The measurement of pulmonary perfusion (blood delivered to the capillary bed within a voxel) using arterial spin labeling (ASL) magnetic resonance imaging is often complicated by signal artifacts from conduit vessels that carry blood destined for voxels at a distant location in the lung. One approach to dealing with conduit vessel contributions involves the application of an absolute threshold on the ASL signal. While useful for identifying a subset of the most dominant high signal conduit image features, signal thresholding cannot discriminate between perfusion and conduit vessel contributions at intermediate and low signal. As an alternative, this article discusses a data-driven statistical approach based on statistical clustering for characterizing and discriminating between capillary perfusion and conduit vessel contributions over the full signal spectrum. An ASL flow image is constructed from the difference between a pair of tagged magnetic resonance images. However, when viewed as a bivariate projection that treats the image pair as independent measures (rather than the univariate quantity that results from the subtraction of the two images), the signal associated with capillary perfusion contributions is observed to cluster independently of the signal associated with conduit vessel contributions. Analyzing the observed clusters using a Gaussian mixture model makes it possible to discriminate between conduit vessel and capillary-perfusion-dominated signal contributions over the full signal spectrum of the ASL image. As a demonstration of feasibility, this study compares the proposed clustering approach with the standard absolute signal threshold strategy in a small number of test images.
Keywords: Gaussian mixture model; arterial spin labeling; feature segmentation; image processing; log-normal statistics; pulmonary blood flow; statistical clustering.
Copyright © 2015 John Wiley & Sons, Ltd.