Publications

2020
Q. Dong, N. Qiang, J. Lv, X. Li, L. Dong, T. Liu, and Q Li, Z. 2020. “A Novel fMRI Representation Learning Framework with GAN.” International Workshop on Machine Learning in Medical Imaging , Pp. 21-29.
D. Wu, K. Gong, C.D. Arru, F Homayounieh, B. Bizzo, V. Buch, and & others. 2020. “Severity and consolidation quantification of COVID-19 from CT images using deep learning based on hybrid weak labels.” IEEE Journal of Biomedical and Health Informatics, 24, 12, Pp. 3529-3538.
S. Jeong, X. Li, J. Yang, Q. Li, and V. Tarokh. 2020. “Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study.” IEEE Access , 8, Pp. 36728-36740. Publisher's Version
Q. Dong, Qiang. N., J. Ly, X. Li, T. Liu, and Q. Li. 2020. “Spatiotemporal Attention Autoencoder (STAAE) for ADHD Classification.” In 2020 International Conference on Medical Image Computing and Computer-Assisted Intervention. Publisher's Version
2019
Z. Guo, N. Guo, K. Gong, and Q. Li. 2019. “Automatic multi-modality segmentation of gross tumor volume for head and neck cancer radiotherapy using 3D U-Net.” Medical Imaging, Pp. 1095009.
D. Wu, K. Kim, and Q. Li. 2019. “Computationally efficient deep neural network for computed tomography image reconstruction.” Medical physics. Publisher's Version
J. Cui, K. Gong, N. Guo, K. Kim, H. Liu, and Q. Li. 2019. “CT-guided PET parametric image reconstruction using deep neural network without prior training data.” Medical Imaging, Pp. 109480Z.
Z. Guo, X. Li, H. Huang, N. Guo, and Q. Li. 2019. “Deep Learning-Based Image Segmentation on Multimodal Medical Imaging.” IEEE Transactions on Radiation and Plasma Medical Sciences, 3, Pp. 162-169.
K. Gong, C. Catana, J. Qi, and Q. Li. 2019. “Direct Patlak Reconstruction for Low-Dose Dynamic PET Using Unsupervised Deep Learning.” Nuclear Medicine, 60, Pp. 575-575.
K. Gong, C. Catana, J. Qi, and Q. Li. 2019. “Direct patlak reconstruction from dynamic PET using unsupervised deep learning.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 110720R.
R. Ju, C. Hu, P. Zhou, and Q. Li. 2019. “Early diagnosis of Alzheimer's disease based on resting-state brain networks and deep learning.” IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB),, 16, Pp. 244-257.
K. Gong, D. Wu, K. Kim, J. Yang, G. El Fakhri, Y. Seo, and et al. 2019. “EMnet: an unrolled deep neural network for PET image reconstruction.” Medical Imaging, Pp. 1094853.
N. Guo, C. Wu, Z. Guo, and Q. Li. 2019. “Intratumoral heterogeneity predicts recurrence after radiofrequency ablation therapy using early post-treatment 18F-FDG PET in lung cancer.” Journal of Nuclear Medicine, 60, Pp. 1588-1588.
D. Wu, K. Kim, M. K. Kalra, B. De Man, and Q. Li. 2019. “Learned primal-dual reconstruction for dual energy computed tomography with reduced dose.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 1107206. Publisher's Version
K. Gong, K. Kim, D. Wu, M. K. Kalra, and Q Li, Z. 2019. “Low-dose dual energy CT image reconstruction using non-local deep image prior.” In IEEE Nuclear Science Symposium and Medical Imaging Conference , Pp. 1-2.
K. Gong, D. Wu, K. Kim, J. Yang, T. Sun, G. El Fakhri, and et al. 2019. “MAPEM-Net: an unrolled neural network for Fully 3D PET image reconstruction.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 110720O.
K. Kim, Y. D. Son, J.-H. Kim, and Q. Li. 2019. “Parametric image estimation using Residual simplified reference tissue model.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 1107237.
T.-A. Song, F. Yang, S. R. Chowdhury, K. Kim, K. A. Johnson, and G. El Fakhri. 2019. “PET Image Deblurring and Super-Resolution with an MR-Based Joint Entropy Prior.” IEEE Transactions on Computational Imaging.
J. Cui, K. Gong, N. Guo, C. Wu, K. Kim, and H. Liu. 2019. “Population and individual information guided PET image denoising using deep neural network.” In 15th International Meeting on Fully Three-Dimensional Image Reconstruction in Radiology and Nuclear Medicine, Pp. 110721E.
T.-A. Song, S. R. Chowdhury, G. El Fakhri, Q. Li, and J. Dutta. 2019. “Super-resolution PET imaging using a generative adversarial network.” Journal of Nuclear Medicine, 60, Pp. 576-576.

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