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Objective: The role of angiogenesis in cancer growth has stimulated research aimed at noninvasive cancer detection by blood perfusion imaging. Recently, contrast ultrasound dispersion imaging was proposed as an alternative method for angiogenesis imaging. After the intravenous injection of an ultrasound-contrast-agent bolus, dispersion can be indirectly estimated from the local similarity between neighboring time-intensity curves (TICs) measured by ultrasound imaging. Up until now, only linear similarity measures have been investigated. Motivated by the promising results of this approach in prostate cancer (PCa), we developed a novel dispersion estimation method based on mutual information, thus including nonlinear similarity, to further improve its ability to localize PCa., Methods: First, a simulation study was performed to establish the theoretical link between dispersion and mutual information. Next, the method's ability to localize PCa was validated in vivo in 23 patients (58 datasets) referred for radical prostatectomy by comparison with histology., Results: A monotonic relationship between dispersion and mutual information was demonstrated. The in vivo study resulted in a receiver operating characteristic (ROC) curve area equal to 0.77, which was superior (p = 0.21-0.24) to that obtained by linear similarity measures (0.74-0.75) and (p <; 0.05) to that by conventional perfusion parameters (≤0.70)., Conclusion: Mutual information between neighboring time-intensity curves can be used to indirectly estimate contrast dispersion and can lead to more accurate PCa localization., Significance: An improved PCa localization method can possibly lead to better grading and staging of tumors, and support focal-treatment guidance. Moreover, future employment of the method in other types of angiogenic cancer can be considered.

Prostate cancer care can benefit from accurate and cost-efficient imaging modalities that are able to reveal prognostic indicators for cancer. Angiogenesis is known to play a central role in the growth of tumors towards a metastatic or a lethal phenotype. With the aim of localizing angiogenic activity in a non-invasive manner, Dynamic Contrast Enhanced Ultrasound (DCE-US) has been widely used. Usually, the passage of ultrasound contrast agents thought the organ of interest is analyzed for the assessment of tissue perfusion. However, the heterogeneous nature of blood flow in angiogenic vasculature hampers the diagnostic effectiveness of perfusion parameters. In this regard, quantification of the heterogeneity of flow may provide a relevant additional feature for localizing angiogenesis. Statistics based on flow magnitude as well as its orientation can be exploited for this purpose. In this paper, we estimate the microbubble velocity fields from a standard bolus injection and provide a first statistical characterization by performing a spatial entropy analysis. By testing the method on 24 patients with biopsy-proven prostate cancer, we show that the proposed method can be applied effectively to clinically acquired DCE-US data. The method permits estimation of the in-plane flow vector fields and their local intricacy, and yields promising results (receiver-operating-characteristic curve area of 0.85) for the detection of prostate cancer.