Ruud J. G. van Sloun

Ultrasound-contrast-agent dispersion and velocity imaging for prostate cancer localization

&NA; Prostate cancer (PCa) is the second‐leading cause of cancer death in men; however, reliable tools for detection and localization are still lacking. Dynamic Contrast Enhanced UltraSound (DCE‐US) is a diagnostic tool that is suitable for analysis of vascularization, by imaging an intravenously injected microbubble bolus. The localization of angiogenic vascularization associated with the development of tumors is of particular interest. Recently, methods for the analysis of the bolus convective dispersion process have shown promise to localize angiogenesis. However, independent estimation of dispersion was not possible due to the ambiguity between convection and dispersion. Therefore, in this study we propose a new method that considers the vascular network as a dynamic linear system, whose impulse response can be locally identified. To this end, model‐based parameter estimation is employed, that permits extraction of the apparent dispersion coefficient (D), velocity (v), and Péclet number (Pe) of the system. Clinical evaluation using data recorded from 25 patients shows that the proposed method can be applied effectively to DCE‐US, and is able to locally characterize the hemodynamics, yielding promising results (receiver‐operating‐characteristic curve area of 0.84) for prostate cancer localization. HighlightsA novel contrast‐ultrasound method is proposed for prostate cancer localization.Independent estimation of dispersion and velocity of ultrasound contrast agents.Tumors show increased velocity estimates, and reduced dispersion estimates.Clinical evaluation on 25 patients yields promising results (ROC area 0.84). Graphical abstract Figure. No caption available.

Entropy of Ultrasound-Contrast-Agent Velocity Fields for Angiogenesis Imaging in Prostate Cancer

Prostate cancer care can benefit from accurate and cost-efficient imaging modalities that are able to reveal prognostic indicators for cancer. Angiogenesis is known to play a central role in the growth of tumors towards a metastatic or a lethal phenotype. With the aim of localizing angiogenic activity in a non-invasive manner, Dynamic Contrast Enhanced Ultrasound (DCE-US) has been widely used. Usually, the passage of ultrasound contrast agents thought the organ of interest is analyzed for the assessment of tissue perfusion. However, the heterogeneous nature of blood flow in angiogenic vasculature hampers the diagnostic effectiveness of perfusion parameters. In this regard, quantification of the heterogeneity of flow may provide a relevant additional feature for localizing angiogenesis. Statistics based on flow magnitude as well as its orientation can be exploited for this purpose. In this paper, we estimate the microbubble velocity fields from a standard bolus injection and provide a first statistical characterization by performing a spatial entropy analysis. By testing the method on 24 patients with biopsy-proven prostate cancer, we show that the proposed method can be applied effectively to clinically acquired DCE-US data. The method permits estimation of the in-plane flow vector fields and their local intricacy, and yields promising results (receiver-operating-characteristic curve area of 0.85) for the detection of prostate cancer.

Imaging of the dispersion coefficient of Ultrasound contrast agents by Wiener system identification for prostate cancer localization

Prostate cancer (PCa) is the most prevalent form of cancer in Western men; however, reliable tools for PCa detection and localization are lacking. Dynamic Contrast Enhanced Ultrasound (DCE-US) is a diagnostic tool that allows analysis of vascularization, by imaging an intravenously injected microbubble bolus. The localization of angiogenic vascularization associated with the development of tumors is of particular interest. Recently, methods aiming at estimating contrast dispersion to localize angiogenesis have shown promise. However, independent estimation of dispersion was not possible due to the ambiguity between dispersive and convective processes. Therefore, in this study we propose a new method that considers the vascular network as a dynamic linear system, whose impulse response can be locally identified by solving the Wiener-Hopf equations. To facilitate characterization, model-based parameter estimation is employed, permitting the determination of the apparent dispersion coefficient (D), velocity (v), and Péclet number (Pe) of the system. A preliminary clinical evaluation using data recorded from 10 patients shows that the proposed method can be applied effectively to DCE-US, and is able to locally characterize the hemodynamics in the prostate.

