Angiogenesis imaging by spatiotemporal analysis of ultrasound contrast agent dispersion kinetics

Can 3D Multiparametric Ultrasound Imaging Predict Prostate Biopsy Outcome?

OBJECTIVES

To assess the value of 3D multiparametric ultrasound imaging, combining hemodynamic and tissue stiffness quantifications by machine learning, for the prediction of prostate biopsy outcomes.

METHODS

After signing informed consent, 54 biopsy-naïve patients underwent a 3D dynamic contrast-enhanced ultrasound (DCE-US) recording, a multi-plane 2D shear-wave elastography (SWE) scan with manual sweeping from base to apex of the prostate, and received 12-core systematic biopsies (SBx). 3D maps of 18 hemodynamic parameters were extracted from the 3D DCE-US quantification and a 3D SWE elasticity map was reconstructed based on the multi-plane 2D SWE acquisitions. Subsequently, all the 3D maps were segmented and subdivided into 12 regions corresponding to the SBx locations. Per region, the set of 19 computed parameters was further extended by derivation of eight radiomic features per parameter. Based on this feature set, a multiparametric ultrasound approach was implemented using five different classifiers together with a sequential floating forward selection method and hyperparameter tuning. The classification accuracy with respect to the biopsy reference was assessed by a group-k-fold cross-validation procedure, and the performance was evaluated by the Area Under the Receiver Operating Characteristics Curve (AUC).

RESULTS

Of the 54 patients, 20 were found with clinically significant prostate cancer (csPCa) based on SBx. The 18 hemodynamic parameters showed mean AUC values varying from 0.63 to 0.75, and SWE elasticity showed an AUC of 0.66. The multiparametric approach using radiomic features derived from hemodynamic parameters only produced an AUC of 0.81, while the combination of hemodynamic and tissue-stiffness quantifications yielded a significantly improved AUC of 0.85 for csPCa detection (p-value < 0.05) using the Gradient Boosting classifier.

CONCLUSIONS

Our results suggest 3D multiparametric ultrasound imaging combining hemodynamic and tissue-stiffness features to represent a promising diagnostic tool for biopsy outcome prediction, aiding in csPCa localization.

Spatiotemporal analysis of contrast-enhanced ultrasound for differentiating between malignant and benign breast lesions

OBJECTIVES

The aim of this study was to apply spatiotemporal analysis of contrast-enhanced ultrasound (CEUS) loops to quantify the enhancement heterogeneity for improving the differentiation between benign and malignant breast lesions.

MATERIALS AND METHODS

This retrospective study included 120 women (age range, 18-82 years; mean, 52 years) scheduled for ultrasound-guided biopsy. With the aid of brightness-mode images, the border of each breast lesion was delineated in the CEUS images. Based on visual evaluation and quantitative metrics, the breast lesions were categorized into four grades of different levels of contrast enhancement. Grade-1 (hyper-enhanced) and grade-2 (partly-enhanced) breast lesions were included in the analysis. Four parameters reflecting enhancement heterogeneity were estimated by spatiotemporal analysis of neighboring time-intensity curves (TICs). By setting the threshold on mean parameter, the diagnostic performance of the four parameters for differentiating benign and malignant lesions was evaluated.

RESULTS

Sixty-four of the 120 patients were categorized as grade 1 or 2 and used for estimating the four parameters. At the pixel level, mutual information and conditional entropy present significantly different values between the benign and malignant lesions (p < 0.001 in patients of grade 1, p = 0.002 in patients of grade 1 or 2). For the classification of breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893 in patients of grade 1, AUC = 0.848 in patients of grade 1 or 2).

CONCLUSIONS

The proposed spatiotemporal analysis for assessing the enhancement heterogeneity shows promising results to aid in the diagnosis of breast cancer by CEUS.

CLINICAL RELEVANCE STATEMENT

The proposed spatiotemporal method can be developed as a standardized software to automatically quantify the enhancement heterogeneity of breast cancer on CEUS, possibly leading to the improved diagnostic accuracy of differentiation between benign and malignant lesions.

KEY POINTS

• Advanced spatiotemporal analysis of ultrasound contrast-enhanced loops for aiding the differentiation of malignant or benign breast lesions. • Four parameters reflecting the enhancement heterogeneity were estimated in the hyper- and partly-enhanced breast lesions by analyzing the neighboring pixel-level time-intensity curves. • For the classification of hyper-enhanced breast lesions, mutual information produces the best diagnostic performance (AUC = 0.893).

Clinical Trial Protocol: Developing an Image Classification Algorithm for Prostate Cancer Diagnosis on Three-dimensional Multiparametric Transrectal Ultrasound

Introduction and hypothesis

The tendency toward population-based screening programs for prostate cancer (PCa) is expected to increase demand for prebiopsy imaging. This study hypothesizes that a machine learning image classification algorithm for three-dimensional multiparametric transrectal prostate ultrasound (3D mpUS) can detect PCa accurately.

Design

This is a phase 2 prospective multicenter diagnostic accuracy study. A total of 715 patients will be included in a period of approximately 2 yr. Patients are eligible in case of suspected PCa for which prostate biopsy is indicated or in case of biopsy-proven PCa for which radical prostatectomy (RP) will be performed. Exclusion criteria are prior treatment for PCa or contraindications for ultrasound contrast agents (UCAs).

Protocol overview

Study participants will undergo 3D mpUS, consisting of 3D grayscale, 4D contrast-enhanced ultrasound, and 3D shear wave elastography (SWE). Whole-mount RP histopathology will provide the ground truth to train the image classification algorithm. Patients included prior to prostate biopsy will be used for subsequent preliminary validation. There is a small, anticipated risk for participants associated with the administration of a UCA. Informed consent has to be given prior to study participation, and (serious) adverse events will be reported.

