Maarten P. J. Kuenen

Correspondence - Spatiotemporal correlation of ultrasound contrast agent dilution curves for angiogenesis localization by dispersion imaging

The major role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer imaging based on assessment of microvascular perfusion. The limited results so far may be caused by the complex and contradictory effects of angiogenesis on perfusion. Alternatively, assessment of ultrasound contrast agent dispersion kinetics, resulting from features such as density and tortuosity, has shown a promising potential to characterize angiogenic effects on the microvascular structure. This method, referred to as contrast-ultrasound dispersion imaging (CUDI), is based on contrast-enhanced ultrasound imaging after an intravenous contrast agent bolus injection. In this paper, we propose a new spatiotemporal correlation analysis to perform CUDI. We provide the rationale for indirect estimation of local dispersion by deriving the analytical relation between dispersion and the correlation coefficient among neighboring time-intensity curves obtained at each pixel. This robust analysis is inherently normalized and does not require curve-fitting. In a preliminary validation of the method for localization of prostate cancer, the results of this analysis show superior cancer localization performance (receiver operating characteristic curve area of 0.89) compared with those of previously reported CUDI implementations and perfusion estimation methods.

Maximum-Likelihood Estimation for Indicator Dilution Analysis

Indicator-dilution methods are widely used by many medical imaging techniques and by dye-, lithium-, and thermodilution measurements. The measured indicator dilution curves are typically fitted by a mathematical model to estimate the hemodynamic parameters of interest. This paper presents a new maximum-likelihood algorithm for parameter estimation, where indicator dilution curves are considered as the histogram of underlying transit-time distribution. Apart from a general description of the algorithm, semianalytical solutions are provided for three well-known indicator dilution models. An adaptation of the algorithm is also introduced to cope with indicator recirculation. In simulations as well as in experimental data obtained by dynamic contrast-enhanced ultrasound imaging, the proposed algorithm shows a superior parameter estimation accuracy over nonlinear least-squares regression. The feasibility of the algorithm for use in vivo is evaluated using dynamic contrast-enhanced ultrasound recordings obtained with the purpose of prostate cancer detection. The proposed algorithm shows an improved ability (increase in receiver-operating characteristic curve area of up to 0.13) with respect to existing methods to differentiate between healthy tissue and cancer.

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.

Contrast ultrasound dispersion imaging of different tumor types

Contrast-enhanced ultrasound imaging is a promising approach for prostate cancer detection by analysis of ultrasound-contrast-agent (UCA) transport kinetics. We have recently proposed the assessment of UCA dispersion kinetics as a valuable tool for characterizing prostate cancer microvascular architectures. To this end, a convective dispersion model is fitted to measured UCA time concentration curves. In this study, spatial coherence analysis is introduced as an alternative reliable method for estimation of UCA dispersion. An analytical monotonic relation between spatial coherence and UCA dispersion is derived. Coherence analysis is therefore proposed for classification of different tumor types. To this end, DU-145 and PC-3 human prostate cancer lines are studied in mice xenograft models. These models are investigated by coherence analysis, aiming at the characterization of their microvascular architecture. The results show a good correlation between microvascular density (MVD) and the obtained UCA dispersion (coherence) maps with p-value <; 0.01, suggesting contrast ultrasound dispersion imaging as a valuable non invasive option for characterization of MVD.

Transabdominal Contrast-Enhanced Ultrasound Imaging of the Prostate

Numerous age-related pathologies affect the prostate gland, the most menacing of which is prostate cancer (PCa). The diagnostic tools for prostate investigation are invasive, requiring biopsies when PCa is suspected. Novel dynamic contrast-enhanced ultrasound (DCE-US) imaging approaches have been proposed recently and appear promising for minimally invasive localization of PCa. Ultrasound imaging of the prostate is traditionally performed with a transrectal probe because the location of the prostate allows for high-resolution images using high-frequency transducers. However, DCE-US imaging requires lower frequencies to induce bubble resonance and, thus, improve contrast-to-tissue ratio. For this reason, in this study we investigate the feasibility of quantitative DCE-US imaging of the prostate via the abdomen. The study included 10 patients (age = 60.7 ± 5.7 y) referred for a needle biopsy study. After having given informed consent, patients underwent DCE-US with both transabdominal and transrectal probes. Time-intensity contrast curves were derived using both approaches and their model-fit quality was compared. Although further improvements are expected by optimization of the transabdominal settings, the results of transabdominal and transrectal DCE-US are closely comparable, confirming the feasibility of transabdominal DCE-US; transabdominal curve fitting revealed an average determination coefficient r(2) = 0.91 (r(2) > 0.75 for 78.6% of all prostate pixels) compared with r(2) = 0.91 (r(2) > 0.75 for 81.6% of all prostate pixels) by the transrectal approach. Replacing the transrectal approach with more acceptable transabdominal scanning for prostate investigation is feasible. This approach would improve patient comfort and represent a useful option for PCa localization and monitoring.

Spatiotemporal methods for prostate cancer detection by contrast-ultrasound dispersion imaging

Prostate cancer is the most diagnosed form of cancer in men in western countries. The lack of reliable imaging solutions currently restricts treatment options to radical interventions. Reliable imaging of angiogenesis, which correlates with cancer aggressiveness, could enable efficient targeting of biopsies and focal therapies, but the complicated effects of angiogenesis on perfusion have prevented a breakthrough so far. As an alternative, contrast-ultrasound dispersion imaging (CUDI) has been proposed to obtain a better characterization of angiogenenic microvascular changes. CUDI makes use of dynamic contrast-enhanced ultrasound imaging after an intravenous contrast-agent bolus injection. Dispersion is estimated by spatiotemporal analysis of time-intensity curves (TICs) obtained at each pixel. Here we present an analytical framework for a dispersion analysis by estimation of the spatial TIC similarity. Data preprocessing is improved by a spatial Wiener deconvolution filter, which reduces the effects of the anisotropic spatial ultrasound resolution, and by TIC time windowing. In addition, we propose the TIC correlation coefficient as a new TIC similarity measure. A comparison of the resulting dispersion maps from 7 recordings with histology, obtained after radical prostatectomy, showed an increased spatial similarity in the presence of cancer in all patients. The cancer classification performance of CUDI was superior to all perfusion-related parameters and was improved by spatial filtering and windowing. Although an extended validation is required, these results confirm the promising value of CUDI for prostate cancer detection.

A multiparametric approach for dynamic contrast-enhanced ultrasound imaging of prostate cancer

Prostate cancer (PCa) is the most commonly diagnosed type of non-cutaneous cancer in American men [1]. A sufficiently reliable PCa imaging method is currently not available, leaving systematic biopsy as the guideline-recommended technique for PCa diagnosis [2]. Dynamic Contrast-Enhanced Ultrasound (DCE-US) is currently being studied for the characterization of prostatic tissue, since it might prove able to reveal the vascular changes associated with (PCa) angiogenesis [3]. The effect of PCa on blood flow, however, is ambiguous [4]; assessment of the contrast agent kinetics that is more objective than the visual inspection of the contrast video is therefore required.

26 Lokalisatie van prostaatkanker met contrast dispersion beeldvorming

SamenvattingGebrek aan een betrouwbare beeldvormende techniek voor de lokalisatie van prostaatkanker maakt het gebruik van onder andere gerichte biopten, active surveillance en focale therapie moeilijk.