Contrast-enhanced angiogenesis imaging by mutual information analysis (original) (raw)
Related papers
Contrast-ultrasound dispersion imaging of cancer neovascularization by mutual-information analysis
2014 IEEE International Ultrasonics Symposium, 2014
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.
2013
Angiogenesis plays a major role in prostate cancer growth. Despite extensive research on blood perfusion imaging aimed at angiogenesis detection, the diagnosis of prostate cancer still requires systematic biopsies. This may be due to the complex relationship between angiogenesis and microvascular perfusion. Analysis of ultrasound-contrast-agent dispersion kinetics, determined by multipath trajectories in the microcirculation, may provide better characterization of the microvascular architecture. We propose the physical rationale for dispersion estimation by an existing spatiotemporal similarity analysis. After an intravenous ultrasoundcontrast-agent bolus injection, dispersion is estimated by coherence analysis among time-intensity curves measured at neighbor pixels. The accuracy of the method is increased by time-domain windowing and anisotropic spatial filtering for speckle regularization. The results in 12 patient data sets indicated superior agreement with histology (receiver operating characteristic curve area 5 0.88) compared with those obtained by reported perfusion and dispersion analyses, providing a valuable contribution to prostate cancer
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2000
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 timeintensity 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.
IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control
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 timeintensity 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.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control, 2015
Currently, nonradical treatment for prostate cancer is hampered by the lack of reliable diagnostics. Contrastultrasound dispersion imaging (CUDI) has recently shown great potential as a prostate cancer imaging technique. CUDI estimates the local dispersion of intravenously injected contrast agents, imaged by transrectal dynamic contrast-enhanced ultrasound (DCE-US), to detect angiogenic processes related to tumor growth. The best CUDI results have so far been obtained by similarity analysis of the contrast kinetics in neighboring pixels. To date, CUDI has been investigated in 2-D only. In this paper, an implementation of 3-D CUDI based on spatiotemporal similarity analysis of 4-D DCE-US is described. Different from 2-D methods, 3-D CUDI permits analysis of the entire prostate using a single injection of contrast agent. To perform 3-D CUDI, a new strategy was designed to estimate the similarity in the contrast kinetics at each voxel, and data processing steps were adjusted to the cha...
2014 IEEE International Ultrasonics Symposium, 2014
Prostate cancer (PCa) is the type of cancer with the highest incidence in men in Western countries. To date, reliable tools for PCa localization are lacking. Recently, contrastultrasound dispersion imaging (CUDI) by spatiotemporal analysis performed on transrectal dynamic contrast-enhanced ultrasound (DCE-US) has been proposed as a promising option for PCa localization. This technique evaluates the spatial similarity between indicator dilution curves in a ring-shaped kernel and its center pixel. Until now, CUDI has been performed in 2D only. Hence, each imaged plane requires a separate bolus injection of contrast agent, motion compensation is limited, and out-of-plane contrast flow cannot be observed. 3D DCE-US can potentially solve the aforementioned issues, permitting the analysis of the entire prostate with a single bolus injection. In this work, we implemented a full 4D spatiotemporal similarity analysis. Its feasibility to localize PCa was evaluated in 2 patients by qualitatively comparing similarity maps obtained by 3D CUDI with those obtained by 2D CUDI in the corresponding planes and with histopathologic results from 12-core systematic biopsies. All results showed good agreement, confirming the feasibility of 3D CUDI for PCa localization and encouraging extension of the study to a larger dataset. Additionally, the characteristics -and in particular the spatial and temporal resolution -of 3D DCE-US were analyzed with respect to the requirements for CUDI. Both the spatial and temporal resolution were considered to be sufficient for CUDI.
New developments in prostate cancer localization by contrast ultrasound dispersion imaging
Chemical Engineering & Technology, 2012
In the United States, prostate cancer (PCa) accounts for 29% and 11% of all cancer diagnoses and deaths in men, respectively . Although efficient focal therapies are available, their applicability is hampered by a lack of imaging solutions. Despite their invasiveness and poor spatial accuracy, systematic biopsies remain the most reliable option for PCa localization. Contrast-enhanced ultrasound imaging has recently opened new possibilities for PCa localization. Based on a proven correlation between cancer aggressiveness and angiogenesis [2, 3], several imaging methods have been proposed that are based on blood-perfusion assessment. However, possibly due to the complex hemodynamic effects produced by angiogenesis , no method has yet generated reliable results. We have recently proposed contrast-ultrasound dispersion imaging (CUDI) as a new alternative method for PCa localization . Different from perfusion, whose relation with angiogenesis is affected by opposing effects , the intravascular dispersion of ultrasound contrast agents is directly influenced by angiogenic changes in the microvascular architecture.
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 washin 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. Abbreviations AT Appearance time CUDI Contrast-ultrasound dispersion imaging DCE-US Dynamic contrast-enhanced ultrasound FWHM Full width half mximum GMM Gaussian mixture model LDRW Local density random walk mpMRI Multiparametric magnetic resonance imaging NPV Negative predictive value PCa Prostate cancer PPV Positive predictive value PSA Prostate-specific antigen PT Peak time ROC Receiver operating characteristic ROI Region of interest TIC Time-intensity curve TRUS Transrectal ultrasound UCA Ultrasound contrast agent WIT Wash-in time * Rogier R. Wildeboer