Abstract
The achievable spatial resolution of (13)C metabolic images acquired with hyperpolarized (13)C-pyruvate is worse than (1)H images typically by an order of magnitude due to the rapidly decaying hyperpolarized signals and the low gyromagnetic ratio of (13)C. This study is to develop and characterize a volumetric patch-based super-resolution reconstruction algorithm that enhances spatial resolution (13)C cardiac MRI by utilizing structural information from (1)H MRI. The reconstruction procedure comprises anatomical segmentation from high-resolution (1)H MRI, calculation of a patch-based weight matrix, and iterative reconstruction of high-resolution multi-slice (13)C MRI. The method was tested with a multi-compartmental digital phantom for optimizing the patch size and an anthropomorphic cardiac MR phantom for validating the performance. Finally, the method was applied to human cardiac (13)C images, acquired with an injection of hyperpolarized [1-(13)C]pyruvate. The phantom studies demonstrated that high-resolution multi-slice (13)C images, reconstructed from a single-slice low-resolution input (13)C image, retained the signal intensity range. The reconstruction accuracy was asymptotically improved as the patch size increased whereas intra-segmental spatial fluctuations were preserved better with smaller patches. However, a structurally non-identified tissue region was not restored regardless of the patch size. The cardiac MR phantom and the human cardiac images demonstrated improved spatial resolutions in the reconstructed images (10 × 10 × 30 mm(3)/voxel to 2 × 2 × 5 mm(3)/voxel). The volumetric patch-based super-resolution method reconstructs multi-slice high-resolution of (13)C images, enhancing the cardiac structure, while preserving the quantitative accuracy. The proposed method is applicable to other multi-modal images that suffer from limited spatial resolution.