Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework

Zhongxing Zhou; Scott S. Hsieh; Hao Gong; Cynthia H. McCollough; Lifeng Yu

doi:10.1117/12.3008581

1 April 2024 Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework

Zhongxing Zhou, Scott S. Hsieh, Hao Gong, Cynthia H. McCollough, Lifeng Yu

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Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 1292508 (2024) https://doi.org/10.1117/12.3008581
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Deep learning-based image reconstruction and noise reduction (DLIR) methods have been increasingly deployed in clinical CT. Accurate assessment of their data uncertainty properties is essential to understand the stability of DLIR in response to noise. In this work, we aim to evaluate the data uncertainty of a DLIR method using real patient data and a virtual imaging trial framework and compare it with filtered-backprojection (FBP) and iterative reconstruction (IR). The ensemble of noise realizations was generated by using a realistic projection domain noise insertion technique. The impact of varying dose levels and denoising strengths were investigated for a ResNet-based deep convolutional neural network (DCNN) model trained using patient images. On the uncertainty maps, DCNN shows more detailed structures than IR although its bias map has less structural dependency, which implies that DCNN is more sensitive to small changes in the input. Both visual examples and histogram analysis demonstrated that hotspots of uncertainty in DCNN may be associated with a higher chance of distortion from the truth than IR, but it may also correspond to a better detection performance for some of the small structures.

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Zhongxing Zhou, Scott S. Hsieh, Hao Gong, Cynthia H. McCollough, and Lifeng Yu "Evaluation of data uncertainty for deep-learning-based CT noise reduction using ensemble patient data and a virtual imaging trial framework", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 1292508 (1 April 2024); https://doi.org/10.1117/12.3008581

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