| Literature DB >> 31283497 |
Daniele Mammoli, Jeremy Gordon, Adam Autry, Peder E Z Larson, Yan Li, Hsin-Yu Chen, Brian Chung, Peter Shin, Mark Van Criekinge, Lucas Carvajal, James B Slater, Robert Bok, Jason Crane, Duan Xu, Susan Chang, Daniel B Vigneron.
Abstract
Kinetic modeling of the in vivo pyruvate-to-lactate conversion is crucial to investigating aberrant cancer metabolism that demonstrates Warburg effect modifications. Non-invasive detection of alterations to metabolic flux might offer prognostic value and improve the monitoring of response to treatment. In this clinical research project, hyperpolarized [1-13C] pyruvate was intravenously injected in a total of 10 brain tumor patients to measure its rate of conversion to lactate ( kPL ) and bicarbonate ( kPB ) via echo-planar imaging. Our aim was to investigate new methods to provide kPL and kPB maps with whole-brain coverage. The approach was data-driven and addressed two main issues: selecting the optimal model for fitting our data and determining an appropriate goodness-of-fit metric. The statistical analysis suggested that an input-less model had the best agreement with the data. It was also found that selecting voxels based on post-fitting error criteria provided improved precision and wider spatial coverage compared to using signal-to-noise cutoffs alone.Entities:
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Year: 2019 PMID: 31283497 PMCID: PMC6939147 DOI: 10.1109/TMI.2019.2926437
Source DB: PubMed Journal: IEEE Trans Med Imaging ISSN: 0278-0062 Impact factor: 10.048