合成双能CT从单能CT重建使用人工智能
Jiwoong Jeong1,2, Andrew Wentland3, Domenico Mastrodicasa4
1Department of Radiology, Mayo Clinic, 13400 E. Shea Blvd, Scottsdale, AZ, 85259, USA. jjeong35@asu.edu.
Abdominal radiology (New York)
|September 4, 2023
概括
使用人工智能从单能CT (SECT) 生成的合成双能CT (sDECT) 显示出有前途. 该Pix2PixHD模型表现出卓越的性能,可能提供来自SECT数据的DECT的好处.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 双能量CT (DECT) 提供了有价值的材料分解信息.
- 使用人工智能 (AI) 来生成单能CT (SECT) 的DECT图像,可以扩大DECT的实用性.
- 生成对抗网络 (GAN) 是用于图像翻译的先进AI模型.
研究的目的:
- 使用两个GAN架构 (Pix2PixHD和CycleGAN) 来开发和评估来自SECT的合成DECT (sDECT) 图像.
- 评估人工智能生成的sDECT在临床应用中的实用性,例如检测胃肠道出血和虚拟无增强成像等.
主要方法:
- 对734名患者增强DECT扫描的回顾性分析.
- 训练Pix2PixHD和CycleGAN模型从70keV的SECT产生50keV,和虚拟未增强 (VUE) 图像.
- 使用平均平方误差,结构相似度指数和峰值信号与噪声比率进行定量评估.
- 对胃肠道出血和CT尿图数据集的临床实用性测试.
主要成果:
- 两种GAN的SDECT图像在质量上与真正的DECT相似.
- 在量化指标方面,Pix2PixHD显著超过CycleGAN (例如,的结构相似性指数:87%与46%相比).
- 通过Pix2PixHD生成的sDECT改善了胃肠道出血的明显性和VUE成像中的去除.
结论:
- 从SECT获得基于AI的sDECT生成是可行的.
- 在Pix2PixHD GAN架构显示强大的潜力,创造临床上有用的sDECT图像.
- 使用Pix2PixHD的sDECT可能与传统的DECT提供类似的优势.
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