Statistical analysis

The primary outcome will be the diagnostic performance of the algorithm for detecting clinically significant PCa (csPCa) on a per-voxel and a per-microregion level. Diagnostic performance will be reported as the area under the receiver operating characteristic curve. Clinically significant PCa is defined as the International Society of Urological grade group ≥2. Full-mount RP histopathology will be used as the reference standard. Secondary outcomes will be sensitivity, specificity, negative predictive value, and positive predictive value for csPCa on a per-patient level, evaluated in patients included prior to prostate biopsy, using biopsy results as the reference standard. A further analysis will be performed on the ability of the algorithm to differentiate between low-, intermediate-, and high-risk tumors.

Discussion and summary

This study aims to develop an ultrasound-based imaging modality for PCa detection. Subsequent head-to-head validation trials with magnetic resonance imaging have to be performed in order to determine its role in clinical practice for risk stratification in patients suspected for PCa.

The evolving role of contrast-enhanced ultrasound in urology: a review

PURPOSE

Ultrasound's versatility and ease of use has expanded its application in many clinical settings. Technological advancements with contrast-enhanced ultrasound (CEUS) have allowed high quality imaging similar to CT or MRI with lower risk of contrast toxicity and radiation exposure. In this review article we examine the development of CEUS and its vast applications in the field of urology.

METHODS

A PubMed literature search was performed using keywords: contrast enhanced ultrasound, prostate cancer, renal cancer, and multiparametric ultrasound.

RESULTS

The development of CEUS has improved transrectal ultrasound imaging with increased detection of prostate cancer (PCa). Further enhancements of CEUS such as subharmonic imaging (SHI), flash replenishment imaging (FRI) and contrast ultrasound dispersion imaging (CUDI) allow improved PCa diagnosis. CEUS has also emerged as an important tool in characterizing suspicious renal mass without compromising renal function with contrast imaging.

CONCLUSION

CEUS has modernized imaging and diagnosis of prostate and renal cancer. Future advancements and utilization of CEUS will allow its expansion into other urological subspecialties.

Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast-Enhanced Ultrasound

This work proposes an interpretable radiomics approach to differentiate between malignant and benign focal liver lesions (FLLs) on contrast-enhanced ultrasound (CEUS). Although CEUS has shown promise for differential FLLs diagnosis, current clinical assessment is performed only by qualitative analysis of the contrast enhancement patterns. Quantitative analysis is often hampered by the unavoidable presence of motion artifacts and by the complex, spatiotemporal nature of liver contrast enhancement, consisting of multiple, overlapping vascular phases. To fully exploit the wealth of information in CEUS, while coping with these challenges, here we propose combining features extracted by the temporal and spatiotemporal analysis in the arterial phase enhancement with spatial features extracted by texture analysis at different time points. Using the extracted features as input, several machine learning classifiers are optimized to achieve semiautomatic FLLs characterization, for which there is no need for motion compensation and the only manual input required is the location of a suspicious lesion. Clinical validation on 87 FLLs from 72 patients at risk for hepatocellular carcinoma (HCC) showed promising performance, achieving a balanced accuracy of 0.84 in the distinction between benign and malignant lesions. Analysis of feature relevance demonstrates that a combination of spatiotemporal and texture features is needed to achieve the best performance. Interpretation of the most relevant features suggests that aspects related to microvascular perfusion and the microvascular architecture, together with the spatial enhancement characteristics at wash-in and peak enhancement, are important to aid the accurate characterization of FLLs.

A Prototype System With Custom-Designed RX ICs for Contrast-Enhanced Ultrasound Imaging

This work presents a prototype system based on a multichannel receiving (RX) integrated circuit (IC) for contrast-enhanced ultrasound (CEUS) imaging. The RX IC is implemented in a 40-nm low-voltage CMOS technology and is designed to interface to a capacitive micromachined ultrasonic transducer array. To enable a direct connection of the RX electronics to the transducer, an analog multiplexer with on-chip protection circuitry is developed. Stress tests confirm the reliability of this arrangement when combined with a high-voltage pulser. The RX IC is equipped with a highly programmable bandpass filter to capture harmonic signals from ultrasound contrast agents (UCAs) while suppressing fundamental components. In order to examine the impact of analog front-end (AFE) bandpass filtering, in vitro acoustic experiments are performed with UCAs. A spatial resolution analysis suggests that the AFE bandpass filtering combined with a pulse inversion (PI) technique can improve the lateral resolution by 38% or 9% compared to the original full-bandwidth approach or a stand-alone PI approach, respectively, while the impact on axial resolution is negligible. A phantom study shows that compared to digital bandpass filtering, the AFE bandpass filtering enables better use of the dynamic range of the RX electronics, resulting in better generalized contrast-to-noise ratio from 0.44/0.53 to 0.57/0.68 without or with PI.

