用扩散模型将低剂量CBCT转为等效CT，提升放疗规划精度。
Toward CT-Equivalent Image Quality in Low-Dose Radiotherapy Planning: Conditional Diffusion-Based CBCT-to-CT Synthesis and the Impact of CBCT Input Representation
- 采用条件去噪扩散模型，从低剂量CBCT生成CT图像。
- 使用原始投影数据重建的FDK图像作为输入，生成效果更优。
- 为放疗中降低辐射剂量提供高保真图像合成方案，适合医学影像研究者。
标准放疗规划中，为实现患者定位、验证与自适应规划，常需多次进行CT扫描，导致累积辐射剂量增加。为缓解此问题，治疗过程中通常采用低剂量锥形束CT（CBCT）成像。然而，由于散射、噪声、束硬化及重建伪影等问题，现有CBCT图像质量仍不足以支持精确的剂量计算和自适应放疗规划。本研究提出一种基于监督深度学习的CBCT到CT图像合成框架，采用条件去噪扩散概率模型（DDPM），利用低剂量CBCT生成可用于精准定位与剂量计算的CT图像。核心目标是探究输入表示方式对扩散模型性能的影响：比较临床标准DICOM格式的CBCT图像与基于原始投影数据的滤波反投影（FDK）重建图像的效果。结果表明，物理感知的输入表示能显著提升生成图像质量，实现接近真实CT的图像表现，同时保持低剂量优势，适用于临床放疗流程中的图像重建优化。
原文摘要 · Abstract (English)
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.
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