用生成模型模拟患者特异性解剖变形，提升质子治疗计划评估准确性。
SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation
- 基于条件变分自编码器学习解剖形变潜在空间，生成局部速度场。
- 可扩展生成大视野CT数据的相干形变，支持内存受限环境。
- 适用于需个性化形变评估的放疗计划验证，尤其呼吸运动影响研究。
在质子治疗中，治疗计划通常基于单一规划CT优化，因此在解剖结构变化下的鲁棒性评估至关重要。然而，现有方法多依赖简化扰动，难以捕捉复杂且个体化的解剖变异。本文提出SynthRCT，一种可扩展的条件生成框架，用于3D解剖形变合成。该框架基于条件变分自编码器，学习解剖形变的潜在空间，并根据输入解剖结构解码出局部静止速度场。这些局部场被整合为整体一致的全体积形变，实现对大视野CT数据的内存高效生成。我们在每名受试者多个呼吸相位的呼吸4DCT数据上验证了该方法的有效性，结果表明SynthRCT能够生成超出预设鲁棒性场景的、符合患者特性的合理解剖形变。代码已开源：https://github.com/TomasGuija/SynthRCT。
原文摘要 · Abstract (English)
In proton therapy, plans are typically optimized on a single planning CT, making robustness evaluation essential under anatomical changes. However, current scenarios often rely on simplified perturbations that poorly capture complex, patient-specific variability. We propose SynthRCT, a scalable conditional generative framework for 3D anatomical deformation synthesis. Based on a conditional variational autoencoder, SynthRCT learns a latent deformation space and decodes sampled latent codes into local stationary velocity fields conditioned on an input anatomy. Local fields are assembled into coherent full-volume transformations, enabling memory-scalable generation for large field-of-view CT data. We validate the approach on respiratory 4DCT data with multiple breathing-phase anatomies per subject. SynthRCT enables patient-specific sampling of plausible anatomical transformations beyond predefined robustness scenarios. Code available at: https://github.com/TomasGuija/SynthRCT.
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