Acoustic characterization of tissue-mimicking materials for ultrasound perfusion imaging research

Materials with well-characterized acoustic properties are of great interest for the development of tissue-mimicking phantoms with designed (micro)vasculature networks. These represent a useful means for controlled in-vitro experiments to validate perfusion imaging methods such as Doppler and contrast-enhanced ultrasound (CEUS) imaging. In this work, acoustic properties of seven tissue-mimicking phantom materials at different concentrations of their compounds and five phantom case materials are characterized and compared at room temperature. The goal of this research is to determine the most suitable phantom and case material for ultrasound perfusion imaging experiments. The measurements show a wide range in speed of sound varying from 1057 to 1616 m/s, acoustic impedance varying from 1.09 to 1.71 × 10⁶ kg/m²s, and attenuation coefficients varying from 0.1 to 22.18 dB/cm at frequencies varying from 1 MHz to 6 MHz for different phantom materials. The nonlinearity parameter B/A varies from 6.1 to 12.3 for most phantom materials. This work also reports the speed of sound, acoustic impedance and attenuation coefficient for case materials. According to our results, polyacrylamide (PAA) and polymethylpentene (TPX) are the optimal materials for phantoms and their cases, respectively. To demonstrate the performance of the optimal materials, we performed power Doppler ultrasound imaging of a perfusable phantom, and CEUS imaging of that phantom and a perfusion system. The obtained results can assist researchers in the selection of the most suited materials for in-vitro studies with ultrasound imaging.

Blind Source Separation for Clutter and Noise Suppression in Ultrasound Imaging: Review for Different Applications

Blind source separation (BSS) refers to a number of signal processing techniques that decompose a signal into several "source" signals. In recent years, BSS is increasingly employed for the suppression of clutter and noise in ultrasonic imaging. In particular, its ability to separate sources based on measures of independence rather than their temporal or spatial frequency content makes BSS a powerful filtering tool for data in which the desired and undesired signals overlap in the spectral domain. The purpose of this work was to review the existing BSS methods and their potential in ultrasound imaging. Furthermore, we tested and compared the effectiveness of these techniques in the field of contrast-ultrasound super-resolution, contrast quantification, and speckle tracking. For all applications, this was done in silico, in vitro, and in vivo. We found that the critical step in BSS filtering is the identification of components containing the desired signal and highlighted the value of a priori domain knowledge to define effective criteria for signal component selection.

Artificial intelligence in multiparametric prostate cancer imaging with focus on deep-learning methods

Prostate cancer represents today the most typical example of a pathology whose diagnosis requires multiparametric imaging, a strategy where multiple imaging techniques are combined to reach an acceptable diagnostic performance. However, the reviewing, weighing and coupling of multiple images not only places additional burden on the radiologist, it also complicates the reviewing process. Prostate cancer imaging has therefore been an important target for the development of computer-aided diagnostic (CAD) tools. In this survey, we discuss the advances in CAD for prostate cancer over the last decades with special attention to the deep-learning techniques that have been designed in the last few years. Moreover, we elaborate and compare the methods employed to deliver the CAD output to the operator for further medical decision making.

Contrast-Enhanced Ultrasound Quantification: From Kinetic Modeling to Machine Learning

Ultrasound contrast agents (UCAs) have opened up immense diagnostic possibilities by combined use of indicator dilution principles and dynamic contrast-enhanced ultrasound (DCE-US) imaging. UCAs are microbubbles encapsulated in a biocompatible shell. With a rheology comparable to that of red blood cells, UCAs provide an intravascular indicator for functional imaging of the (micro)vasculature by quantitative DCE-US. Several models of the UCA intravascular kinetics have been proposed to provide functional quantitative maps, aiding diagnosis of different pathological conditions. This article is a comprehensive review of the available methods for quantitative DCE-US imaging based on temporal, spatial and spatiotemporal analysis of the UCA kinetics. The recent introduction of novel UCAs that are targeted to specific vascular receptors has advanced DCE-US to a molecular imaging modality. In parallel, new kinetic models of increased complexity have been developed. The extraction of multiple quantitative maps, reflecting complementary variables of the underlying physiological processes, requires an integrative approach to their interpretation. A probabilistic framework based on emerging machine-learning methods represents nowadays the ultimate approach, improving the diagnostic accuracy of DCE-US imaging by optimal combination of the extracted complementary information. The current value and future perspective of all these advances are critically discussed.

Contrast-enhanced ultrasound with dispersion analysis for the localization of prostate cancer: correlation with radical prostatectomy specimens

PURPOSE

To determine the value of two-dimensional (2D) contrast-enhanced ultrasound (CEUS) imaging and the additional value of contrast ultrasound dispersion imaging (CUDI) for the localization of clinically significant prostate cancer (csPCa).

METHODS

In this multicentre study, subjects scheduled for a radical prostatectomy underwent 2D CEUS imaging preoperatively. CUDI maps were generated from the CEUS recordings. Both CEUS recordings and CUDI maps were scored on the likelihood of presenting csPCa (any Gleason ≥ 4 + 3 and Gleason 3 + 4 larger than 0.5 mL) by five observers and compared to radical prostatectomy histopathology. An automated three-dimensional (3D) fusion protocol was used to match imaging with histopathology. Receiver operator curve (ROC) analysis was performed per observer and imaging modality.

RESULTS

133 of 216 (62%) patients were included in the final analysis. Average area under the ROC for all five readers for CEUS, CUDI and the combination was 0.78, 0.79 and 0.78, respectively. This yields a sensitivity and specificity of 81 and 64% for CEUS, 83 and 56% for CUDI and 83 and 55% for the combination. Interobserver agreement for CEUS, CUDI and the combination showed kappa values of 0.20, 0.18 and 0.18 respectively.

CONCLUSION

The sensitivity and specificity of 2D CEUS and CUDI for csPCa localization are moderate. Despite compressing CEUS in one image, CUDI showed a similar performance to 2D CEUS. With a sensitivity of 83% at cutoff point 3, it could become a useful imaging procedure, especially with 4D acquisition, improved quantification and combination with other US imaging techniques such as elastography.

