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Lee, N. Y.

Publications and source records attributed to Lee, N. Y..

2 recordsLinked to original sources

Optimal Mass Transport Kinetic Modeling for Head and Neck DCE-MRI: Initial Analysis

Current state-of-the-art models for estimating the pharmacokinetic parameters do not account for intervoxel movement of the contrast agent (CA). We introduce an optimal mass transport (OMT) formulation that naturally handles intervoxel CA movement and distinguishes between advective and diffusive flows. Ten patients with head and neck squamous cell carcinoma (HNSCC) were enrolled in the study between June 2014 and October 2015 and under-went DCE MRI imaging prior to beginning treatment. The CA tissue concentration information was taken as the input in the data-driven OMT model. The OMT approach was tested on HNSCC DCE data that provides quantitative information for forward flux ({Phi}F) and backward flux ({Phi}B). OMT-derived {Phi}F was compared with the volume transfer constant for CA, Ktrans, derived from the Extended Tofts Model (ETM). The OMT-derived flows showed a consistent jump in the CA diffusive behavior across the images in accordance with the known CA dynamics. The mean forward flux was 0.0082 {+/-} 0.0091 (min-1) whereas the mean advective component was 0.0052{+/-}0.0086 (min-1) in the HNSCC patients. The diffusive percentages in forward and backward flux ranged from 8.67-18.76% and 12.76-30.36%, respectively. The OMT model accounts for intervoxel CA movement and results show that the forward flux ({Phi}F) is comparable with the ETM-derived Ktrans. This is a novel data-driven study based on optimal mass transport principles applied to patient DCE imaging to analyze CA flow in HNSCC.

systems biology

Rapid and label-free identification of individual bacterial pathogens exploiting three-dimensional quantitative phase imaging and deep learning

The healthcare industry is in dire need for rapid microbial identification techniques. Microbial infection is a major healthcare issue with significant prevalence and mortality, which can be treated effectively during the early stages using appropriate antibiotics. However, determining the appropriate antibiotics for the treatment of the early stages of infection remains a challenge, mainly due to the lack of rapid microbial identification techniques. Conventional culture-based identification and matrix-assisted laser desorption/ionization time-of-flight mass spectroscopy are the gold standard methods, but the sample amplification process is extremely time-consuming. Here, we propose an identification framework that can be used to measure minute quantities of microbes by incorporating artificial neural networks with three-dimensional quantitative phase imaging. We aimed to accurately identify the species of bacterial bloodstream infection pathogens based on a single colony-forming unit of the bacteria. The successful distinction between a total of 19 species, with the accuracy of 99.9% when ten bacteria were measured, suggests that our framework can serve as an effective advisory tool for clinicians during the initial antibiotic prescription. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/596486v2_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@786668org.highwire.dtl.DTLVardef@8b4eb4org.highwire.dtl.DTLVardef@1dc2452org.highwire.dtl.DTLVardef@1d4888c_HPS_FORMAT_FIGEXP M_FIG C_FIG

microbiology