Use of Principal Component Analysis to Study Features Within Functional 1H Lung MRI at 3 T

Motivation: Emerging methods for quantification1H-based, free-breathing lung function face is challenged by confounding cardiac signals and low SNR. Goal(s): This study investigates the potential of principal component analysis (PCA) to isolate signal components linked to specific physiological contrast mechanisms, and also de-noise non-contrast lung data to improve the SNR of the obtained parametric maps. Approach: PCA was applied to dynamic, free-breathing 3T 1H lung MRI prior to VOLVE analysis. Results: The use of PCA provided a method of distinguishing vascular contributions with distinct signal profiles, and produced a marked reduction in noise for ventilation/perfusion assessment. Impact: PCA may provide a method to gain additional functional insight from, and enhance the SNR of, 1H ventilation and perfusion maps.