Liver contrast-enhanced sonography: computer-assisted differentiation between focal nodular hyperplasia and inflammatory hepatocellular adenoma by reference to microbubble transport patterns

OBJECTIVE

A new computer tool is proposed to distinguish between focal nodular hyperplasia (FNH) and an inflammatory hepatocellular adenoma (I-HCA) using contrast-enhanced ultrasound (CEUS). The new method was compared with the usual qualitative analysis.

METHODS

The proposed tool embeds an "optical flow" algorithm, designed to mimic the human visual perception of object transport in image series, to quantitatively analyse apparent microbubble transport parameters visible on CEUS. Qualitative (visual) and quantitative (computer-assisted) CEUS data were compared in a cohort of adult patients with either FNH or I-HCA based on pathological and radiological results. For quantitative analysis, several computer-assisted classification models were tested and subjected to cross-validation. The accuracies, area under the receiver-operating characteristic curve (AUROC), sensitivity and specificity, positive predictive values (PPVs), negative predictive values (NPVs), false predictive rate (FPRs) and false negative rate (FNRs) were recorded.

RESULTS

Forty-six patients with FNH (n = 29) or I-HCA (n = 17) with 47 tumours (one patient with 2 I-HCA) were analysed. The qualitative diagnostic parameters were accuracy = 93.6%, AUROC = 0.94, sensitivity = 94.4%, specificity = 93.1%, PPV = 89.5%, NPV = 96.4%, FPR = 6.9% and FNR = 5.6%. The quantitative diagnostic parameters were accuracy = 95.9%, AUROC = 0.97, sensitivity = 93.4%, specificity = 97.6%, PPV = 95.3%, NPV = 96.7%, FPR = 2.4% and FNR = 6.6%.

CONCLUSIONS

Microbubble transport patterns evident on CEUS are valuable diagnostic indicators. Machine-learning algorithms analysing such data facilitate the diagnosis of FNH and I-HCA tumours.

KEY POINTS

• Distinguishing between focal nodular hyperplasia and an inflammatory hepatocellular adenoma using dynamic contrast-enhanced ultrasound is sometimes difficult. • Microbubble transport patterns evident on contrast-enhanced sonography are valuable diagnostic indicators. • Machine-learning algorithms analysing microbubble transport patterns facilitate the diagnosis of FNH and I-HCA.

A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection

The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learning framework to detect prostate cancer in the sequential CEUS images. The proposed method uniformly extracts features from both the spatial and the temporal dimensions by performing three-dimensional convolution operations, which captures the dynamic information of the perfusion process encoded in multiple adjacent frames for prostate cancer detection. The deep learning models were trained and validated against expert delineations over the CEUS images recorded using two types of contrast agents, i.e., the anti-PSMA based agent targeted to prostate cancer cells and the non-targeted blank agent. Experiments showed that the deep learning method achieved over 91 percent specificity and 90 percent average accuracy over the targeted CEUS images for prostate cancer detection, which was superior ( ) than previously reported approaches and implementations.

Automated multiparametric localization of prostate cancer based on B-mode, shear-wave elastography, and contrast-enhanced ultrasound radiomics

OBJECTIVES

The aim of this study was to assess the potential of machine learning based on B-mode, shear-wave elastography (SWE), and dynamic contrast-enhanced ultrasound (DCE-US) radiomics for the localization of prostate cancer (PCa) lesions using transrectal ultrasound.

METHODS

This study was approved by the institutional review board and comprised 50 men with biopsy-confirmed PCa that were referred for radical prostatectomy. Prior to surgery, patients received transrectal ultrasound (TRUS), SWE, and DCE-US for three imaging planes. The images were automatically segmented and registered. First, model-based features related to contrast perfusion and dispersion were extracted from the DCE-US videos. Subsequently, radiomics were retrieved from all modalities. Machine learning was applied through a random forest classification algorithm, using the co-registered histopathology from the radical prostatectomy specimens as a reference to draw benign and malignant regions of interest. To avoid overfitting, the performance of the multiparametric classifier was assessed through leave-one-patient-out cross-validation.

RESULTS

The multiparametric classifier reached a region-wise area under the receiver operating characteristics curve (ROC-AUC) of 0.75 and 0.90 for PCa and Gleason > 3 + 4 significant PCa, respectively, thereby outperforming the best-performing single parameter (i.e., contrast velocity) yielding ROC-AUCs of 0.69 and 0.76, respectively. Machine learning revealed that combinations between perfusion-, dispersion-, and elasticity-related features were favored.

CONCLUSIONS

In this paper, technical feasibility of multiparametric machine learning to improve upon single US modalities for the localization of PCa has been demonstrated. Extended datasets for training and testing may establish the clinical value of automatic multiparametric US classification in the early diagnosis of PCa.

KEY POINTS

• Combination of B-mode ultrasound, shear-wave elastography, and contrast ultrasound radiomics through machine learning is technically feasible. • Multiparametric ultrasound demonstrated a higher prostate cancer localization ability than single ultrasound modalities. • Computer-aided multiparametric ultrasound could help clinicians in biopsy targeting.

3-D Multi-parametric Contrast-Enhanced Ultrasound for the Prediction of Prostate Cancer

Trans-rectal ultrasound-guided 12-core systematic biopsy (SBx) is the standard diagnostic pathway for prostate cancer (PCa) because of a lack of sufficiently accurate imaging. Quantification of 3-D dynamic contrast-enhanced ultrasound (US) might open the way for a targeted procedure in which biopsies are directed at lesions suspicious on imaging. This work describes the expansion of contrast US dispersion imaging algorithms to 3-D and compares its performance against malignant and benign disease. Furthermore, we examined the feasibility of a multi-parametric approach to predict SBx-core outcomes using machine learning. An area under the receiver operating characteristic (ROC) curve of 0.76 and 0.81 was obtained for all PCa and significant PCa, respectively, an improvement over previous US methods. We found that prostatitis, in particular, was a source of false-positive readings.

