Abstract
Positron Emission Tomography (PET), known for its sensitivity and non-invasiveness in visualizing metabolic processes in the human body, has been widely utilized for clinical diagnosis. However, the procedure of PET imaging requires the administration of a radioactive tracer, which poses potential risks to human health. Reducing the usage of radioactive tracers leads to lower information content and increased independent noise. Therefore, the reconstruction of low-dose PET images becomes crucial. Existing reconstruction methods that learn a single mapping for low-dose PET reconstruction often suffer from over-denoising or incomplete information. To address this challenge, this work investigates the generation of realistic full-dose PET images. Firstly, we propose a simple yet reasonable low-dose PET model that treats each reconstructed voxel as a random variable. This model divides the reconstruction problem into two sub-problems: noise suppression and missing data recovery. Subsequently, we introduce a novel framework called the Coordinated Reconstruction Dual Branch Network (CRDB). The CRDB utilizes dual branches to separately perform denoising and information completion for PET reconstruction. Moreover, the CRDB leverages the Fast Channel Attention mechanism to capture diverse and unique information from different channels. Additionally, to emphasize pronounced distinctions, we adopt the Huber loss as the loss function. Quantitative experiments demonstrate that our strategy achieves favorable results in low-dose PET reconstruction.
Data used in preparation of this article were obtained from the University of Bern, Dept. of Nuclear Medicine and School of Medicine, Ruijin Hospital. As such, the investigators contributed to the design and implementation of DATA and/or provided data but did not participate in analysis or writing of this report. A complete listing of investigators can be found at: “https://ultra-low-dose-pet.grandchallenge.org/Description/”.
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Acknowledgements
This work was supported in part by the National Key Research and Development Program of China under Grant 2022YFF0606303, the National Natural Science Foundation of China under Grant 62206054, Research Capacity Enhancement Project of Key Construction Discipline in Guangdong Province under Grant 2022ZDJS028. Thanks to Xue Song, Kuangyu Shi & Axel Rominger, Dept. of Nuclear Medicine of the University of Bern, Hanzhong Wang, Rui Guo & Biao Li, Ruijin Hospital, Shanghai Jiaotong University for support of the source of the DATA.
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Li, Y. et al. (2024). Coordinated Reconstruction Dual-Branch Network for Low-Dose PET Reconstruction. In: Cai, Z., Xiao, M., Zhang, J. (eds) Theoretical Computer Science. NCTCS 2023. Communications in Computer and Information Science, vol 1944. Springer, Singapore. https://doi.org/10.1007/978-981-99-7743-7_12
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