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Updated: Jun 6, 2026

Hyperpolarized 13C Metabolic Magnetic Resonance Spectroscopy and Imaging
Published on: December 30, 2016
Hyperpolarized 13C MRS surface coil: design and signal-to-noise ratio estimation
Giulio Giovannetti1, Francesca Frijia, Luca Menichetti
1Institute of Clinical Physiology, National Council of Research, via G. Moruzzi 1, 56124 Pisa, Italy. giovannetti@ifc.cnr.it
Purpose:
Hyperpolarized carbon-13 magnetic resonance spectroscopy is a novel and powerful tool for exploring the metabolic state of tissue, but a number of technological problems still limit this technology and need innovative solutions. In particular, the low molar concentration of derivate metabolites give rise to low signal-to-noise ratio (SNR), which makes the design and development of dedicated RF coils a task of fundamental importance. In this article, the authors describe the simulation and the design of a dedicated 13C surface coil for cardiac metabolism assessment in pig models.
Methods:
A SNR model for a circular loop is presented and applied to the design of a 13C coil which guarantees the desired field-of-view and provides high SNR with a good penetration in deep sample regions. The coil resistance was calculated from Ohm's law and the magnetic field pattern was calculated using Biot-Savart law, while the sample induced resistance was calculated using a numerical finite-difference time-domain algorithm. Successively, a prototype of the coil was built and tested on the workbench and by acquisition of MR data.
Results:
The comparison of SNR-vs-depth profiles between the theoretical SNR model and the experimental SNR extracted from the phantom chemical shift image (CSI) showed the accuracy of the authors' model. Moreover, the authors demonstrated the use of the coil for the acquisition of a CSI of a hyperpolarized [1-13C] pyruvate phantom.
Conclusions:
The results demonstrated the design trade-offs to successfully design a dedicated coil for cardiac imaging in the pig with hyperpolarized 13C by developing a SNR model which allows the prediction of the coil performance. This approach can be employed for deriving SNR formulations for coil with more complex geometries.
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