A Robust Method for Estimating Regional Pulmonary Parameters in Presence of Noise

Abstract

Estimation of regional lung function parameters from hyperpolarized gas magnetic resonance images can be very sensitive to presence of noise. Clustering pixels and averaging over the resulting groups is an effective method for reducing the effects of noise in these images, commonly performed by grouping proximal pixels together, thus creating large groups called "bins." This method has several drawbacks, primarily that it can group dissimilar pixels together, and it degrades spatial resolution. This study presents an improved approach to simplifying data via principal component analysis (PCA) when noise level prohibits a pixel-by-pixel treatment of data, by clustering them based on similarity to one another rather than spatial proximity. The application to this technique is demonstrated in measurements of regional lung oxygen tension using hyperpolarized (3)He magnetic resonance imaging (MRI). A synthetic dataset was generated from an experimental set of oxygen tension measurements by treating the experimentally derived parameters as "true" values, and then solving backwards to generate "noiseless" images. Artificial noise was added to the synthetic data, and both traditional binning and PCA-based clustering were performed. For both methods, the root-mean-square (RMS) error between each pixel's "estimated" and "true" parameters was computed and the resulting effects were compared. At high signal-to-noise ratios (SNRs), clustering did not enhance accuracy. Clustering did, however, improve parameter estimations for moderate SNR values (below 100). For SNR values between 100 and 20, the PCA-based K-means clustering analysis yielded greater accuracy than Cartesian binning. In extreme cases (SNR<5), Cartesian binning can be more accurate. The reliability of parameters estimation in imaging-based regional functional measurements can be improved in the presence of noise by utilizing principal component analysis-based clustering without sacrificing spatial resolution compared to Cartesian binning. Results suggest that this approach has a great potential for robust grouping of pixels in hyperpolarized (3)He MRI maps of lung oxygen tension.