Contrast-Ultrasound Diffusion Imaging for Localization of Prostate Cancer

Abstract

Prostate cancer is the most prevalent form of cancer in western men. An accurate early localization of prostate cancer, permitting efficient use of modern focal therapies, is currently hampered by a lack of imaging methods. Several methods have aimed at detecting microvascular changes associated with prostate cancer with limited success by quantitative imaging of blood perfusion. Differently, we propose contrast-ultrasound diffusion imaging, based on the hypothesis that the complexity of microvascular changes is better reflected by diffusion than by perfusion characteristics. Quantification of local, intravascular diffusion is performed after transrectal ultrasound imaging of an intravenously injected ultrasound contrast agent bolus. Indicator dilution curves are measured with the ultrasound scanner resolution and fitted by a modified local density random walk model, which, being a solution of the convective diffusion equation, enables the estimation of a local, diffusion-related parameter. Diffusion parametric images obtained from five datasets of four patients were compared with histology data on a pixel basis. The resulting receiver operating characteristic (curve area = 0.91) was superior to that of any perfusion-related parameter proposed in the literature. Contrast-ultrasound diffusion imaging seems therefore to be a promising method for prostate cancer localization, encouraging further research to assess the clinical reliability.

Angiogenesis in prostate cancer: onset, progression and imaging

“…microvascular architecture by investigating the dispersion kinetics of UCAs through the microvasculature [ 98,128 ] . The preliminary results are promising [ 97 ] ( Fig.…”

Transabdominal Contrast-Enhanced Ultrasound Imaging of the Prostate

“…Before this, the data was linearized (Kuenen et al 2011). We used the local density random walk model, which describes the convection-dispersion process of the microbubbles and is extensively reported to produce accurate fits of TICs (Mischi et al 2003;Strouthos et al 2010).…”

“…We used the local density random walk model, which describes the convection-dispersion process of the microbubbles and is extensively reported to produce accurate fits of TICs (Mischi et al 2003;Strouthos et al 2010). The model-fitting algorithm proposed in Kuenen et al (2011) was adopted. At each pixel in the prostate, the fit quality was assessed by the determination coefficient r 2 , which is commonly used to indicate the agreement between a model and the data (Menard 2000).…”

“…Typical features extracted are the mean transit time, wash-in rate and area under the curve. More recently, some authors have proposed UCA dispersion as a better marker than perfusion for detection of angiogenic changes in the microvascular architecture (Kuenen et al 2011). The results obtained in the prostate are promising and have motivated the development of improved algorithms for dispersion analysis (Kuenen et al 2013b;Mischi et al 2012).…”

Observed cumulative time delay between second harmonic and fundamental component of pressure wave fields propagating through ultrasound contrast agents

“…Ultrasound contrast agents (UCAs) are intravascular contrast agents with size comparable to red blood cells and made of gas-filled microbubbles encapsulated in a lipid shell. Thanks to their high echogenicity, UCAs can be utilized in combination with ultrasound imaging to quantify perfusion [1], blood flow [2], and to localize cancer [3,4,5]. To better discriminate UCAs from tissue, the nonlinear response of microbubbles to ultrasound may be exploited.…”