Contrast-enhanced Ultrasound in evaluating of angiogenesis and tumor staging of nasopharyngeal carcinoma in nude mice

OBJECTIVE

To explore the use of Contrast-enhanced Ultrasound (CEUS) in evaluating angiogenesis in a xenograft nasopharyngeal carcinoma (NPC) model in nude mice and the evolution of CEUS parameters according to the growth of NPC.

METHODS

Nude mice were divided into three groups according to experiments conducted at various times from tumor implantation (8 mice/group; group A: 4 weeks from implantation; group B:6 weeks from implantation; group C:8 weeks from implantation). CNE-2 cells were transplanted in 24 nude mice and CEUS evaluations of the tumors were performed at 4, 6 or 8 weeks from implantation. CEUS parametric perfusion images and pathological findings were recorded. R version 3.4.4 software was used to analyze the CEUS parameters and pathological findings.

RESULTS

One-way anova analysis indicated statistically significant differences among the three groups with the parameters of peak intensity (PI) (p<0.001), area wash in (AWI) (p<0.001), area wash out (AWO) (p<0.001) and tumor volumes (p<0.001).Pearson correlation coefficient analysis indicated that microvessel density (MVD) was correlated with tumor volume (r = 0.644, p = 0.001), PI (r = 0.904, p<0.0001), AWI (r = 0.547, p = 0.008) and AWO (r = 0.744, P<0.0001). Tumor volume was correlated with MVD (r = 0.644, p = 0.001), PI (r = 0.625, p = 0.002), AWI (r = 0.528, p = 0.012) and AWO (r = 0.784, p<0.001). The percentage of necrosis in histological sections was correlated with the percentage of CEUS unperfused area (r = 0.446,p = 0.038). Spearman rank correlation coefficient analysis indicated that vascular endothelial growth factor (VEGF) was correlated with PI (r = 0.462, P = 0.032). Welch t test indicated PI, AWI and AWO parameters were significantly lower than that of kidneys (p<0.001, p = 0.009, p = 0.005).

CONCLUSIONS

The CEUS parameters PI, AWI and AWO indirectly reflect the MVD and the tumor volume in our model of subcutaneous transplanted NPC in nude mice, providing precious information on angiogenesis and tumor growth. VEGF may play a role in promoting angiogenesis of NPC.

Longitudinal characterization of local perfusion of the rat placenta using contrast-enhanced ultrasound imaging

The placenta performs many physiological functions critical for development. Insufficient placental perfusion, due to improper vascular remodelling, has been linked to many pregnancy-related diseases. To study longitudinal in vivo placental perfusion, we have implemented a pixel-wise time-intensity curve (TIC) analysis of contrast-enhanced ultrasound (CEUS) images. CEUS images were acquired of pregnant Sprague Dawley rats after bolus injections of gas-filled microbubble contrast agents. Conventionally, perfusion can be quantified using a TIC of contrast enhancement in an averaged region of interest. However, the placenta has a complex structure and flow profile, which is insufficiently described using the conventional technique. In this work, we apply curve fitting in each pixel of the CEUS image series in order to quantify haemodynamic parameters in the placenta and surrounding tissue. The methods quantified an increase in mean placental blood volume and relative blood flow from gestational day (GD) 14 to GD18, while the mean transit time of the microbubbles decreased, demonstrating an overall rise in placental perfusion during gestation. The variance of all three parameters increased during gestation, showing that regional differences in perfusion are observable using the pixel-wise TIC approach. Additionally, the high-resolution parametric images show distinct regions of high blood flow developing during late gestation. The developed methods could be applied to assess placental vascular remodelling during the treatment of the pathologies of pregnancy.

On the Relationship between Dynamic Contrast-Enhanced Ultrasound Parameters and the Underlying Vascular Architecture Extracted from Acoustic Angiography

Dynamic contrast-enhanced ultrasound (DCE-US) has been proposed as a powerful tool for cancer diagnosis by estimation of perfusion and dispersion parameters reflecting angiogenic vascular changes. This work was aimed at identifying which vascular features are reflected by the estimated perfusion and dispersion parameters through comparison with acoustic angiography (AA). AA is a high-resolution technique that allows quantification of vascular morphology. Three-dimensional AA and 2-D DCE-US bolus acquisitions were used to monitor the growth of fibrosarcoma tumors in nine rats. AA-derived vascular properties were analyzed along with DCE-US perfusion and dispersion to investigate the differences between tumor and control and their evolution in time. AA-derived microvascular density and DCE-US perfusion exhibited good agreement, confirmed by their spatial distributions. No vascular feature was correlated with dispersion. Yet, dispersion provided better cancer classification than perfusion. We therefore hypothesize that dispersion characterizes vessels that are smaller than those visible with AA.

Convective-Dispersion Modeling in 3D Contrast-Ultrasound Imaging for the Localization of Prostate Cancer

Despite being the solid tumor with the highest incidence in western men, prostate cancer (PCa) still lacks reliable imaging solutions that can overcome the need for systematic biopsies. Dynamic contrast-enhanced ultrasound imaging (DCE-US) allows us to quantitatively characterize the vascular bed in the prostate, due to its ability to visualize an intravenously administered bolus of contrast agents. Previous research has demonstrated that DCE-US parameters related to the vascular architecture are useful markers for the localization of PCa lesions. In this paper, we propose a novel method to assess the convective dispersion (D) and velocity (v) of the contrast bolus spreading through the prostate from three-dimensional (3D) DCE-US recordings. By assuming that D and v are locally constant, we solve the convective-dispersion equation by minimizing the corresponding regularized least-squares problem. 3D multiparametric maps of D and v were compared with 3D histopathology retrieved from the radical prostatectomy specimens of six patients. With a pixel-wise area under the receiver operating characteristic curve of 0.72 and 0.80, respectively, the method shows diagnostic value for the localization of PCa.

