Hyperpolarised [1-13C]pyruvate forms an effective probe of metabolism in vivo and has been used extensively to diagnose and prognosticate cancer. Commonly, [1-13C]pyruvate metabolism is quantified by either total metabolite-to-pyruvate integral ("AUC") ratios, or by fitting metabolic models by least-squares methods. Here, we use a modified Markov Chain Monte Carlo (MCMC) method with adaptive sampling and delayed rejection to fit models to hyperpolarised datasets of the healthy rat brain generated by a spectral-spatial EPI imaging sequence . The method is able to statistically discriminates between signal and noise, and returns quantitatively bounded maps of rate constants of interest, such as $$$k_{\text{Pyruvate}\rightarrow\text{Lactate}}$$$.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords