Abstract
In this paper, a novel network based on gated recurrent unit (GRU) is proposed for separating single-scan dual-tracer PET mixed images. Compared to conventional methods, this method can separate dual-tracer that are simultaneously injected or even labeled with the same marker, and do not require arterial blood input function. The proposed 4-layer network denoises the time activity curves (TACs) extracted from the dynamic dual-tracer reconstruction images with noise by pre-training the parameters in the first and second layer, and then uses TAC time information for dual-tracer separation. During the training stage, we optimize the network by minimizing the mean square error (MSE) objective function of the separated predicted value and ground truth. Monte Carlo is used to simulate the PET sampling environment with the mixed dual-tracer \({^{62}}\)Cu-ATSM+\({^{62}}\)Cu-PTSM and \({^{18}}\)F-FDG+\({^{11}}\)C-MET. Calculating the bias and variance to quantitatively analyze the results, we demonstrate that this method is more robust and better separation than the similar methods.
Supported in part by Shenzhen Innovation Funding (No: JCYJ20170818164343304, JCYJ20170816172431715), by the National Natural Science Foundation of China (No: U1809204, 61525106, 61427807, 61701436), and by the National Key Technology Research and Development Program of China (No: 2017YFE0104000, 2016YFC1300302).
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Andreyev, A., Celler, A.: Dual-isotope PET using positron-gamma emitters. Phys. Med. Biol. 56(14), 4539 (2011)
Cheng, X., et al.: Direct parametric image reconstruction in reduced parameter space for rapid multi-tracer PET imaging. IEEE Trans. Med. Imaging 34(7), 1498–1512 (2015)
Feng, D., Wong, K.P., Wu, C.M., Siu, W.C.: A technique for extracting physiological parameters and the required input function simultaneously from PET image measurements: theory and simulation study. IEEE Trans. Inf Technol. Biomed. 1(4), 243–254 (1997)
Guo, J., et al.: 18F-Alfatide II and 18F-FDG dual-tracer dynamic PET for parametric, early prediction of tumor response to therapy. J. Nucl. Med. 55(1), 154–160 (2014)
Huang, S., Carson, A., Hoffman, E., Phelps, D.: An investigation of a double-tracer technique for positron computerized tomography. J. Nucl. Med. 23, 816–822 (1982)
Kadrmas, D.J., Rust, T.C.: Feasibility of rapid multitracer PET tumor imaging. IEEE Trans. Nucl. Sci. 52(5), 1341–1347 (2005)
Luo, Y., Mesgarani, N.: TasNet: time-domain audio separation network for real-time, single-channel speech separation. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 696–700. IEEE (2018)
Ruan, D., Liu, H.: Separation of a mixture of simultaneous dual-tracer PET signals: a data-driven approach. IEEE Trans. Nucl. Sci. 64(9), 2588–2597 (2017)
Rust, T., Kadrmas, D.: Rapid dual-tracer PTSM+ ATSM PET imaging of tumour blood flow and hypoxia: a simulation study. Phys. Med. Biol. 51(1), 61 (2005)
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Tong, J., Chen, Y., Liu, H. (2020). Single-Scan Dual-Tracer Separation Network Based on Pre-trained GRU. In: Li, Q., Leahy, R., Dong, B., Li, X. (eds) Multiscale Multimodal Medical Imaging. MMMI 2019. Lecture Notes in Computer Science(), vol 11977. Springer, Cham. https://doi.org/10.1007/978-3-030-37969-8_6
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DOI: https://doi.org/10.1007/978-3-030-37969-8_6
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