Efficient Evaluation of Low-contrast Detectability of Deep-CNN-based CT Reconstruction Using Channelized Hotelling Observer on the ACR Accreditation Phantom.
Efficient Evaluation of Low-contrast Detectability of Deep-CNN-based CT Reconstruction Using Channelized Hotelling Observer on the ACR Accreditation Phantom.
复制标题
在 ACR 认证模型上使用通道化 Hotelling 观察器对基于深度 CNN 的 CT 重建的低对比度可检测性进行有效评估。
DOI：
10.1117/12.2612414
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Yu,Lifeng
中科院分区：
文献类型：
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作者：
Fan,Mingdong;Zhou,Zhongxing;Vrieze,Thomas;Wang,Jia;McCollough,Cynthia;Yu,Lifeng
在许多临床CT任务中，已被证明与人类观察者的表现有很好的相关性的非线性Hotelling观察者（CHO），有很大的潜力成为客观图像质量评估的首选方法。然而，由于缺乏有效的实施，它的使用在常规CT实践中相当有限。在这项工作中，针对最广泛使用的ACR CT认证体模优化的CHO模型被应用于评估GE Revolution扫描仪上配备的基于深度学习的重建（DLIR）的低对比度可检测性。市售DLIR重建方法显示，在常规剂量水平下，与FBP和IR方法相比，低对比度可检测性一致增加，这表明FBP重建的潜在剂量降低高达27.5%。
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, its use has been quite limited in routine CT practice due to lack of efficient implementation. In this work, a CHO model optimized for the most widely used ACR CT accreditation phantom was applied to evaluate the low-contrast detectability of a deep-learning based reconstruction (DLIR) equipped on a GE Revolution scanner. The commercially available DLIR reconstruction method showed consistent increase in low-contrast detectability over the FBP and the IR method at routine dose levels, which suggests potential dose reduction to the FBP reconstruction by up to 27.5%.