Abstract #3738
Quantifying uncertainty in kinetic modelling parameters of hyperpolarized dynamic nuclear polarization data through the applicaton of Bayesian Inference fitting techniques
Samira Kazan 1 , Steven Reynolds 2 , Gillian Tozer 1 , Martyn Paley 2 , and Michael Chappell 3,4
1
CR-UK/YCR Sheffield Cancer Research Centre,
University of Sheffield, Sheffield, South Yorkshire,
United Kingdom,
2
Academic
Unit of Radiology, University of Sheffield, Sheffield,
South Yorkshire, United Kingdom,
3
Oxford
Centre for Functional MRI of the Brain, University of
Oxford, Oxfordshire, United Kingdom,
4
Department
of Engineering Science, University of Oxford,
Oxfordshire, United Kingdom
Dynamic nuclear polarization (DNP) is a novel technique
for increasing the sensitivity of magnetic resonance
spectroscopy and imaging. Intravenous administration of
hyperpolarized pyruvate provides a means for quantifying
pyruvate-lactate interconversion in living tissues via
MRS/MRSIand in oncology, is a potential marker for the
efficacy of anti-cancer drugs. The rate constant for
pyruvate to lactate conversion, kpl, requires
mathematical models to extract kinetic parameters, from
the spectroscopy data and the quantification of such
parameters depends on the mathematical model and the
fitting approach used. In this study we determine
whether a Bayesian fitting method (previously adopted in
the quantification of perfusion from Arterial Spin
Labeling) offers improvements in accuracy and robustness
compared to common fitting methods such as Nelder-Mead
when applied to the quantification of pyruvate to
lactate rate constants. Additionally, we use the
estimates of the uncertainty in kpl obtained from the
Bayesian method to assess whether the variations kpl are
the result of fitting error or inter-group variability.
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