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4 April 2022 Efficient evaluation of low-contrast detectability of deep-CNN-based CT reconstruction using channelized Hotelling observer on the ACR accreditation phantom
Mingdong Fan, Zhongxing Zhou, Thomas Vrieze, Jia Wang, Cynthia McCollough, Lifeng Yu
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Proceedings Volume 12031, Medical Imaging 2022: Physics of Medical Imaging; 1203118 (2022) https://doi.org/10.1117/12.2612414
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
Abstract
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%.
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Mingdong Fan, Zhongxing Zhou, Thomas Vrieze, Jia Wang, Cynthia McCollough, and Lifeng Yu "Efficient evaluation of low-contrast detectability of deep-CNN-based CT reconstruction using channelized Hotelling observer on the ACR accreditation phantom", Proc. SPIE 12031, Medical Imaging 2022: Physics of Medical Imaging, 1203118 (4 April 2022); https://doi.org/10.1117/12.2612414
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KEYWORDS
Image quality
Computed tomography
CT reconstruction
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