Hyperpolarization of carbon-13 is a new technology that dramatically enhances the sensitivity of NMR spectroscopy. Hyperpolarized MR imaging can monitor both the uptake and the subsequent metabolic transformations of substrates such as pyruvate, enabling new approaches to diagnosis and treatment monitoring of cancer and other diseases. At present, widely used imaging methods based on echo planar spectroscopic imaging (EPSI) or two-dimension chemical shift imaging (2D CSI) offer relatively limited spatial resolution, which limits the applications of these methods and complicates image interpretation. In addition, blood volume and blood flow play an important role in determining local signal intensity, partially obscuring the effects of metabolism. This proposal describes the development of new imaging methods that exploit the sparsity of hyperpolarized 13C spectra to acquire chemical-shift selective images with high spatial and temporal resolution. These methods are based on steady-state free-precession (SSFP) imaging sequences, which can be readily modified to obtain images that modulate the relative magnitude and phase of signals from different metabolites, thereby enabling the reconstruction of individual images of each metabolite. These techniques will be optimized using phantom and in vivo studies and then compared with conventional 2D CSI methods to validate their performance. The methods will then be applied to simultaneous imaging of perfusion and metabolism in a xenograft model of renal cell carcinoma. Completion of these studies will provide valuable new tools for the study and development of novel treatments for cancer, including anti-angiogenesis drugs and metabolic interventions.
Hyperpolarization is a new technology that dramatically enhances the sensitivity of magnetic resonance imaging, providing new tools for imaging metabolism and blood flow in cancer and other diseases. This proposal is aimed at developing new techniques that will increase the speed and resolution of hyperpolarized imaging. These methods will be validated by comparison with older techniques, and applied to imaging of blood flow and metabolism in cancer.