Development of a Fluid Dynamic Model for Quantitative Contrast-Enhanced Ultrasound Imaging

Contrast-enhanced ultrasound (CEUS) is a non-invasive imaging technique extensively used for blood perfusion imaging of various organs. This modality is based on the acoustic detection of gas-filled microbubble contrast agents used as intravascular flow tracers. Recent efforts aim at quantifying parameters related to the enhancement in the vascular compartment using time-intensity curve (TIC), and at using these latter as indicators for several pathological conditions. However, this quantification is mainly hampered by two reasons: first, the quantification intrinsically solely relies on temporal intensity variation, the explicit spatial transport of the contrast agent being left out. Second, the exact relationship between the acquired US-signal and the local microbubble concentration is hardly accessible. This paper introduces the use of a fluid dynamic model for the analysis of dynamic CEUS (DCEUS), in order to circumvent the two above-mentioned limitations. A new kinetic analysis is proposed in order to quantify the velocity amplitude of the bolus arrival. The efficiency of proposed methodology is evaluated both in-vitro, for the quantitative estimation of microbubble flow rates, and in-vivo, for the classification of placental insufficiency (control versus ligature) of pregnant rats from DCEUS. Besides, for the in-vivo experimental setup, we demonstrated that the proposed approach outperforms the performance of existing TIC-based methods.

A Temporal and Spatial Analysis Approach to Automated Segmentation of Microbubble Signals in Contrast-Enhanced Ultrasound Images: Application to Quantification of Active Vascular Density in Human Lower Limbs

Contrast-enhanced ultrasound (CEUS) using microbubble contrast agents has shown great promise in visualising and quantifying active vascular density. Most existing approaches for vascular density quantification using CEUS are calculated based on image intensity and are susceptible to confounding factors and imaging artefact. Poor reproducibility is a key challenge to clinical translation. In this study, a new automated temporal and spatial signal analysis approach is developed for reproducible microbubble segmentation and quantification of contrast enhancement in human lower limbs. The approach is evaluated in vitro on phantoms and in vivo in lower limbs of healthy volunteers before and after physical exercise. In this approach, vascular density is quantified based on the relative areas microbubbles occupy instead of their image intensity. Temporal features of the CEUS image sequences are used to identify pixels that contain microbubble signals. A microbubble track density (MTD) measure, the ratio of the segmented microbubble area to the whole tissue area, is calculated as a surrogate for active capillary density. In vitro results reveal a good correlation (r² = 0.89) between the calculated MTD measure and the known bubble concentration. For in vivo results, a significant increase (129% in average) in the MTD measure is found in lower limbs of healthy volunteers after exercise, with excellent repeatability over a series of days (intra-class correlation coefficient = 0.96). This compares to the existing state-of-the-art approach of destruction and replenishment analysis on the same patients (intra-class correlation coefficient ≤0.78). The proposed new approach shows great potential as an accurate and highly reproducible clinical tool for quantification of active vascular density.

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).

A New Method for Discriminating between Bronchial and Pulmonary Arterial Phases using Contrast-Enhanced Ultrasound

This study aimed to explore the value of a real-time comparative observation method using contrast-enhanced ultrasound (CEUS) for discriminating between bronchial and pulmonary arterial phases in diagnosing lung diseases. Forty-nine patients with 50 pulmonary lesions (45 peripheral lesions and five central lesions with obstructive atelectasis, including 36 malignant tumors, five tuberculomas, four inflammatory pseudotumors and five pneumonia lesions) detected via computed tomography and visible on ultrasonography were enrolled in this study. The arterial phases were determined by comparing contrast agent arrival time (AT) in the peripheral lung lesion with that in adjacent lung tissue, referred to as a real-time comparative observation method. Detection rates of this observation method were 100% (50/50) for pulmonary arterial phase and 88% (44/50) for bronchial arterial phase. Using the instrument's built-in graphing and analysis software, a time-intensity curve was constructed based on a chosen region of interest within the lesion where enhancement was the most obvious. Commonly used perfusion indicators in CEUS, such as AT, time-to-peak and peak intensity, were obtained from the time-intensity curve. Percutaneous puncture biopsies were performed under ultrasound guidance, and specimens of all 50 lesions were examined pathologically. AT was significantly shorter in patients with pneumonia than in those with malignant tumors or chronic inflammation (p < 0.05), whereas no difference was seen between those with malignant tumors and those with chronic inflammation. No significant differences in time-to-peak or peak intensity were seen among those with various lung diseases (p > 0.05). This is the first description of a real-time comparative observation method using CEUS for determining the arterial phases in the lungs. This method is accurate, simple to perform and provides a direct display. It is expected to become a practical and feasible tool for diagnosing lung diseases.

Mathematical Models of Contrast Transport Kinetics for Cancer Diagnostic Imaging: A Review

Angiogenesis plays a fundamental role in cancer growth and the formation of metastasis. Novel cancer therapies aimed at inhibiting angiogenic processes and/or disrupting angiogenic tumor vasculature are currently being developed and clinically tested. The need for earlier and improved cancer diagnosis, and for early evaluation and monitoring of therapeutic response to angiogenic treatment, have led to the development of several imaging methods for in vivo noninvasive assessment of angiogenesis. The combination of dynamic contrast-enhanced imaging with mathematical modeling of the contrast agent kinetics enables quantitative assessment of the structural and functional changes in the microvasculature that are associated with tumor angiogenesis. In this paper, we review quantitative imaging of angiogenesis with dynamic contrast-enhanced magnetic resonance imaging, computed tomography, and ultrasound.

