Publications

2019
K. Kim, D. Kim, J. Yang, G. El Fakhri, Y. Seo, J. A. Fessler, and et al. 2019. “Time of flight PET reconstruction using nonuniform update for regional recovery uniformity.” Medical physics, 46, Pp. 649-664.
D. Wu, K. Kim, G. El Fakhri, and Q. Li. 2019. “Computational-efficient cascaded neural network for CT image reconstruction.” Physics of Medical Imaging, Pp. 109485Z.
2018
J. H. Thrall, X. Li, Q. Li, C. Cruz, S. Do, and K. Dreyer. 2018. “Artificial intelligence and machine learning in radiology: opportunities, challenges, pitfalls, and criteria for success.” Journal of the American College of Radiology, 15, Pp. 504-508.
F. Yang, R. Tabassum, J. Sanchez, A. Becker, G. El Fakhri, Q. Li, and et al. 2018. “Association between partial volume corrected longitudinal tau measures and cognitive decline,” Journal of Nuclear Medicine, 59, Pp. 411-411.
K. Gong, J. Yang, K. Kim, G. El Fakhri, Y. Seo, and Q. Li. 2018. “Attenuation correction for brain PET imaging using deep neural network based on Dixon and ZTE MR images.” Physics in Medicine & Biology, 63, Pp. 125011.
K. Gong, J. Yang, K. Kim, G. El Fakhri, Y. Seo, and Q. Li. 2018. “Attenuation Correction of PET/MR Using Deep Neural Network Based on Dixon and ZTE MR Images.” Journal of Nuclear Medicine, 59, Pp. 650-650.
L. Zhang, H. Wang, Q. Li, M.-H. Zhao, and Q.-M. Zhan. 2018. “Big data and medical research in China.” bmj, 360, Pp. j5910.
X. WANG, X. Zhen, Q. Li, D. Shen, and H. Huang. 2018. “Cognitive assessment prediction in Alzheimer’s disease by multi-layer multi-target regression.” Neuroinformatics, 16, Pp. 285-294.
D. Wu, K. Kim, and Q. Li. 2018. “Computationally Efficient Cascaded Training for Deep Unrolled Network in CT Imaging.” arXiv preprint arXiv:1810.03999.
D. Pantazis, M. Fang, S. Qin, Y. Mohsenzadeh, Q. Li, and R. M. Cichy. 2018. “Decoding the orientation of contrast edges from MEG evoked and induced responses.” NeuroImage, 180, Pp. 267-279.
D. Pantazis, M. Fang, S. Qin, Y. Mohsenzadeh, Q. Li, and R. M. Cichy. 2018. “Decoding the orientation of contrast edges from MEG evoked and induced responses.” NeuroImage, 180, Pp. 267-279.
D. Wu, K. Kim, B. Dong, G. El Fakhri, and Q. Li. 2018. “End-to-End Lung Nodule Detection in Computed Tomography.” International Workshop on Machine Learning in Medical Imaging, Pp. 37-45.
P. Bandi, O. Geessink, Q. Manson, M. Van Dijk, M. Balkenhol, and M. Herms. 2018. “From detection of individual metastases to classification of lymph node status at the patient level: the CAMELYON17 challenge.” IEEE transactions on medical imaging, 38, Pp. 550-560.
K. Gong, J. Guan, K. Kim, X. Zhang, J. Yang, Y. Seo, and et al. 2018. “Iterative PET image reconstruction using convolutional neural network representation.” IEEE Transactions on Medical Imaging, 38, Pp. 675-685.
F. Yang, R. Tabassum, A. Becker, J. S. Sanchez, G. El Fakhri, Q. Li, and et al. 2018. “JOINT DEBLURRING OF LONGITUDINAL DIFFERENTIAL PET IMAGES OF TAU.” Alzheimer's & Dementia: The Journal of the Alzheimer's Association, 14, Pp. P167.
K. Gong, K. Kim, J. Cui, N. Guo, C. Catana, J. Qi, and et al. 2018. “Learning personalized representation for inverse problems in medical imaging using deep neural network.” arXiv preprint arXiv:1807.01759.
K. Gong, J. Yang, K. Kim, G. El Fakhri, Y. Seo, and Q. Li. 2018. “Learning personalized representation for inverse problems in medical imaging using deep neural network.” Physics in Medicine & Biology, 63, Pp. 125011.
Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li. 2018. “Medical image segmentation based on multi-modal convolutional neural network: study on image fusion schemes.” In 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Pp. 903-907.
Y. Zhao, X. Li, W. Zhang, S. Zhao, M. Makkie, M. Zhang, and et al. 2018. “Modeling 4D fMRI Data via Spatio-Temporal Convolutional Neural Networks (ST-CNN).” In International Conference on Medical Image Computing and Computer-Assisted Intervention, Pp. 181-189.
X. Li, Q. Chen, X. WANG, N. Guo, N. Wu, and Q. Li. 2018. “Network Modeling and Pathway Inference from Incomplete Data (" PathInf").” arXiv preprint arXiv:1810.00839.

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