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Abstract #2271

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

Zachary Peggs1,2, Susan Francis1,2, and Penny Gowland1,2
1Sir Peter Mansfield Imaging Centre, University of Nottingham, Nottingham, United Kingdom, 2NIHR Nottingham Biomedical Research Centre (BRC), Respiratory Medicine, School of Medicine, University of Nottingham, Nottingham, United Kingdom

Synopsis

Keywords: Lung, Lung, PCA

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.

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