SELECTED CONFERENCE PAPERS/ABSTRACTS

  1. M. Anandakumar, T. Trinklein, J. V. Sweedler, F. Lam. Integrating model-based reconstruction and deep learning for accelerating mass spectrometry imaging (Oral Presentation, <5% of total abstracts).
    Proc. of 73rd ASMS Conference on Mass Spectrometry and Allied Topics. Baltimore, Maryland; 2025, ID: 322537.
  2. Y. Wang, U. Saha, E. Roy, A. Smith, F. Lam. Cell-resolved MR spectroscopic signatures for cancer cells mapping: Dual-state subspaces and GFP-labeled glioma mouse validation.
    In Proc. of ISMRM, 2025, p. 0606. (Powerpitch Presentation)
  3. J. Tu, Y. Shi, F. Lam. Score-based self-supervised MRI denoising.
    The Thirteenth International Conference on Learning Representations (ICLR), 2025. https://openreview.net/forum?id=uNd289HjLi.
  4. T. Trinklein, S. Rubakhin, M. Asadian, KR Sabitha, O Lazarov, F. Lam+, JV Sweedler+. Mapping lipids associated with Aβ plaques in a mouse model of Alzheimer’s disease via multiscale mass spectrometry imaging.
    In Alzheimer’s Association International Conference. 2024.
  5. C. Cao, F. Lam+, D. Llano+, R. Dilger, S. K. Silverman, Z. P. Liang, K. C. Li, G. E. Robinson+. Global DNA methylation as an early biomarker for Alzheimer’s disease.
    In Society of Neuroscience. 2024.
  6. R. Zhao, Z. Wang, F. Lam. Learning disentangled representation for multidimensional MR image reconstruction.
    In Prof. of IEEE EMBC, 2024, pp. 1-4. doi: 10.1109/EMBC53108.2024.10782954.
  7. Z. Wang, B. P. Sutton, F. Lam, Robust volumetric diffusion-weighted MRSI via time-resolved phase reconstruction and correction.
    In Proc. of ISMRM, 2024, p. 0253.
  8. Y. Li, Z. Wang, A. Anderson, R. Zhao, P. Arnold, G. Huesmann, F. Lam, Fast MRSI reconstruction combining linear and nonlinear manifold models.
    In Proc. of ISMRM, 2023, p.0870.
  9. R. Zhao, Y. Li, Z. Wang, A. Anderson, P. Arnold, G. Huesmann, F. Lam, MR spatiospectral reconstruction using plug&play denoiser with self-supervised training.
    In Proc. of ISMRM, 2023, p.0955.
  10. Z. Wang, Y. Li, F. Lam, High-Resolution brain metabolite T2 mapping using optimized multi-TE MRSI.
    In Proc. of ISMRM 2022, p. 4998.
  11. Z. Wang, F. Lam, Fast volumetric diffusion-weighted MRSI: improved acquisition and data processing
    In Proc. of ISMRM 2022, p. 3524.
  12. F. Lam, Y. Li, Y. Zhao, J. Haldar, Improving lipid suppression for 1H-MRSI using region-optimized virtual coils.
    In Proc. of ISMRM 2022, p. 2621.
  13. F. Lam, H. Hetherington, and J. Pan, Rapid MRSI of the brain on 7T using subspace-based processing.
    Annual Meeting of International Society for Magnetic Resonance in Medicine, 2021, p. 2206.
  14. Y. Li, L. Ruhm, A. Henning, F. Lam, LeaRning nonlineAr representatIon and projectIon for faSt constrained MRSI rEconstruction (RAIISE).
    In Proc. of ISMRM 2022, p. 4808.
  15. Z. Wang, Y. Li, and F. Lam, Optimized subspace-based J-resolved MRSI for simultaneous metabolite and neurotransmitter mapping.
    Annual Meeting of ISMRM, 2021, p. 72.
  16. Y. Li, X. Peng, F. Lam, Learning nonlinear low-dimensional models for MR spectroscopic imaging using neural networks.
