用AI生成高质量肾盂造影CT图像，可降低三分之一辐射剂量。
3D Nephrographic Image Synthesis in CT Urography with the Diffusion Model and Swin Transformer
- 结合扩散模型与Swin Transformer，生成3D肾盂造影图像。
- 合成图像与真实图像相似度高，辐射可减少33.3%。
- 适合需要降低辐射的泌尿系统CT检查人群。
本研究旨在开发并验证一种基于扩散模型与Swin Transformer的深度学习方法，用于合成CT尿路造影（CTU）中的3D肾盂造影相图像。回顾性研究纳入327例患者（平均年龄63±15岁，男性174例，女性153例），三相扫描经仿射配准对齐。提出dsSNICT模型生成肾盂造影图像，评估指标包括峰值信噪比（PSNR）、结构相似性指数（SSIM）、平均绝对误差（MAE）和弗雷切特视频距离（FVD）。结果表明，合成图像平均PSNR为26.3±4.4 dB，SSIM为0.84±0.069，MAE为12.74±5.22 HU，FVD为1323。两名腹部放射科专家评分显示，真实图像平均分3.5，合成图像平均分3.4（P=0.5），差异无统计学意义。该方法能有效生成高质量3D肾盂造影图像，使CTU辐射剂量降低33.3%，提升检查安全性与诊断价值。
原文摘要 · Abstract (English)
Purpose: This study aims to develop and validate a method for synthesizing 3D nephrographic phase images in CT urography (CTU) examinations using a diffusion model integrated with a Swin Transformer-based deep learning approach. Materials and Methods: This retrospective study was approved by the local Institutional Review Board. A dataset comprising 327 patients who underwent three-phase CTU (mean $\pm$ SD age, 63 $\pm$ 15 years; 174 males, 153 females) was curated for deep learning model development. The three phases for each patient were aligned with an affine registration algorithm. A custom deep learning model coined dsSNICT (diffusion model with a Swin transformer for synthetic nephrographic phase images in CT) was developed and implemented to synthesize the nephrographic images. Performance was assessed using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Mean Absolute Error (MAE), and Fréchet Video Distance (FVD). Qualitative evaluation by two fellowship-trained abdominal radiologists was performed. Results: The synthetic nephrographic images generated by our proposed approach achieved high PSNR (26.3 $\pm$ 4.4 dB), SSIM (0.84 $\pm$ 0.069), MAE (12.74 $\pm$ 5.22 HU), and FVD (1323). Two radiologists provided average scores of 3.5 for real images and 3.4 for synthetic images (P-value = 0.5) on a Likert scale of 1-5, indicating that our synthetic images closely resemble real images. Conclusion: The proposed approach effectively synthesizes high-quality 3D nephrographic phase images. This model can be used to reduce radiation dose in CTU by 33.3\% without compromising image quality, which thereby enhances the safety and diagnostic utility of CT urography.
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