2022 international Genetically Engineered Machine Competition
Published:
BNU-China won silver medal in iGEM 2022.
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Published:
BNU-China won silver medal in iGEM 2022.
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Published in Physics in Medicine & Biology, Volume 68, Number 14, 2023
This paper presents a simulation study to demonstrate that the contrast recovery coefficients (CRC) and detectability of small lesions of a one-meter-long positron emission tomography (PET) scanner can be further enhanced by the integration of high resolution virtual-pinhole (VP) PET devices. Read more
Recommended citation: Jiang, J., Hua, J., Wang, H., Yuan, Z., Meng, Y., Lu, H., ... & Tai, Y. C. (2023). "A virtual-pinhole PET device for improving contrast recovery and enhancing lesion detectability of a one-meter-long PET scanner: a simulation study." Physics in Medicine and Biology.. 68(14). https://iopscience.iop.org/article/10.1088/1361-6560/acdfaf
Published in 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD), 2024
This work introduces a multi-GPU-based list-mode fully quantitative Time-of-Flight (TOF) PET image reconstruction technique, named QUANTOF, that integrates all corrections during PET image iterative reconstruction on GPUs. Additionally, optimizations have been applied to the backprojection algorithm, resulting in a two-fold increase in speed compared to the pre-optimized version. Validation using experimental datasets from a Siemens Biograph Vision PET/CT scanner demonstrates that this technique significantly reduces reconstruction time costs while achieving exceptional quantitative accuracy. Read more
Recommended citation: Yuan, Z., Zhan, F., Lu, H., Li, C., Hou, Y., Wang, H., ... & Jiang, J. (2024, October). QUANTOF: multi-GPU-based list-mode fully quantitative TOF PET image reconstruction. In 2024 IEEE Nuclear Science Symposium (NSS), Medical Imaging Conference (MIC) and Room Temperature Semiconductor Detector Conference (RTSD) (pp. 1-1). IEEE. https://ieeexplore.ieee.org/abstract/document/10657018
Published in Journal of Geophysical Research: Machine Learning and Computation, 2025
To enhance the model’s generalization ability, here we implemented and compared three domain adaptation methods, i.e., the classic supervised fine-tuning method and two proposed unsupervised methods: Image StyleTransfer Domain Adaptation (ISTDA) and the Tversky Adversarial Domain Adaptation (TADA) network. In our proposed ISTDA, we uniformed the contextual information of multi-temporal images by Cycle Generative Adversarial Network (CycleGAN). We introduced the Tversky loss and the automatic adjustment of weights for multiple loss functions to suppress false positives and to improve the generalization of TADA. Read more
Recommended citation: Lin, Y., Hu, X., Lu, H., Niu, F., Liu, G., Huang, L., et al. (2025). Multi-annual inventorying of retrogressive thaw slumps using domain adaptation. Journal of Geophysical Research: Machine Learning and Computation, 2, e2024JH000370. https://doi.org/10.1029/2024JH000370 https://doi.org/10.1029/2024JH000370
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This is a description of your talk, which is a markdown files that can be all markdown-ified like any other post. Yay markdown! Read more
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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