Primary AffiliationComputational Imaging
Professor Lam received his B.S. in Biomedical Engineering from Tsinghua University in 2008 and his M.S. and Ph.D. in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign in 2011 and 2015 respectively.
- IEEE Senior Membership, 2021
- Trailblazer Award, National Institute of Biomedical Imaging and Bioengineering (NIBIB, NIH), 2020
- National Science Foundation CAREER Award, 2020
- List of Teachers Ranked As Excellent by Students, 2019
- IEEE Transactions on Medical Imaging Editorial Board, Associate Editor, 2019 - Present
- Junior Fellow, International Society of Magnetic Resonance in Medicine, 2017
Professor Lam’s research program focuses on developing and translating new imaging technologies to visualize biological processes in the brain at a level beyond anatomy and neuronal activation, particularly, the molecular and chemical underpinnings of brain function and diseases. Specific scientific pursuits include:
- Pushing the functional scale of in vivo neuroimaging by developing a new generation of multinuclear, ultrahigh-resolution magnetic resonance spectroscopic imaging technologies for multiplexed molecular imaging of the brain.
- Developing novel methods for quantitative and multimodal biochemical mapping of the nervous systems, to study tissue microstructures and elucidate connections across modalities and scales.
- Translating novel molecular imaging technologies to study specific CNS disorders, with the aim of advancing the diagnosis, treatment, and prognosis of these diseases.
- Li, Y, Wang, Z, Sun, R & Lam, F 2021, 'Separation of Metabolites and Macromolecules for Short-TE 1H-MRSI Using Learned Component-Specific Representations', IEEE Transactions on Medical Imaging. DOI: 10.1109/TMI.2020.3048933
- Lam, F & Sutton, BP 2020, 'Intravoxel B0 inhomogeneity corrected reconstruction using a low-rank encoding operator', Magnetic Resonance in Medicine, vol. 84, no. 2, pp. 885-894. DOI: 10.1002/mrm.28182
- Lam, F, Li, Y & Peng, X 2020, 'Constrained Magnetic Resonance Spectroscopic Imaging by Learning Nonlinear Low-Dimensional Models', IEEE Transactions on Medical Imaging, vol. 39, no. 3, 8770102, pp. 545-555. DOI: 10.1109/TMI.2019.2930586
- Lam, F, Li, Y, Guo, R, Clifford, B & Liang, ZP 2020, 'Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces', Magnetic Resonance in Medicine, vol. 83, no. 2, pp. 377-390. DOI: 10.1002/mrm.27980
- Lam, F, Li, Y, Clifford, B & Liang, Z-P 2018, 'Macromolecule mapping of the brain using ultrashort-TE acquisition and reference-based metabolite removal', Magnetic Resonance in Medicine, vol. 79, no. 5, pp. 2460-2469. DOI: 10.1002/mrm.26896
- Li, Y, Lam, F, Clifford, B & Liang, ZP 2017, 'A subspace approach to spectral quantification for MR spectroscopic imaging', IEEE Transactions on Biomedical Engineering, vol. 64, no. 10, 8013142, pp. 2486-2489. DOI: 10.1109/TBME.2017.2741922