发表机构
Univ. Rennes, CLCC Eugène Marquis, INSERM, LTSI - UMR 1099（雷恩大学，欧仁·马基斯癌症中心，法国国家健康与医学研究院，LTSI - UMR 1099）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
TotalSynth提出可复用的预训练框架，从MRI和CBCT生成全身合成CT，在内部数据上达到低MAE和高SSIM，外部数据验证了域偏移下微调的必要性。
AI 中文摘要
目的：开发并评估TotalSynth，一个可复用的预训练框架，用于从MRI和锥形束CT（CBCT）图像生成全身合成CT（sCT）。材料与方法：在这项回顾性技术研究中，数据集于2020年至2026年间从SynthRAD挑战数据、四个前列腺队列和BIC-MAC中收集。经过配准质量控制后，1800对公共挑战数据中保留了1450对；350对因配准质量不足或源图像/CT严重不匹配而被排除。语料库还包括84对额外的前列腺MRI/CT和60对外部BIC-MAC MRI/CT病例。使用图像域、解剖感知、基于配准和不确定性指标评估了三个5折模型族。年龄和性别在公共数据集中并非始终可用。结果：发布的MRI到CT模型实现了总体MAE为67.49 HU，SSIM为0.920，PSNR为29.28 dB。CBCT到CT模型实现了总体MAE为53.55 HU，SSIM为0.939，PSNR为32.09 dB。统一模型在MRI输入（MAE，67.68 HU）和CBCT输入（MAE，54.22 HU）上保持了相似的性能。在外部BIC-MAC数据上，MRI到CT的MAE在未微调时为100.91 HU，微调后为62.21 HU。结论：TotalSynth提供了具有广泛解剖覆盖范围的可复用MRI和CBCT基CT合成模型，而外部评估强调了在域偏移下进行本地验证和可选微调的必要性。
英文摘要
Purpose: To develop and evaluate TotalSynth, a reusable pretrained framework for whole-body synthetic CT (sCT) generation from MRI and cone-beam CT (CBCT) images. Materials and Methods: In this retrospective technical study, the dataset was assembled between 2020 and 2026 from SynthRAD challenge data, four prostate cohorts, and BIC-MAC. After registration quality control, 1450 of 1800 public challenge pairs were retained; 350 were excluded for insufficient registration quality or major source/CT mismatch. The corpus also included 84 additional prostate MRI/CT and 60 external BIC-MAC MRI/CT cases. Three 5-fold model families were evaluated with image-domain, anatomy-aware, registration-based, and uncertainty metrics. Age and sex were not consistently available across public datasets. Results: The released MRI-to-CT model achieved an overall MAE of 67.49 HU, SSIM of 0.920, and PSNR of 29.28 dB. The CBCT-to-CT model achieved an overall MAE of 53.55 HU, SSIM of 0.939, and PSNR of 32.09 dB. The unified model maintained similar performance on MRI inputs (MAE, 67.68 HU) and CBCT inputs (MAE, 54.22 HU). On external BIC-MAC data, MRI-to-CT MAE was 100.91 HU without fine-tuning and 62.21 HU after fine-tuning. Conclusion: TotalSynth provides reusable MRI- and CBCT-based CT synthesis models with broad anatomical coverage, while external evaluation highlights the need for local validation and optional fine-tuning under domain shift.
Comments10 pages, 3 figures
