervised deep learning based CBCT to CT synthesis framework using a conditional denoising diffusion probabilistic model (DDPM), where the generation of a CT-based planning for accurate positioning and dose calculation is obtained using generative models with low dose CBCT imaging. Beyond demonstrating CBCT to CT synthesis, the primary objective is to investigate how the representation of CBCT input data, either standard clinical DICOM CBCT images or filtered back-projection (FDK) reconstructions from raw projection data, affects the performance of diffusion based CT synthesis. The overarching aim is to assess whether physics aware CBCT representations better support CT-equivalent image quality while maintaining reduced imaging dose in radiotherapy workflows.
发表机构
- Jordan University of Science and Technology（约旦科技大学）
- University of Dundee（邓迪大学）
- NHS Ninewells Dundee（NHS邓迪奈恩韦尔斯医院）
- University of Glasgow（格拉斯哥大学）
机构由 AI 辅助整理，请以论文原文为准。
