A comparison of quantitative methods for clinical imaging with hyperpolarized (13)C-pyruvate.
NMR Biomed
; 29(4): 387-99, 2016 Apr.
Article
em En
| MEDLINE
| ID: mdl-27414749
ABSTRACT
Dissolution dynamic nuclear polarization (DNP) enables the metabolism of hyperpolarized (13)C-labelled molecules, such as the conversion of [1-(13)C]pyruvate to [1-(13)C]lactate, to be dynamically and non-invasively imaged in tissue. Imaging of this exchange reaction in animal models has been shown to detect early treatment response and correlate with tumour grade. The first human DNP study has recently been completed, and, for widespread clinical translation, simple and reliable methods are necessary to accurately probe the reaction in patients. However, there is currently no consensus on the most appropriate method to quantify this exchange reaction. In this study, an in vitro system was used to compare several kinetic models, as well as simple model-free methods. Experiments were performed using a clinical hyperpolarizer, a human 3 T MR system, and spectroscopic imaging sequences. The quantitative methods were compared in vivo by using subcutaneous breast tumours in rats to examine the effect of pyruvate inflow. The two-way kinetic model was the most accurate method for characterizing the exchange reaction in vitro, and the incorporation of a Heaviside step inflow profile was best able to describe the in vivo data. The lactate time-to-peak and the lactate-to-pyruvate area under the curve ratio were simple model-free approaches that accurately represented the full reaction, with the time-to-peak method performing indistinguishably from the best kinetic model. Finally, extracting data from a single pixel was a robust and reliable surrogate of the whole region of interest. This work has identified appropriate quantitative methods for future work in the analysis of human hyperpolarized (13)C data.
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Base de dados:
MEDLINE
Assunto principal:
Espectroscopia de Ressonância Magnética
/
Ácido Pirúvico
Tipo de estudo:
Prognostic_studies
Limite:
Animals
Idioma:
En
Ano de publicação:
2016
Tipo de documento:
Article