评测深度学习生成合成CT，助力放疗精准规划。
Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report
- 基于多中心数据，用深度学习从MRI或CBCT生成等效CT。
- CBCT转CT误差低至MAE 48.3 HU，剂量验证通过率超99%。
- 图像质量不能完全替代剂量评估，需结合临床验证。
放疗需精确分次给药，CT因提供电子密度信息成为治疗规划基础。重复扫描带来辐射与流程负担，MRI缺乏电子密度，锥形束CT（CBCT）需校正才能用于剂量计算。合成CT（sCT）通过将MRI或CBCT转换为含准确亨氏单位（HU）的等效CT图像，支持纯MRI放疗与基于CBCT的自适应流程。基于SynthRAD2023，SynthRAD2025在来自五个欧洲中心的2,362例患者中评测了sCT方法，涵盖头颈部、胸腹部位。任务包括MRI-to-CT（890例）与CBCT-to-CT（1,472例），评估指标包括图像相似性（MAE、PSNR、MS-SSIM）、分割（Dice、HD95）及光子与质子计划的剂量指标。共803名参与者提交，12/13有效。任务一最佳结果：MAE $64.8\pm21.3$ HU，PSNR ∼30 dB，MS-SSIM ∼0.936，Dice 0.79，光子 $γ_{2\%/2\text{mm}}>98\%$，质子 $γ\approx85\%$。任务二性能提升：MAE $48.3\pm13.4$ HU，PSNR 32.6 dB，MS-SSIM 0.968，Dice 0.86，光子 $γ>99\%$，质子 $γ\approx89\%$。图像与分割相关性高（ρ=0.78–0.79），但剂量相关性中等，表明图像质量不足以作为剂量替代指标。头颈部一致性最佳，胸部与腹部变异性较大。组织界面残余误差沿射线路径传播，对质子剂量影响更大。SynthRAD2025证明深度学习可生成临床可用的sCT，尤其在CBCT-to-CT方面表现优异，同时揭示MRI-to-CT挑战，并强调剂量评估对临床验证的关键作用。
原文摘要 · Abstract (English)
Radiation therapy (RT) requires precise dose delivery over multiple fractions, with CT fundamental for treatment planning due to its electron density information. Repeated CT acquisitions impose radiation exposure and logistical burdens, MRI lacks electron density, and cone-beam CT (CBCT) requires correction for dose calculation. Synthetic CT (sCT) generation addresses these by converting MRI or CBCT into CT-equivalent images with accurate Hounsfield Unit (HU) values, enabling MRI-only RT and CBCT-based adaptive workflows. Building on SynthRAD2023, SynthRAD2025 benchmarked sCT methods on 2,362 patients from five European centers across head and neck, thorax, and abdomen. Two tasks: MRI-to-CT (890 cases) and CBCT-to-CT (1,472 cases), evaluated via image similarity (MAE, PSNR, MS-SSIM), segmentation (Dice, HD95), and dosimetric metrics from photon and proton plans. With 803 participants and 12/13 valid submissions, Task 1 top performance reached MAE $64.8\pm21.3$ HU, PSNR $\sim$30 dB, MS-SSIM $\sim$0.936, Dice 0.79, photon $γ_{2\%/2\text{mm}}>98\%$, proton $γ\approx85\%$. Task 2 improved: MAE $48.3\pm13.4$ HU, PSNR 32.6 dB, MS-SSIM 0.968, Dice 0.86, photon $γ>99\%$, proton $γ\approx89\%$. Strong image--segmentation correlations ($ρ=0.78$--$0.79$) but moderate dose correlations confirmed image quality is insufficient as a dosimetric surrogate. Head-and-neck cases were most consistent; thoracic and abdominal cases showed greater variability. Residual errors at tissue interfaces propagate along beam paths, affecting proton dose more than photon. SynthRAD2025 demonstrates that deep learning yields clinically relevant sCTs, especially for CBCT-to-CT, while identifying persistent MRI-to-CT challenges and underscoring dose-based evaluation as essential for clinical validation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。