Adaptive higher-order singular value decomposition clutter filter for ultrafast Doppler imaging of coronary flow under non-negligible tissue motion.

BACKGROUND AND OBJECTIVE: With the development of advanced clutter-filtering techniques by singular value decomposition (SVD) and leveraging favorable acquisition settings such as open-chest imaging by a linear high-frequency probe and plane waves, several studies have shown the feasibility of cardiac flow measurements during the entire cardiac cycle, ranging from coronary flow to myocardial perfusion. When applying these techniques in a routine clinical setting, using transthoracic ultrasound imaging, new challenges emerge. Firstly, a smaller aperture is needed that can fit between ribs. Consequently, diverging waves are employed instead of plane waves to achieve an adequate field of view. Secondly, to ensure imaging at a larger depth, the maximum pulse repetition frequency has to be reduced. Lastly, in comparison to the open-chest scenario, tissue motion induced by the heartbeat is significantly stronger. The latter complicates substantially the distinction between clutter and blood signals. METHODS: This study investigates a strategy to overcome these challenges by diverging wave imaging with an optimal number of tilt angles, in combination with dedicated clutter-filtering techniques. In particular, a novel, adaptive, higher-order SVD (HOSVD) clutter filter, which utilizes spatial, temporal, and angular information of the received ultrasound signals, is proposed to enhance clutter and blood separation. RESULTS: When non-negligible tissue motion is present, using fewer tilt angles not only reduces the decorrelation between the received waveforms but also allows for collecting more temporal samples at a given ensemble duration, contributing to improved Doppler performance. The addition of a third angular dimension enables the application of HOSVD, providing greater flexibility in selecting blood separation thresholds from a 3-D tensor. This differs from the conventional threshold selection method in a 2-D spatiotemporal space using SVD. Exhaustive threshold search has shown a significant improvement in Contrast and Contrast-to-Noise ratio for Power Doppler images filtered with HOSVD compared to the SVD-based clutter filter. CONCLUSION: With the improved settings, the obtained Power Doppler images show the feasibility of measuring coronary flow under the influence of non-negligible tissue motion in both in vitro and ex vivo.

Contrast-Enhanced Ultrasound Angiogenesis Imaging by Mutual Information Analysis for Prostate Cancer Localization.

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.

Multiparametric dynamic contrast-enhanced ultrasound imaging of prostate cancer.

OBJECTIVES: The aim of this study is to improve the accuracy of dynamic contrast-enhanced ultrasound (DCE-US) for prostate cancer (PCa) localization by means of a multiparametric approach. MATERIALS AND METHODS: Thirteen different parameters related to either perfusion or dispersion were extracted pixel-by-pixel from 45 DCE-US recordings in 19 patients referred for radical prostatectomy. Multiparametric maps were retrospectively produced using a Gaussian mixture model algorithm. These were subsequently evaluated on their pixel-wise performance in classifying 43 benign and 42 malignant histopathologically confirmed regions of interest, using a prostate-based leave-one-out procedure. RESULTS: The combination of the spatiotemporal correlation (r), mean transit time (µ), curve skewness (κ), and peak time (PT) yielded an accuracy of 81% ± 11%, which was higher than the best performing single parameters: r (73%), µ (72%), and wash-in time (72%). The negative predictive value increased to 83% ± 16% from 70%, 69% and 67%, respectively. Pixel inclusion based on the confidence level boosted these measures to 90% with half of the pixels excluded, but without disregarding any prostate or region. CONCLUSIONS: Our results suggest multiparametric DCE-US analysis might be a useful diagnostic tool for PCa, possibly supporting future targeting of biopsies or therapy. Application in other types of cancer can also be foreseen. KEY POINTS: • DCE-US can be used to extract both perfusion and dispersion-related parameters. • Multiparametric DCE-US performs better in detecting PCa than single-parametric DCE-US. • Multiparametric DCE-US might become a useful tool for PCa localization.

Fractal Dimension of Tumor Microvasculature by DCE-US: Preliminary Study in Mice.

Neoangiogenesis, which results in the formation of an irregular network of microvessels, plays a fundamental role in the growth of several types of cancer. Characterization of microvascular architecture has therefore gained increasing attention for cancer diagnosis, treatment monitoring and evaluation of new drugs. However, this characterization requires immunohistologic analysis of the resected tumors. Currently, dynamic contrast-enhanced ultrasound imaging (DCE-US) provides new options for minimally invasive investigation of the microvasculature by analysis of ultrasound contrast agent (UCA) transport kinetics. In this article, we propose a different method of analyzing UCA concentration that is based on the spatial distribution of blood flow. The well-known concept of Mandelbrot allows vascular networks to be interpreted as fractal objects related to the regional blood flow distribution and characterized by their fractal dimension (FD). To test this hypothesis, the fractal dimension of parametric maps reflecting blood flow, such as UCA wash-in rate and peak enhancement, was derived for areas representing different microvascular architectures. To this end, subcutaneous xenograft models of DU-145 and PC-3 prostate-cancer lines in mice, which show marked differences in microvessel density spatial distribution inside the tumor, were employed to test the ability of DCE-US FD analysis to differentiate between the two models. For validation purposes, the method was compared with immunohistologic results and UCA dispersion maps, which reflect the geometric properties of microvascular architecture. The results showed good agreement with the immunohistologic analysis, and the FD analysis of UCA wash-in rate and peak enhancement maps was able to differentiate between the two xenograft models (p < 0.05).

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.

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.

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.

Angiogenesis imaging by spatiotemporal analysis of ultrasound contrast agent dispersion kinetics.

The key role of angiogenesis in cancer growth has motivated extensive research with the goal of noninvasive cancer detection by blood perfusion imaging. However, the results are still limited and the diagnosis of major forms of cancer, such as prostate cancer, are currently based on systematic biopsies. The difficulty in the detection of angiogenesis partly resides in a complex relationship between angiogenesis and perfusion. This may be overcome by analysis of the dispersion kinetics of ultrasound contrast agents. Determined by multipath trajectories through the microvasculature, dispersion permits a better characterization of the microvascular architecture and, therefore, more accurate detection of angiogenesis. In this paper, a novel dispersion analysis method is proposed for prostate cancer localization. An ultrasound contrast agent bolus is injected intravenously. Spatiotemporal analysis of the concentration evolution measured at different pixels in the prostate is used to assess the local dispersion kinetics of the injected agent. In particular, based on simulations of the convective diffusion equation, the similarity between the concentration evolutions at neighbor pixels is the adopted dispersion measure. Six measurements in patients, compared with the histology, provided a receiver operating characteristic curve integral equal to 0.87. This result was superior to that obtained by the previous approaches reported in the literature.

Contrast-ultrasound diffusion imaging for localization of prostate cancer.

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.