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.

Cumulative phase delay imaging for contrast-enhanced ultrasound tomography

Standard dynamic-contrast enhanced ultrasound (DCE-US) imaging detects and estimates ultrasound-contrast-agent (UCA) concentration based on the amplitude of the nonlinear (harmonic) components generated during ultrasound (US) propagation through UCAs. However, harmonic components generation is not specific to UCAs, as it also occurs for US propagating through tissue. Moreover, nonlinear artifacts affect standard DCE-US imaging, causing contrast to tissue ratio reduction, and resulting in possible misclassification of tissue and misinterpretation of UCA concentration. Furthermore, no contrast-specific modality exists for DCE-US tomography; in particular speed-of-sound changes due to UCAs are well within those caused by different tissue types. Recently, a new marker for UCAs has been introduced. A cumulative phase delay (CPD) between the second harmonic and fundamental component is in fact observable for US propagating through UCAs, and is absent in tissue. In this paper, tomographic US images based on CPD are for the first time presented and compared to speed-of-sound US tomography. Results show the applicability of this marker for contrast specific US imaging, with cumulative phase delay imaging (CPDI) showing superior capabilities in detecting and localizing UCA, as compared to speed-of-sound US tomography. Cavities (filled with UCA) which were down to 1 mm in diameter were clearly detectable. Moreover, CPDI is free of the above mentioned nonlinear artifacts. These results open important possibilities to DCE-US tomography, with potential applications to breast imaging for cancer localization.

Multiparametric ultrasound in the detection of prostate cancer: a systematic review

PURPOSE

To investigate the advances and clinical results of the different ultrasound modalities and the progress in combining them into multiparametric UltraSound (mpUS).

METHODS

A systematic literature search on mpUS and the different ultrasound modalities included: greyscale ultrasound, computerized transrectal ultrasound, Doppler and power Doppler techniques, dynamic contrast-enhanced ultrasound and (shear wave) elastography.

RESULTS

Limited research available on combining ultrasound modalities has presented improvement in diagnostic performance. The data of two studies suggest that even adding a lower performing ultrasound modality to a better performing modality using crude methods can already improve the sensitivity by 13-51 %. The different modalities detect different tumours. No study has tried to combine ultrasound modalities employing a system similar to the PIRADS system used for mpMRI or more advanced classifying algorithms.

CONCLUSION

Available evidence confirms that combining different ultrasound modalities significantly improves diagnostic performance.

Contrast dispersion imaging for cancer localization

Cancer growth is associated with angiogenic processes in many types of cancer. Several imaging strategies have therefore been developed that target angiogenesis as a marker for cancer localization. To this end, intravascular and extravascular tissue perfusion is typically assessed by dynamic contrast enhanced (DCE) ultrasound (US) and MRI. All the proposed strategies, however, overlook important changes in the microvascular architecture that result from angiogenic processes. To overcome these limitations, we have recently introduced a new imaging strategy that analyzes the intravascular dispersion kinetics of contrast agents spreading through the microvasculature. Contrast dispersion is mainly determined by microvascular multi-path trajectories, reflecting the underlying microvascular architecture. This paper reviews the results obtained for prostate cancer localization by US and MRI dispersion imaging, also presenting the latest new developments and future perspectives.

4-D spatiotemporal analysis of ultrasound contrast agent dispersion for prostate cancer localization: a feasibility study

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 characteristics of 4-D DCE-US images. The technical feasibility of 4-D DCE-US in 3-D CUDI was assessed and confirmed. Additionally, in a preliminary validation in two patients, dispersion maps by 3-D CUDI were quantitatively compared with those by 2-D CUDI and with 12-core systematic biopsies with promising results.

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.

Cumulative phase delay between second harmonic and fundamental components--a marker for ultrasound contrast agents

Several imaging techniques aimed at detecting ultrasound contrast agents (UCAs) echo signals, while suppressing signals coming from the surrounding tissue, have been developed. These techniques are especially relevant for blood flow, perfusion, or contrast dispersion quantification. However, despite several approaches being presented, improving the understanding of the ultrasound/UCAs interaction may support further development of imaging techniques. In this paper, the physical phenomena behind the formation of harmonic components in tissue and UCAs, respectively, are addressed as a possible way to recognize the origin of the echo signals. Simulations based on a modified Rayleigh, Plesset, Noltingk, Neppiras, and Poritsky equation and transmission and backscattering measurements of ultrasound propagating through UCAs performed with a single element transducer and a submergible hydrophone, are presented. Both numerical and in vitro results show the occurrence of a cumulative time delay between the second harmonic and fundamental component which increases with UCA concentration and propagation path length through UCAs, and that was clearly observable at frequencies ( f0 = 2.5 MHz) and pressure regimes (mechanical index = 0.1) of interest for imaging. Most importantly, this delay is not observed in the absence of UCAs. In conclusion, the reported phenomenon represents a marker for UCAs with potential application for imaging.

