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Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging
Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging
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Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging
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Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging
Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging

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Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging
Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging
Journal Article

Enabling SENSE accelerated 2D CSI for hyperpolarized carbon-13 imaging

2024
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Overview
As hyperpolarized (HP) carbon-13 ( 13 C) metabolic imaging is clinically translated, there is a need for easy-to-implement, fast, and robust imaging techniques. However, achieving high temporal resolution without decreasing spatial and/or spectral resolution, whilst maintaining the usability of the imaging sequence is challenging. Therefore, this study looked to accelerate HP 13 C MRI by combining a well-established and robust sequence called two-dimensional Chemical Shift Imaging (2D CSI) with prospective under sampling and SENSitivity Encoding (SENSE) reconstruction. Due to the low natural abundance of 13 C, the sensitivity maps cannot be pre-acquired for the reconstruction. As such, the implementation of sodium ( 23 Na) sensitivity maps for SENSE reconstructed 13 C CSI was demonstrated in a phantom and in vivo in the pig kidney. Results showed that SENSE reconstruction using 23 Na sensitivity maps corrected aliased images with a four-fold acceleration. With high temporal resolution, the kidney spectra produced a detailed metabolic arrival and decay curve, useful for further metabolite kinetic modelling or denoising. Metabolic ratio maps were produced in three pigs demonstrating the technique’s ability for repeat metabolic measurements. In cases with unknown metabolite spectra or limited HP MRI specialist knowledge, this robust acceleration method ensures comprehensive capture of metabolic signals, mitigating the risk of missing spectral data.