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Kinetic Modeling of Hyperpolarized Carbon-13 Pyruvate Metabolism in the Human Brain.
- Source :
-
IEEE transactions on medical imaging [IEEE Trans Med Imaging] 2020 Feb; Vol. 39 (2), pp. 320-327. Date of Electronic Publication: 2019 Jul 02. - Publication Year :
- 2020
-
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- <superscript>13</superscript> C] pyruvate was intravenously injected in a total of 10 brain tumor patients to measure its rate of conversion to lactate ( k <subscript>PL</subscript> ) and bicarbonate ( k <subscript>PB</subscript> ) via echo-planar imaging. Our aim was to investigate new methods to provide k <subscript>PL</subscript> and k <subscript>PB</subscript> 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.
- Subjects :
- Carbon Isotopes analysis
Carbon Isotopes pharmacokinetics
Humans
Image Interpretation, Computer-Assisted
Kinetics
Lactic Acid analysis
Lactic Acid metabolism
Brain diagnostic imaging
Brain metabolism
Brain Neoplasms diagnostic imaging
Brain Neoplasms metabolism
Echo-Planar Imaging methods
Pyruvic Acid analysis
Pyruvic Acid pharmacokinetics
Subjects
Details
- Language :
- English
- ISSN :
- 1558-254X
- Volume :
- 39
- Issue :
- 2
- Database :
- MEDLINE
- Journal :
- IEEE transactions on medical imaging
- Publication Type :
- Academic Journal
- Accession number :
- 31283497
- Full Text :
- https://doi.org/10.1109/TMI.2019.2926437