Abstract
To reduce radiation dose in CT, we developed a novel deep-learning technique, neural network convolution (NNC), for converting ultra-low-dose (ULD) to “virtual” high-dose (HD) CT images with less noise or artifact. NNC is a supervised image-based machine-learning (ML) technique consisting of a neural network regression model. Unlike other typical deep learning, NNC can learn thus output desired images, as opposed to class labels. We trained our NNC with ULDCT (0.1 mSv) and corresponding “teaching” HDCT (5.7 mSv) of an anthropomorphic chest phantom. Once trained, our NNC no longer require HDCT, and it provides “virtual” HDCT where noise and artifact are substantially reduced. To test our NNC, we collected ULDCT (0.1 mSv) of 12 patients with 3 different vendor CT scanners. To determine a dose reduction rate of our NNC, we acquired 6 CT scans of the anthropomorphic chest phantom at 6 different radiation doses (0.1–3.0 mSv). Our NNC reduced noise and streak artifacts in ULDCT substantially, while maintaining anatomic structures and pathologies such as vessels and nodules. With our NNC, the image quality of ULDCT (0.1 mSv) images was improved at the level equivalent to 1.1 mSv CT images, which corresponds to 91% dose reduction.
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Acknowledgments
The authors are grateful to Y. Liu, Ph.D. at Zhejiang University of Technology, S. Chen, Ph.D., at University of Shanghai for Science and Technology, M.K. Kalra, M.D. at Massachusetts General Hospital, S. Date, M.D. at Hiroshima University Hospital, Y. Funama, Ph.D. at Kumamoto University for discussing the issues and current status of CT and dose reduction techniques.
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Suzuki, K., Liu, J., Zarshenas, A., Higaki, T., Fukumoto, W., Awai, K. (2017). Neural Network Convolution (NNC) for Converting Ultra-Low-Dose to “Virtual” High-Dose CT Images. In: Wang, Q., Shi, Y., Suk, HI., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2017. Lecture Notes in Computer Science(), vol 10541. Springer, Cham. https://doi.org/10.1007/978-3-319-67389-9_39
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DOI: https://doi.org/10.1007/978-3-319-67389-9_39
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