SynthRCT：用于合成重复CT生成的可扩展条件形变合成
SynthRCT: Scalable Conditional Deformation Synthesis for Synthetic Repeat CT Generation
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中文总结 AI 辅助
SynthRCT提出可扩展条件生成框架，基于条件变分自编码器合成3D解剖形变，用于质子治疗稳健性评估，实现患者特有的合理变换采样。
中文摘要 AI 辅助
在质子治疗中，治疗计划通常基于单一的计划CT进行优化，因此评估解剖变化下的稳健性至关重要。然而，当前的情景往往依赖于简化的扰动，这些扰动难以捕捉复杂且患者特有的变异性。我们提出SynthRCT，一个用于三维解剖形变合成的可扩展条件生成框架。基于条件变分自编码器，SynthRCT学习潜在形变空间，并将采样的潜在编码解码为以输入解剖为条件的局部平稳速度场。局部场被组装成连贯的全容积变换，从而实现对大数据视野CT的可扩展内存生成。我们在呼吸4DCT数据上验证了该方法，每个受试者包含多个呼吸相位解剖。SynthRCT能够在预定义稳健性情景之外，实现患者特有的合理解剖变换采样。代码可在以下网址获取：此https URL。
英文摘要
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.
发表机构
- Universidad Rey Juan Carlos（胡安·卡洛斯国王大学）
机构由 AI 辅助整理，请以论文原文为准。
