Fan Mingdong, Thayib Theodore, Ren Liqiang, Hsieh Scott, McCollough Cynthia, Holmes David, Yu Lifeng
Department of Radiology, Mayo Clinic, Rochester, MN, 55901, USA.
Proc SPIE Int Soc Opt Eng. 2021 Feb;11595. doi: 10.1117/12.2582123. Epub 2021 Feb 15.
Channelized Hotelling observer (CHO), which has been shown to be well correlated with human observer performance in many clinical CT tasks, has a great potential to become the method of choice for objective image quality assessment. However, the use of CHO in clinical CT is still quite limited, mainly due to its complexity in measurement and calculation in practice, and the lack of access to an efficient and validated software tool for most clinical users. In this work, a web-based software platform for CT image quality assessment and protocol optimization (CTPro) was introduced. A validated CHO tool, along with other common image quality assessment tools, was made readily accessible through this web platform for clinical users and researchers without the need of installing additional software. An example of its application to evaluation of convolutional-neural-network (CNN)-based denoising was demonstrated.
通道化霍特林观察者(CHO)在许多临床CT任务中已被证明与人类观察者的表现具有良好的相关性,它极有可能成为客观图像质量评估的首选方法。然而,CHO在临床CT中的应用仍然相当有限,主要原因在于其在实际测量和计算中的复杂性,以及大多数临床用户无法获得高效且经过验证的软件工具。在这项工作中,引入了一个基于网络的CT图像质量评估和协议优化软件平台(CTPro)。通过这个网络平台,临床用户和研究人员无需安装额外软件,就能轻松使用经过验证的CHO工具以及其他常见的图像质量评估工具。展示了其在基于卷积神经网络(CNN)的去噪评估中的应用实例。