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nmrlearner 02-19-2014 03:12 PM

In vivo single-shot C spectroscopic imaging of hyperpolarized metabolites by spatiotemporal encoding
 
From The DNP-NMR Blog:

In vivo single-shot C spectroscopic imaging of hyperpolarized metabolites by spatiotemporal encoding


Schmidt, R., et al., In vivo single-shot C spectroscopic imaging of hyperpolarized metabolites by spatiotemporal encoding. J Magn Reson, 2014. 240C(0): p. 8-15.


http://www.ncbi.nlm.nih.gov/pubmed/24486720


Hyperpolarized metabolic imaging is a growing field that has provided a new tool for analyzing metabolism, particularly in cancer. Given the short life times of the hyperpolarized signal, fast and effective spectroscopic imaging methods compatible with dynamic metabolic characterizations are necessary. Several approaches have been customized for hyperpolarized 13C MRI, including CSI with a center-out k-space encoding, EPSI, and spectrally selective pulses in combination with spiral EPI acquisitions. Recent studies have described the potential of single-shot alternatives based on spatiotemporal encoding (SPEN) principles, to derive chemical-shift images within a sub-second period. By contrast to EPSI, SPEN does not require oscillating acquisition gradients to deliver chemical-shift information: its signal encodes both spatial as well as chemical shift information, at no extra cost in experimental complexity. SPEN MRI sequences with slice-selection and arbitrary excitation pulses can also be devised, endowing SPEN with the potential to deliver single-shot multi-slice chemical shift images, with a temporal resolution required for hyperpolarized dynamic metabolic imaging. The present work demonstrates this with initial in vivo results obtained from SPEN-based imaging of pyruvate and its metabolic products, after injection of hyperpolarized [1-13C]pyruvate. Multi-slice chemical-shift images of healthy rats were obtained at 4.7T in the region of the kidney, and 4D (2D spatial, 1D spectral, 1D temporal) data sets were obtained at 7T from a murine lymphoma tumor model.


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