Hello CSc faculty and MS & PhD students,
Join us, Friday, Jan 27th, 2023, at 1 PM ET for an exciting virtual talk by Dr. Fan Lam entitled: "Quantitative, Multidimensional MR Spectroscopic Imaging by Integrating Spin Physics and Machine Learning" as part of the activities of the Brain Space Initiative, co-sponsored by the Center for Translational Research in Neuroimaging and Data Science (TReNDS) and the Data Science Initiative, IEEE Signal Processing Society.
Quantitative, Multidimensional MR Spectroscopic Imaging by Integrating Spin Physics and Machine Learning
While neuroimaging has transformed neuroscience by allowing us to map detailed in vivo structural and functional organizations of human brain, technologies to probe the rich molecular complexity and underpinnings of brain functions in vivo are lacking. Magnetic resonance spectroscopic imaging (MRSI) allows for multiplexed molecular imaging and metabolic profiling of the brain in vivo, but its applications have been limited by low sensitivity, poor spatial resolution, slow imaging speed and challenges in separating molecular signals of interest. In this talk, I will discuss our efforts in addressing these challenges. Specifically, I will present our progresses on achieving simultaneous, high-resolution mapping of metabolites, neurotransmitters, and their biophysical parameters using a quantitative multidimensional MRSI approach that builds on and expanding a subspace imaging framework. I will also discuss how we integrate physics-based modeling and machine learning to address the limitations of subspace modeling. Finally, I will discuss our collaborative efforts on clinical translations of the new imaging technology. We expect these developments to create new tools to help better understand the molecular basis of brain function and diseases, improve diagnosis and treatment assessment.
Biosketch: Dr. Fan Lam graduated from Tsinghua University with his BS in Biomedical Engineering. He received his PhD in Electrical and Computer Engineering from the University of Illinois Urbana-Champaign (UIUC, 2015). Currently, he is an assistant professor in the Department of Bioengineering at UIUC, a full-time faculty member with the Beckman Institute for Advanced Science and Technology and a co-director of the Master of Science in Biomedical Image Computing program at UIUC. Lam's research focuses on developing advanced magnetic resonance-based molecular imaging and multimodal brain mapping methods, and their applications to the study of brain function at normal and diseased states. Dr. Lam is a Junior Fellow of ISMRM (International Society of Magnetic Resonance in Medicine), and a recipient of an NSF CAREER Award (2020). Other awards include a Best Student Paper Award from IEEE-ISBI (International Symposium of Biomedical Imaging, 2015), Robert T. Chien Memorial Award from ECE-UIUC (2015), an NIH-NIBIB Trailblazer Award (2020), and an NIH-NIGMS MIRA R35 Award (2021). Dr. Lam is a senior member of IEEE, serves as an Associate Editor for IEEE Transactions on Medical Imaging and a co-chair of the Young Scholar Committee at the World Association for Chinese Biomedical Engineers (WACBE).
1. F. Lam, C. Ma, B. Clifford, C. L. Johnson, Z.-P. Liang. High-resolution 1H-MRSI of the brain using SPICE: Data Acquisition and Image Reconstruction, Magn. Reson. Med., 76:1059-1070, 2017. Link to Paper<https://doi.org/10.1002%2Fmrm.26019>
2. F. Lam, Y. Li, R. Guo, Y. Zhao, B. Clifford, Z.-P. Liang. Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces, Magn. Reson. Med., 83:377-390, 2020. Link to Paper<https://doi.org/10.1002/mrm.27980>
3. F. Lam, Y. Li, X. Peng. Constrained magnetic resonance spectroscopic imaging by learning nonlinear low-dimensional models, IEEE Trans. Med. Imaging, 39:545-555, 2020. Link to Paper<https://doi.org/10.1109/tmi.2019.2930586>
4. Z. Wang, Y. Li, F. Lam, High-resolution, 3D multi-TE 1H-MRSI using fast spatiospectral encoding and subspace imaging, Magn. Reson. Med., 87:1103-1118, 2022. Link to Paper<https://doi.org/10.1002%2Fmrm.29015>
Meeting number: 2624 626 1848
Password: VbjBYSxg396 (82529794 from phones)
Join by phone
+1-415-655-0002 US Toll
Access code: 2624 626 1848
We hope to see you there!