JOURNAL PUBLICATIONS

  1. Y. Wang, U. Saha, S. S. Rubakhin, E. J Roy, A. M. Smith, J. V. Sweedler, F. Lam
    High-resolution 1H-MRSI at 9.4T by integrating relaxation enhancement and subspace imaging
    To Appear in NMR in Biomedicine, 2024
  2. R. Zhao, X. Peng, V. A. Kelkar, M. A. Anastasio, F. Lam
    High-dimensional MR reconstruction integrating subspace and adaptive generative models.
    IEEE Trans. Biomed. Eng., 2024.
  3. Z. Wang, Y. Li, C. Cao, A. Anderson, G. Huesmann, F. Lam
    Multi-parametric molecular imaging of the brain using optimized J-resolved subspace MRSI
    IEEE Trans. Biomed. Eng., 2024.
  4. Y. Xie, D. C. Castro, S. S. Rubakhin, J. V. Sweedler*, F. Lam*
    Multiscale biochemical mapping of the brain via deep-learning-enhanced high-throughput mass spectrometry.
    Nature Methods, 2024.
  5. F. Lam*, X. Peng, Z.-P. Liang
    High-dimensional MR spatiospectral imaging by integrating physics-based modeling and data-driven machine learning.
    IEEE Sig. Proc. Mag., 40:101-115, 2023.
  6. A., Shaffer, S.S. Kwok, A. Naik, A. T. Anderson, F. Lam, T. Wszalek, P. M. Arnold, W. Hassaneen.
    Ultra-high-field MRI in the diagnosis and management of gliomas: A systematic review.
    Front Neurol. 13, 2022.
  7. Y. Li, Z. Wang, F. Lam.
    SNR enhancement for multi-TE MRSI using joint low-dimensional model and spatial constraints.
    IEEE Transactions on Biomedical Engineering, 69:3087-3097, 2022.
  8. F. Lam+, J. Chu+, J. S. Choi, C. Cao, T. K. Hitchens, S. K. Silverman, Z.-P. Liang, R. N. Dilger, G. E. Robinson*, K. C. Li*
    Epigenetic MRI: Noninvasive imaging of DNA methylation in the brain.
    Proceedings of the National Academy of Sciences, 119:e2119891119, 2022.
  9. Y. Xie, D. C. Castro, S. S. Rubakhin, J. V. Sweedler*, F. Lam*
    Enhancing the throughput of FT mass spectrometry imaging using compressed sensing and subspace modeling.
    Analytical Chemistry, 2022, DOI: 10.1021/acs.analchem.1c05279. (*Co-corresponding author)
  10. X. Peng, B. Sutton, F. Lam, Z.-P. Liang
    DeepSENSE: Learning coil sensitivity functions for SENSE reconstruction using deep learning.
    Magnetic Resonance in Medicine, 87:1894-1902, 2022.
  11. Z. Wang, Y. Li, F. Lam.
    High-resolution, 3D multi-TE 1H-MRSI using fast spatiospectral encoding and subspace imaging
    Magnetic Resonance in Medicine, 87:1103-1118, 2022.
  12. Y. Li, Z. Wang, R. Sun, F. Lam.
    Separation of metabolites and macromolecules for short-TE 1H-MRSI using learned component-specific representations.
    IEEE Transactions on Medical Imaging, 40:1157-1167, 2021.
  13. Y. R. Xie, D. C. Castro, F. Lam*, J. V. Sweedler*.
    Accelerating Fourier transform-ion cyclotron resonance mass spectrometry imaging using a subspace approach.
    Journal of the American Society for Mass Spectrometry, 2020 (*Co-corresponding Authors)
  14. R. A. Hamideh, B. Akbari, P. Fathi, S. K. Misra, A. Sutrisno, F. Lam, D. Pan.
    Biodegradable MRI visible drug eluting stent reinforced by metal organic frameworks.
    Advanced Healthcare Materials, 9:2000136, 2020.
  15. F. Lam, B. P. Sutton.
    Intravoxel B0 inhomogeneity corrected reconstruction using a low-rank encoding operator.
    Magnetic Resonance in Medicine, 84:885-894, 2020.
  16. F. Lam, Y. Li, X. Peng.
    Constrained magnetic resonance spectroscopic imaging by learning nonlinear low-dimensional models.
    IEEE Transactions on Medical Imaging, 39:545-555, 2020.
  17. B. Clifford, Y. Gu, Y. Liu, K. Kim, S. Huang, Y. Li, F. Lam, Z.-P. Liang, X. Yu.
    High-resolution dynamic 31P-MR spectroscopic imaging for mapping mitochondrial function.
    IEEE Transactions on Biomedical Engineering, 67:2745-2753, 2020 (TBME Highlight Article).
  18. F. Lam, Y. Li, R. Guo, Y. Zhao, B. Clifford, Z.-P. Liang.
    Ultrafast magnetic resonance spectroscopic imaging using SPICE with learned subspaces.
    Magnetic Resonance in Medicine, 83:377-390, 2020.
