Funder: Mark Foundation Institute for Cancer ResearchFunder: Cambridge Experimental Cancer Medicine CentreFunder: Alan Turing Institute; Id: http://dx.doi.org/10.13039/100012338Funder: Cantab Capital Institute for the Mathematics of InformationPurpose: Dynamic nuclear polarization is an emerging imaging method that allows noninvasive investigation of tissue metabolism. However, the relatively low metabolic spatial resolution that can be achieved limits some applications, and improving this resolution could have important implications for the technique. Methods: We propose to enhance the 3D resolution of carbon‐13 magnetic resonance imaging (13C‐MRI) using the structural information provided by hydrogen‐1 MRI (1H‐MRI). The proposed approach relies on variational regularization in 3D with a directional total variation regularizer, resulting in a convex optimization problem which is robust with respect to the parameters and can efficiently be solved by many standard optimization algorithms. Validation was carried out using an in silico phantom, an in vitro phantom and in vivo data from four human volunteers. Results: The clinical data used in this study were upsampled by a factor of 4 in‐plane and by a factor of 15 out‐of‐plane, thereby revealing occult information. A key finding is that 3D super‐resolution shows superior performance compared to several 2D super‐resolution approaches: for example, for the in silico data, the mean‐squared‐error was reduced by around 40% and for all data produced increased anatomical definition of the metabolic imaging. Conclusion: The proposed approach generates images with enhanced anatomical resolution while largely preserving the quantitative measurements of metabolism. Although the work requires clinical validation against tissue measures of metabolism, it offers great potential in the field of 13C‐MRI and could significantly improve image quality in the future