    Annual Meeting of ISMRM, 2019, p. 947.
  17. F. Lam and B. Sutton, Efficient intravoxel B0 inhomogeneity corrected reconstruction of multi-gradient-echo images using a low-rank encoding operator.
    Annual Meeting of ISMRM, 2019, p. 1257.
  18. F. Lam, Y. Li, R. Guo, B. Clifford, X. Peng, Z.-P. Liang, Further accelerating SPICE for ultrafast MRSI using learned spectral features.
    Annual Meeting of International Society for Magnetic Resonance in Medicine, 2018, p. 1058.
  19. F. Lam, Y. Li, Bryan Clifford, X. Peng, Z.-P. Liang, Simultaneous mapping of brain metabolites, macromolecules and tissue susceptibility using SPICE.
    Annual Meeting of International Society for Magnetic Resonance in Medicine, Honolulu, 2017, p. 1249.
  20. F. Lam, Y. Li, B. Clifford, Z.-P. Liang, Macromolecule mapping with ultrashort-TE acquisition and metabolite spectral prior,
    Annual Meeting of International Society for Magnetic Resonance in Medicine, Honolulu, 2017, p. 5518.
  21. C. Ma, F. Lam, Q. Ning, B. A. Clifford, Q. Liu, C. L. Johnson, and Z.-P. Liang, High-Resolution Dynamic 31P-MRSI Using High-Order Partially Separable Functions.
    Annual Meeting of International Society for Magnetic Resonance in Medicine, Singapore, 2016, pp. 875.
  22. F. Lam, B. Clifford, C. Ma, C. L. Johnson, and Z.-P. Liang. Ultra-high resolution 3D 1H-MRSI of the brain: Subspace-based data acquisitions and processing.
    Annual Meeting of International Society for Magnetic Resonance in Medicine, Toronto, 2015, p. 2370.
  23. F. Lam, B. Zhao, and Z.-P. Liang. Joint estimation of spherical harmonic coefficients from magnitude diffusion-weighted images with sparsity constraints.
    IEEE International Symposium on Biomedical Imaging, New York, pp. 947-950, 2015. (Best Student Paper Award)
  24. B. Zhao, F. Lam, B. Bilgicy, H. Yey, and K. Setsompop. Maximum likelihood reconstruction for magnetic resonance fingerprinting.
    IEEE International Symposium on Biomedical Imaging, New York, pp. 905-909, 2015.
  25. F. Lam, C. Ma, T. K. Hitchens, C. Johnson, C. Ho, and Z.-P. Liang. A subspace approach to high-resolution spectroscopic imaging: In vivo experimental results.
    Annual Meeting of International Society for Magnetic Resonance in Medicine, Milan, Italy, 2014, p. 2894.
  26. F. Lam, C. Ma, and Z.-P. Liang. Performance analysis of denoising with low-rank and sparsity constraints.
    IEEE International Symposium on Biomedical Imaging: From Nano to Macro, San Francisco, pp. 1211-1214, 2013.
  27. F. Lam, S. D. Babacan, N. Schuff, and Z.-P. Liang. Denoising diffusion-weighted MR Images using low rank structure and edge constraints.
    International Society for Magnetic Resonance in Medicine, p. 4308, 2012.
  28. F. Lam, J. P. Haldar, and Z.-P. Liang. Motion compensation for reference-constrained image reconstruction from limited data.
    IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 73-76, 2011.
  29. F. Lam, R. Subramanian, D. Xu, and K. F. King. Incorporating support constraints for sparse regularization reconstruction.
    International Society for Magnetic Resonance in Medicine, p. 2843, 2011.
  30. F. Lam, D. Hernando, K. F. King, and Z.-P. Liang. Compressed sensing reconstruction in the presence of a reference image.
    International Society for Magnetic Resonance in Medicine, Stockholm, Sweden, p. 4861. 2010.
Quantitative Multiscale Imaging Group
Email: fanlam1@illinois.edu