Megavoltage computed tomography (MVCT) has been implemented on many radiation therapy treatment machines as a tomographic imaging modality that allows for three‐dimensional visualization and localization of patient anatomy. Yet MVCT images exhibit lower contrast and greater noise than its kilovoltage CT (kVCT) counterpart. In this work, we sought to improve these disadvantages of MVCT images through an image‐to‐image‐based machine learning transformation of MVCT and kVCT images. We demonstrated that by learning the style of kVCT images, MVCT images can be converted into high‐quality synthetic kVCT (skVCT) images with higher contrast and lower noise, when compared to the original MVCT.

中文翻译：

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通过周期一致的生成机器学习改善兆伏计算机断层扫描 (MVCT) 的对比度和噪声


兆电压计算机断层扫描 (MVCT) 已作为一种断层扫描成像方式在许多放射治疗机器上实施，可实现患者解剖结构的三维可视化和定位。然而，与千伏 CT (kVCT) 图像相比，MVCT 图像的对比度较低，噪声较大。在这项工作中，我们试图通过 MVCT 和 kVCT 图像的基于图像到图像的机器学习转换来改善 MVCT 图像的这些缺点。我们证明，通过学习 kVCT 图像的风格，MVCT 图像可以转换为与原始 MVCT 相比具有更高对比度和更低噪声的高质量合成 kVCT（skVCT）图像。