Contrast-enhanced ultrasound tractography for 3D vascular imaging of the prostate

Diffusion tensor tractography (DTT) enables visualization of fiber trajectories in soft tissue using magnetic resonance imaging. DTT exploits the anisotropic nature of water diffusion in fibrous structures to identify diffusion pathways by generating streamlines based on the principal diffusion vector. Anomalies in these pathways can be linked to neural deficits. In a different field, contrast-enhanced ultrasound is used to assess anomalies in blood flow with the aim of locating cancer-induced angiogenesis. Like water diffusion, blood flow and transport of contrast agents also shows a principal direction; however, this is now determined by the local vasculature. Here we show how the tractographic techniques developed for magnetic resonance imaging DTT can be translated to contrast-enhanced ultrasound, by first estimating contrast flow velocity fields from contrast-enhanced ultrasound acquisitions, and then applying tractography. We performed 4D in-vivo contrast-enhanced ultrasound of three human prostates, proving the feasibility of the proposed approach with clinically acquired datasets. By comparing the results to histopathology after prostate resection, we observed qualitative agreement between the contrast flow tracts and typical markers of cancer angiogenic microvasculature: higher densities and tortuous geometries in tumor areas. The method can be used in-vivo using a standard contrast-enhanced ultrasound protocol, opening up new possibilities in the area of vascular characterization for cancer diagnostics.

A fixed-distance plane wave method for estimating the ultrasound coefficient of nonlinearity

A practical method is proposed to assess the ultrasound coefficient of nonlinearity of a medium by measuring the fundamental and 2nd harmonic in the near field of a plane piston source for varying source pressure. The method uses the Fubini solution to extract the slope of the linear dependency of the ratio harmonic/fundamental on the fundamental pressure measured at the same location. It eliminates the need for a motion stage, required by methods observing harmonic growth with source distance. It also excludes the need to measure the pressure at the source, since, in the current experiment, conducted in distilled water, it neglects depletion of the fundamental due to attenuation and energy transfer to higher harmonics. The variability of the estimated beta was evaluated with 9 measurements for which the setup was mounted anew. This was performed for 4 different distances from the source. The estimated beta slightly decreased with increasing distance from the source, possibly due to focusing effects. The average beta estimated over all measurements was 3.48+−0.43, showing good agreement with previously reported values. The reproducibility and accuracy of the proposed method is relevant for its adoption aimed at beta measurements in tissue samples for clinical diagnostic research.A practical method is proposed to assess the ultrasound coefficient of nonlinearity of a medium by measuring the fundamental and 2nd harmonic in the near field of a plane piston source for varying source pressure. The method uses the Fubini solution to extract the slope of the linear dependency of the ratio harmonic/fundamental on the fundamental pressure measured at the same location. It eliminates the need for a motion stage, required by methods observing harmonic growth with source distance. It also excludes the need to measure the pressure at the source, since, in the current experiment, conducted in distilled water, it neglects depletion of the fundamental due to attenuation and energy transfer to higher harmonics. The variability of the estimated beta was evaluated with 9 measurements for which the setup was mounted anew. This was performed for 4 different distances from the source. The estimated beta slightly decreased with increasing distance from the source, possibly due to focusing effects. The ...

Deep learning for automated detection of B-lines in lung ultrasonography

The application of ultrasound imaging to the diagnosis of lung diseases is gaining attention. Of particular interest are several imaging-artifacts, e.g., A and B line artifacts. A-lines are hyperechoic horizontal lines, which are substantially visualized across the entire image and parallel to pleural-line. They represent the normal pattern of the lung if pneumothorax is excluded. Differently, B-line artifacts correlate with pathology and are defined as hyperechoic vertical artifacts, which originate from a point along the pleura-line and lie perpendicular to the latter. Their presence has been linked to an increase in extravascular lung water, interstitial lung diseases, non-cardiogenic lung edema, interstitial pneumonia and lung contusion. In this work, we describe a method aimed to support the clinicians by automatically identifying the frames of an ultrasound video where B-lines are found. To this end, we employ modern deep learning strategies and train a fully convolutional neural network to perform this task on b-mode images of dedicated ultrasound phantoms (Demi et al., Sci. Rep. 2017). We moreover calculate neural attention maps that visualize which components in the image triggered the network, thereby offering simultaneous localization. Future work includes characterization of the detected B-lines to enable adequate phenotyping of various lung pathologies.

