Full-Dose PET Image Estimation from Low-Dose PET Image Using Deep Learning: a Pilot Study

Juweid ME, Stroobants S, Hoekstra OS, Mottaghy FM, Dietlein M, Guermazi A, Wiseman GA, Kostakoglu L, Scheidhauer K, Buck A, Naumann R, Spaepen K, Hicks RJ, Weber WA, Reske SN, Schwaiger M, Schwartz LH, Zijlstra JM, Siegel BA, Cheson BD, Imaging Subcommittee of International Harmonization Project in Lymphoma: Use of positron emission tomography for response assessment of lymphoma: Consensus of the Imaging Subcommittee of International Harmonization Project in Lymphoma. Journal of Clinical Oncology 25(5):571–578, Feb. 2007

Avril NE, Weber WA: Monitoring response to treatment in patients utilizing PET. Radiologic Clinics of North America 43(1):189–204, Jan. 2005

Fletcher JW, Djulbegovic B, Soares HP, Siegel BA, Lowe VJ, Lyman GH, Coleman RE, Wahl R, Paschold JC, Avril N, Einhorn LH, Suh WW, Samson D, Delbeke D, Gorman M, Shields AF: Recommendations of the use of 18F-FDG PET in oncology. The Journal of Nuclear Medicine 49(3):480–508, Mar. 2008

Kinahan PE, Fletcher JW: PET/CT standardized uptake values (SUVs) in clinical practice and assessing response to therapy. Semin Ultrasound CT MR 31(6):496–505, Dec, 2010

Huang B, Wai-Ming Law M, Khong P: Whole-body PET/CT scanning: estimation of radiation dose and cancer risk. Medical Physics 251(1):166–174, Apr. 2009

Xiang L, Qiao Y, Nie D, An L, Lin W, Wang Q, Shen D: Deep auto-context convolutional neural networks for standard-dose PET image estimation from low-dose PET/MRI. Neurocomputing 267(1):406–416, Jun. 2017

Q. Yang, G. Wang, P. Yan, and M. K. Kalra, “CT image denoising with perceptive deep neural networks,” in The 14th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Xián China, 2017, pp. 858–863.

Wolterink J, Leiner T, Viergever MA, Išgum I: Generative adversarial networks for noise reduction in low-dose CT. IEEE Transactions of Medical Imaging 36(12):2536–2545, Dec. 2017

Yang W et al.: Improving low-dose CT image using residual convolutional network. IEEE Special Section on Advanced Signal Processing Methods in Medical Imaging 5(1):24698–24705, Oct. 2017

Chen H et al.: Low-dose CT denoising with convolutional neural network. In: IEEE 14th International Symposium on Biomedical Imaging. Australia: Melbourne, p. 2017

K. Suzuki et al, “Neural network convolution (NNC) for converting ultra-low-dose to ‘virtual’ high-dose CT images,” in Machine Learning in Medical Imaging, Quebec City, Canada, 2017, pp. 334–343.

Jifara W et al.: Medical image denoising using convolutional neural network: a residual learning approach. The Journal of Supercomputing., 2017. https://doi.org/10.1007/s11227-017-2080-0

J. Xu, E. Gong, J. Pauly, G. Zaharchuk, 200x low-dose PET reconstruction using deep learning, https://arxiv.org/abs/1712.04119 (last accessed Oct. 23, 2018).