Principal Component Analysis Reconstruction Improves Visualization of Multivolume MR Spectroscopic Imaging at 1.5 T

Motivation: We address the critical problem of the low signal-to-noise ratio (SNR) of in vivo MR spectroscopic imaging (MRSI) at 1.5 tesla using principal component analysis (PCA). Goal(s): We seek to improve the translatability of MRSI acquired in 1.5T clinical scanners by improving data visualization using PCA reconstruction (PCAR). Approach: We initially corrected for spectral phase and frequency variations in multivolume 31P and 1H MRSI data noninvasively acquired at 1.5 T from human subjects and subsequently used PCAR. Results: We validate the improvement of the MRSI data after PCAR by demonstrating signals that were not distinguishable in the original datasets. Impact: Principal component analysis reconstruction (PCAR) improves the accuracy to assess spectral signals from multivolume 31P and 1H MR spectroscopic imaging data acquired on highly accessible 1.5T clinical scanners, increasing their potential to become noninvasive metabolic biomarkers of human diseases.