Super-Resolution Hyperpolarized (13)C Imaging of Human Brain Using Patch-Based Algorithm

Spatial resolution of metabolic imaging with hyperpolarized (13)C-labeled substrates is limited owing to the multidimensional nature of spectroscopic imaging and the transient characteristics of dissolution dynamic nuclear polarization. In this study, a patch-based algorithm (PA) is proposed to enhance spatial resolution of hyperpolarized (13)C human brain images by exploiting compartmental information from the corresponding high-resolution (1)H images. PA was validated in simulation and phantom studies. Effects of signal-to-noise ratio, upsampling factor, segmentation, and slice thickness on reconstructing (13)C images were evaluated in simulation. PA was further applied to low-resolution human brain metabolite maps of hyperpolarized [1-(13)C] pyruvate and [1-(13)C] lactate with 3 compartment segmentations (gray matter, white matter, and cerebrospinal fluid). The performance of PA was compared with other conventional interpolation methods (sinc, nearest-neighbor, bilinear, and spline interpolations). The simulation and the phantom tests showed that PA improved spatial resolution by up to 8 times and enhanced the image contrast without compromising quantification accuracy or losing the intracompartment signal inhomogeneity, even in the case of low signal-to-noise ratio or inaccurate segmentation. PA also improved spatial resolution and image contrast of human (13)C brain images. Dynamic analysis showed consistent performance of the proposed method even with the signal decay along time. In conclusion, PA can enhance low-resolution hyperpolarized (13)C images in terms of spatial resolution and contrast by using a priori knowledge from high-resolution (1)H magnetic resonance imaging while preserving quantification accuracy and intracompartment signal inhomogeneity.