Optimally-truncated SVD and PCA applied to spatio-temporal hyperpolarized 13C cardiac data

Motivation: Current hyperpolarized data analysis techniques, first validated for interrogating cancer, are now being utilized for HP 13C cardiac MRI which is metabolically diverse. Goal(s): Our goal was to explore the viability of applying advanced data analysis techniques to previously acquired HP 13C cardiac data to derive novel and potentially impactful information. Approach: We applied optimally-truncated SVD based PCA to fasted and fed state [1-13C] pyruvate images and k-means clustering on resulting data. Results: Low-rank (r=3) PCA results captured sufficient data to represent the pyruvate images and identify plausible separable components of interest. Clustering spatially resolving data sufficiently for potential further constrained analyses. Impact: Hyperpolarized 13C cardiac MRI can be analyzed using PCA to derive novel and potentially impactful voxel-wise analysis. As use of HP 13C MRI expands to new applications, advanced analysis techniques can be utilized to better characterize complex alterations in metabolism.