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计算机断层扫描超分辨率的深度学习使用多模式数据训练
Wai Yan Ryana Fok1,2, Andreas Fieselmann1, Magdalena Herbst1
1X-ray Products, Siemens Healthcare GmbH, Forchheim, Germany.
Medical physics
|November 16, 2023
概括
本研究引入了超分辨率 (SR) U-Net,以增强低分辨率CT图像,以生成现实的数字放射图 (DRR). SR U-Net显著提高了图像质量,使得更好的AI训练数据能够用于X射线成像.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 有限的注释数据阻碍了X射线成像中的AI.
- CT和X射线共享成像物理,使跨领域的数据共享.
- 超分辨率 (SR) 可以增强CT分辨率,用于生成现实的数字重建放射图 (DRR).
研究的目的:
- 提出一个新的SR网络,使用基于内核的低分辨率 (LR) 和高分辨率 (HR) 图像进行训练.
- 从HR圆束CT (CBCT) 扫描中生成现实的多探测器CT (MDCT) 像LR图像.
- 在医学成像中改善SR的双立方插值.
主要方法:
- 在CBCT图像切片上使用SR U-Net架构进行LR-HR映射.
- 训练了两个模型:SRUN (基于内核的LR) 和SRUN (双立方下采样基线).
- 在未见的CBCT和MDCT数据集上评估模型.
主要成果:
- 这两种SRUN模型都在未见的CBCT图像上显示了MAE,PSNR和SSIM的显著改善.
- SRUN (基于内核) 的表现优于SRUN (双立方),MAE减少了14%,PSNR减少了6%,SSIM增加了8%.
- SRUN产生了更清晰的图像,并证明了LR MDCT数据的跨模式改进.
结论:
- 拟议的SR方法超越了对未见LR CBCT图像的传统插值.
- 数据频率特征对于学习SR特征至关重要.
- 这种方法使得CT生成的高分辨率DRR能够用于深度学习培训.
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