Contrast-ultrasound dispersion imaging of cancer neovascularization by mutual-information analysis

Being an established marker for cancer growth, neovascularization is probed by several approaches with the aim of cancer imaging. Recently, analysis of the dispersion kinetics of ultrasound contrast agents (UCAs) has been proposed as a promising approach for localizing neovascularization in prostate cancer. Determined by multipath trajectories through the microvasculature, dispersion enables characterization of the microvascular architecture and, therefore, localization of cancer neovascularization. Analysis of the spatiotemporal similarity among indicator dilution curves (IDCs) measured at each pixel by dynamic contrast-enhanced ultrasound imaging has been proposed to assess the local dispersion kinetics of UCAs. Only linear similarity measures, such as temporal correlation or spectral coherence, have been used up until now. Here we investigate the use of nonlinear similarity measures by estimation of the statistical dependency between IDCs. In particular, dispersion maps are generated by estimation of the mutual information between IDCs. The method is tested for prostate cancer localization and the results compared with the histology results in 15 patients referred for radical prostatectomy because of biopsy-proven prostate cancer. With sensitivity and specificity equal to 84% and 85%, respectively, and receiver operating characteristic curve area equal to 0.92, our results outperformed those obtained by any other parameter, motivating further validation with a larger dataset and with other types of cancer.