Which properties of the vascular architecture are reflected by dynamic contrast-enhanced ultrasound imaging of dispersion and wash-in rate? A comparison with acoustic angiography

Tumor growth requires formation of new angiogenic vessels, which differ in their morphology from those of healthy tissue. These vascular abnormalities result in altered blood flow dynamics, which can be assessed by dynamic contrast-enhanced ultrasound (DCE-US). Two distinct approaches are typically employed in DCE-US following an intravenous injection of ultrasound contrast agent: assessment of perfusion or dispersion parameters from the measured time intensity curves [1]. In this paper, we compare maps of dispersion and perfusion with those of acoustic angiography (AA), a 3D high-resolution technique capable of capturing the vascular morphology [2]. We aim at determining those properties of the vascular architecture that are reflected by perfusion and dispersion parameters, as well as their evolution over time as tumor grows. To this end, a longitudinal study has been performed with tumor models in 3 rats.

Shear wave viscoelasticity imaging using local system identification

Tissue elasticity is an important parameter which relates to the pathological state of soft tissue. Fibrotic lesions or malignant tumors are known to be notoriously stiff compared to benign tissue. Shear wave elastography can provide a fully quantitative measure of lesion stiffness by estimating the speed at which acoustically induced shear waves propagate through the material. This speed is in turn related to the Youngs modulus. In soft tissue, elasticity is generally accompanied by viscosity, leading to dispersion of the shear wave. For the detection and characterization of malignant lesions, viscosity has in fact diagnostic value. Here, we describe a new method that enables imaging not only elasticity but also viscosity from shear wave elastography by local model-based system identification. We show that the proposed method can be applied effectively to standard shear wave acquisitions, and is able to generate high-resolution parametric maps of the viscoelastic material properties in an in-vitro setting.

Shear-wave imaging of viscoelasticity using local impulse response identification

Imaging technologies that allow assessment of the elastic properties of soft tissue provide clinicians with an important asset for several diagnostic applications. A quantitative measure of stiffness can be obtained by shear-wave (SW) elasticity imaging, a method that uses acoustic radiation force to produce laterally-propagating shear waves that can be tracked to obtain the velocity, which in turn is related to the shear modulus. If one considers the medium to be purely elastic, its local shear modulus can be estimated by determining the local SW velocity. However, this assumption does not hold for many tissue types, whenever the shear viscosity plays an important role. In fact, there is increasing evidence that viscosity itself could be an important marker for malignancy [1]. In this work, we therefore aim at providing a joint local estimate of tissue elasticity and viscosity based on SW elastography.

Determination of a potential quantitative measure of the state of the lung using lung ultrasound spectroscopy

B-lines are ultrasound-imaging artifacts, which correlate with several lung-pathologies. However, their understanding and characterization is still largely incomplete. To further study B-lines, lung-phantoms were developed by trapping a layer of microbubbles in tissue-mimicking gel. To simulate the alveolar size reduction typical of various pathologies, 170 and 80 µm bubbles were used for phantom-type 1 and 2, respectively. A normal alveolar diameter is approximately 280 µm. A LA332 linear-array connected to the ULA-OP platform was used for imaging. Standard ultrasound (US) imaging at 4.5 MHz was performed. Subsequently, a multi-frequency approach was used where images were sequentially generated using orthogonal sub-bands centered at different frequencies (3, 4, 5, and 6 MHz). Results show that B-lines appear predominantly with phantom-type 2. Moreover, the multi-frequency approach revealed that the B-lines originate from a specific portion of the US spectrum. These results can give rise to significant clinical applications since, if further confirmed by extensive in-vivo studies, the native frequency of B-lines could provide a quantitative-measure of the state of the lung.