Dynamic contrast-enhanced ultrasound of slaughterhouse porcine livers in machine perfusion

The aim of this study was to enable investigations into novel imaging and surgical techniques by developing a readily accessible, versatile liver machine perfusion system. Slaughterhouse pig livers were used, and dynamic contrast-enhanced ultrasound was introduced to optimize the procurement process and provide real-time perfusion monitoring. The system comprised a single pump, oxygenator, bubble trap and two flowmeters for pressure-controlled perfusion of the vessels using an off-the-shelf perfusate at room temperature. Successful livers exhibited homogeneous perfusion in both the portal vein and hepatic artery with dynamic contrast-enhanced ultrasound, which correlated with stable oxygen uptake, bile production and hepatic resistance and normal histology at the end of 3 h of perfusion. Dynamic contrast-enhanced ultrasound revealed perfusion abnormalities invisible to the naked eye, thereby providing context to the otherwise systemic biochemical/hemodynamic measurements and focal biopsy findings. The model developed here is a simple, cost-effective approach for stable ex vivo whole-organ machine perfusion.

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.

A multiplicative model for improving microvascular flow estimation in dynamic contrast-enhanced ultrasound (DCE-US): theory and experimental validation

Perfusion parameter estimation from dynamic contrast-enhanced ultrasound (DCE-US) data relies on fitting parametric models of flow to curves describing linear echo power as a function of time. The least squares criterion is generally used to fit these models to data. This criterion is optimal in the sense of maximum likelihood under the assumption of an additive white Gaussian noise. In the current work, it is demonstrated that this assumption is not held for DCEUS. A better-adapted maximum likelihood criterion based on a multiplicative model is proposed. It is tested on simulated bolus perfusion data and on 11 sequences acquired in vivo during bolus perfusion of contrast agent in the cortex of healthy murine kidney, an area where the perfusion is expected to be approximately homogeneous. Results on simulated data show a significant improvement (p < 0.05) of the precision and the accuracy for the estimations of perfusion parameters time to peak (TTP), wash-in rate (WiR), and mean transit time (MTT). On the 11 in vivo sequences, the new method leads to a significant reduction (p < 0.05) in the variation of parametric maps for 9 sequences for TTP and 10 sequences for WiR and MTT. The mean percent decreases of the coefficient of variation are 40%, 25%, and 59% for TTP, WiR, and MTT, respectively. This method should contribute to a more robust and accurate estimation of perfusion parameters and an improved resolution of parametric imaging.

Acoustic characterization and pharmacokinetic analyses of new nanobubble ultrasound contrast agents

In contrast to the clinically used microbubble ultrasound contrast agents, nanoscale bubbles (or nanobubbles) may potentially extravasate into tumors that exhibit more permeable vasculature, facilitating targeted molecular imaging and drug delivery. Our group recently presented a simple strategy using the non-ionic surfactant Pluronic as a size control excipient to produce nanobubbles with a mean diameter of 200 nm that exhibited stability and echogenicity on par with microbubbles. The objective of this study was to carry out an in-depth characterization of nanobubble properties as compared with Definity microbubbles, both in vitro and in vivo. Through use of a tissue-mimicking phantom, in vitro experiments measured the echogenicity of the contrast agent solutions and the contrast agent dissolution rate over time. Nanobubbles were found to be more echogenic than Definity microbubbles at three different harmonic frequencies (8, 6.2 and 3.5 MHz). Definity microbubbles also dissolved 1.67 times faster than nanobubbles. Pharmacokinetic studies were then performed in vivo in a subcutaneous human colorectal adenocarcinoma (LS174T) in mice. The peak enhancement and decay rates of contrast agents after bolus injection in the liver, kidney and tumor were analyzed. No significant differences were observed in peak enhancement between the nanobubble and Definity groups in the three tested regions (tumor, liver and kidney). However, the decay rates of nanobubbles in tumor and kidney were significantly slower than those of Definity in the first 200-s fast initial phase. There were no significant differences in the decay rates in the liver in the initial phase or in three regions of interest in the terminal phase. Our results suggest that the stability and acoustic properties of the new nanobubble contrast agents are superior to those of the clinically used Definity microbubbles. The slower washout of nanobubbles in tumors suggests potential entrapment of the bubbles within the tumor parenchyma.

Prostate cancer localization by novel magnetic resonance dispersion imaging

Diagnosis and focal treatment of prostate cancer, the most prevalent form of cancer in men, is hampered by the limits of current clinical imaging. Angiogenesis imaging is a promising option for detection and localization of prostate cancer. It can be imaged by dynamic contrast-enhanced (DCE) MRI, assessing microvascular permeability as an indicator for angiogenesis. However, information on microvascular architecture changes associated with angiogenesis is not available. This paper presents a new model enabling the combined assessment of microvascular permeability and architecture. After the intravenous injection of a gadolinium-chelate bolus, time-concentration curves (TCCs) are measured by DCE-MRI at each voxel. According to the convective dispersion equation, the microvascular architecture is reflected in the dispersion coefficient. A solution of this equation is therefore proposed to represent the intravascular blood plasma compartment in the Tofts model. Fitting the resulting model to TCCs measured at each voxel leads to the simultaneous generation of a dispersion and a permeability map. Measurement of an arterial input function is no longer required. Preliminary validation was performed by spatial comparison with the histological results in seven patients referred for radical prostatectomy. Cancer localization by the obtained dispersion maps provided an area under the receiver operating characteristic curve equal to 0.91. None of the standard DCE-MRI parametric maps could outperform this result, motivating towards an extended validation of the method, also aimed at investigating other forms of cancer with pronounced angiogenic development.

Contrast-ultrasound dispersion imaging for prostate cancer localization by improved spatiotemporal similarity analysis

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 ultrasound-contrast-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 = 0.88) compared with those obtained by reported perfusion and dispersion analyses, providing a valuable contribution to prostate cancer localization.