  19. A. T. Mudd, J. E. Fil, L. C. Knight, F. Lam, Z.-P. Liang, R. N. Dilger.
    Early-life iron deficiency reduces brain iron content and alters brain tissue composition despite iron repletion: A neuroimaging assessment.
    Nutrients, 10:135, 2018.
  20. F. Lam, Y. Li, B. Clifford, Z.-P. Liang.
    Macromolecule mapping of the brain using ultrashort-TE acquisition and reference-based metabolite removal.
    Magnetic Resonance in Medicine, 79:2460-2469, 2018.
  21. X. Peng, F. Lam, Y. Li, B. Clifford, Z.-P. Liang.
    Simultaneous QSM and metabolic imaging of the brain using SPICE.
    Magnetic Resonance in Medicine, 79:13-21, 2018.
  22. Y. Li, F. Lam, B. Clifford, Z.-P. Liang.
    A subspace approach to spectral quantification for MR spectroscopic imaging.
    IEEE Transactions on Biomedical Engineering, 64:2486-2489, 2017 (TBME Highlight Article).
  23. C. Ma, B. Clifford, Y. Liu, Y. Gu, F. Lam, X. Yu, Z.-P. Liang.
    High-resolution dynamic 31P-MRSI using a low-rank tensor Model.
    Magnetic Resonance in Medicine, 78:419-428, 2017.
  24. C. Ma, F. Lam, Q. Ning, C. L. Johnson, and Z.-P. Liang.
    High-resolution 1H-MRSI of the brain using short-TE SPICE.
    Magnetic Resonance in Medicine, 77:467-479, 2017.
  25. F. Lam, C. Ma, B. Clifford, C. L. Johnson, and Z.-P. Liang.
    High-resolution 1H-MRSI of the brain using SPICE: Data Acquisition and Image Reconstruction,
    Magnetic Resonance in Medicine, 76:1059-1070, 2017. (MRM Cover Feature Article).
  26. Q. Ning, C. Ma, F. Lam, Z.-P. Liang.
    Spectral quantification for high-resolution MR spectroscopic imaging with spatiospectral constraints.
    IEEE Transactions on Biomedical Engineering, 64: 1178-1186, 2016.
  27. C. Ma, F. Lam, C. L. Johnson, and Z.-P. Liang.
    Removal of nuisance signals from limited and sparse 1H MRSI data using a union-of-subspaces model.
    Magnetic Resonance in Medicine, 75:488–497, 2016.
  28. J. He, Q. Liu, A. Christodoulou, C. Ma, F. Lam, Z.-P. Liang.
    Accelerated high-dimensional MR imaging with sparse sampling using low-rank tensors.
    IEEE Transaction on Medical Imaging, vol. 35, pp. 2119-2129, 2016.
  29. F. Lam, D. Liu, Z. Song, N. Schuff, and Z.-P. Liang.
    A fast algorithm for rank and edge constrained denoising of magnitude diffusion-weighted images.
    Magnetic Resonance in Medicine, 75:433-440, 2016.
  30. Y. Liu, C. Ma, B. A. Clifford, F. Lam, C. L. Johnson, and Z.-P. Liang.
    Improved low-rank filtering of magnetic resonance spectroscopic imaging data corrupted by noise and B0 field inhomogeneity.
    IEEE Transactions on Biomedical Engineering, 63:841-849, 2015 (TBME Highlight Article)
  31. B. Zhao, W. Lu, T. K. Hitchens, F. Lam, C. Ho, and Z.-P. Liang.
    Accelerated MR parameter mapping with low-rank and sparsity constraints.
    Magnetic Resonance in Medicine, 74:489–498, 2015.
  32. B. Zhao, F. Lam, and Z.-P. Liang.
    Model-based MR parameter mapping with sparsity constraints: Parameter estimation and performance bounds.
    IEEE Transactions on Medical Imaging, 33:1832-1844, 2014.
  33. F. Lam and Z.-P. Liang.
    A subspace approach to high-resolution spectroscopic imaging.
    Magnetic Resonance in Medicine, 71:1349-1357, 2014.
  34. F. Lam, S. D. Babacan, J. P. Haldar, M. W. Weiner, N. Schuff, and Z.-P. Liang.
    Denoising diffusion-weighted magnitude MR images using rank and edge constraints.
    Magnetic Resonance in Medicine, 71:1272-1284, 2014.
  35. X. Qu, Y. Hou, F. Lam, D. Guo, J. Zhong, and Z. Chen.
    Magnetic resonance image reconstruction from undersampled measurements using a patch-based nonlocal operator.
    Medical Image Analysis, 18:843-856, 2014.
  36. J. Gai, N. Obeid, J. L. Holtrop, X.-L. Wu, F. Lam, M. Fu, J. P. Haldar, W.-M. Hwu, Z.-P. Liang, and B. P. Sutton.
    More IMPATIENT: A gridding-accelerated Toeplitz-based strategy for non-Cartesian high-resolution 3D MRI on GPUs.
    Journal of Parallel and Distributed Computing, 73:686-697, 2013.
  37. H. Du and F. Lam.
    Compressed sensing MR image reconstruction using a motion compensated reference.
    Magnetic Resonance Imaging, 30:954-963, 2012.