Abstract
Positron Emission Tomography (PET) has been widely applied in clinics for diagnosis of cancer, cardiovascular disease, neurological disorder, and other challenging diseases. Radiotracers are injected into patients prior to PET exams, introducing inevitable radiation risks. While recent deep learning methods have shown to enable low-dose PET without compromising image quality, their performance are often limited when the amplified noise in low-dose scans becomes indistinguishable from high-intensity small abnormality. In this paper, we propose a noise-aware dual Res-UNet framework to enable low dose PET scans and achieve the image quality comparable to that from standard-dose PET scans. Specifically, noise-aware dual Res-UNets are designed to identify the location of high intensity noise in the low dose PET images first, followed by an image reconstruction network incorporating the estimated noise attention map to reconstruct the high quality standard-dose PET image. In order to better reduce the Poisson distribution noise, a general and adaptive robust loss is applied. Experimental results show that our method can outperform other state-of-the-art methods quantitatively and qualitatively and can be applied on real clinical application.
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Xiang, L., Wang, L., Gong, E., Zaharchuk, G., Zhang, T. (2020). Noise-Aware Standard-Dose PET Reconstruction Using General and Adaptive Robust Loss. In: Liu, M., Yan, P., Lian, C., Cao, X. (eds) Machine Learning in Medical Imaging. MLMI 2020. Lecture Notes in Computer Science(), vol 12436. Springer, Cham. https://doi.org/10.1007/978-3-030-59861-7_66
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DOI: https://doi.org/10.1007/978-3-030-59861-7_66
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