Fan Lam

Fan Lam (S’10) received the B.S. degree in biomedical engineering from Tsinghua University, Beijing, China, in 2008, and the M.S. and Ph.D. degrees in electrical engineering from University of Illinois at Urbana-Champaign, Urbana, IL, USA, in 2011 and 2015, respectively. His research interests include sparse sampling, denoising and parameter estimation, with applications to magnetic resonance imaging and spectroscopic imaging. Dr. Lam received the Computational Science and Engineering Fellowship, Beckman Institute Graduate Fellowship, and Beckman Institute Postdoctoral Fellowship from the University of Illinois. He received the Best Student PaperAward at ISBI’2015. He also received the Magna Cum Laude Award from the International Society of Magnetic Resonance in Medicine in 2012, 2013, and 2014.

Associated articles

TBME, Featured Articles
A Subspace Approach to Spectral Quantification for MR Spectroscopic Imaging
Magnetic resonance spectroscopic imaging (MRSI) has been recognized as a potentially powerful tool for label-free in vivo molecular imaging. Spectral quantification is an essential step in deriving quantitative molecular information from experimental MRSI data. However, obtaining accurate spectral estimates is... Read more
TBME, Featured Articles
Improved Low-Rank Filtering of Magnetic Resonance Spectroscopic Imaging Data Corrupted by Noise and B0 Field Inhomogeneity
Magnetic resonance spectroscopic imaging (MRSI) is a unique tool for molecular imaging without exogenous contrast agents. For example, MRSI allows for mapping many brain metabolites, such as N-acetylaspartate, Choline, and Creatine, which provide useful information about neuronal viability, cellular membrane... Read more
TBME, Featured Articles
High-Resolution Dynamic 31P-MR Spectroscopic Imaging for Mapping Mitochondrial Function
Abnormal mitochondrial metabolism is a hallmark of many prevalent diseases such as diabetes and cardiovascular disease; however, current understanding of mitochondrial function is mostly gained from studies on isolated mitochondria under nonphysiological conditions. This work presents a novel high-resolution dynamic 31P magnetic resonance spectroscopic imaging method that synergistically integrates physics-based models of spectral structures, biochemical modeling of molecular dynamics, and subspace learning, for metabolic mapping at high-spatiotemporal resolution. It enables in vivo imaging of phosphocreatine resynthesis rates, a well-established index of mitochondrial oxidative capacity, thus providing a powerful tool for longitudinal assessment of mitochondrial function in disease progression... Read more