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从单能扫描中深度学习生成脏双能CT

M Sheikhi¹, S Sina², M Karimipourfard³

¹Nuclear Engineering Department, School of Mechanical Engineering, Shiraz University, Shiraz, Iran; Abu Ali Sina Hospital, Shiraz, Iran.

Clinical radiology

|November 3, 2023

概括

深度学习 (DL) 从单能扫描中有效生成双能CT图像,改善结石类型诊断. 这种方法减少了辐射剂量,并提高了尿路结石的诊断准确性.

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:

准确的结石类型是有效治疗的关键.
双能计算断层扫描 (DECT) 有助于石头的表征,但涉及更高的辐射剂量.
从单能CT (SECT) 模拟DECT的方法的开发可以优化患者的护理.

研究的目的:

评估一种深度学习 (DL) 方法,用于从SECT数据生成类似DECT的图像.
评估DL生成的DECT图像的准确性,以准确诊断结石类型.
探索DL在CT成像中减少辐射暴露的潜力.

主要方法:

使用一个p2p-UNet-GAN模型,从23名患者的SECT数据集中合成DECT图像.
生成的图像与已建立的CT协议 (120 kVp/80 kVp和120 kVp/135 kVp) 相比较.
使用SSIM,PSNR和MSE等指标量化评估图像质量,并进行衰减比分析.

主要成果:

该DL模型实现了高图像质量,SSIM值为0.85-0.95,PSNR为28-32,MSE为0.85-0.89.
生成的图像显示了结石的精确定位.
该方法成功地预测了与真实患者数据相比,具有高准确性的石头类型.

结论:

基于DL的图像合成提供了一种可行的方法,用于从SECT生成DECT类图像.
这种技术有望降低患者的辐射剂量.
DL方法有助于准确预测尿路结石类型,有助于临床诊断.

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