Indicator dilution models for the quantification of microvascular blood flow with bolus administration of ultrasound contrast agents

Bolus tracking from pulsed x-ray projections: A feasibility study using a five-dimensional cardiac CT contrast dynamics model

BACKGROUND

Cardiac computed tomography (CT) exams are some of the most complex CT exams due to the need to carefully time the scan when the heart chambers are near the peak contrast concentration. With current "bolus tracking" and "timing bolus" techniques, after contrast medium is injected, a target vessel or chamber is scanned periodically, and images are reconstructed to monitor the opacification. Both techniques have opportunities for improvement, such as reducing the contrast medium volume, the exam time, the number of manual steps, and improving the robustness of correctly timing the peak opacification.

PURPOSE

The objective of our study is to (1) develop a novel autonomous cardiac CT clinical workflow to track contrast bolus dynamics directly from pulsed x-ray projections, (2) develop a new five-dimensional virtual cardiac CT data generation tool with programmable cardiac profiles and bolus dynamics, and (3) demonstrate the feasibility of projection-domain prospective bolus tracking using a neural network trained and tested with the virtual data to find the contrast peak.

METHODS

In our proposed workflow, pulsed mode projections (PMPs) are acquired with a wide-open collimator under sparse view conditions (monitoring phase). Each time a new PMP is acquired, the neural network is used to estimate the contrast enhancement inside the target chambers. To train such a network, we introduce a new approach to generate clinically realistic virtual scan data based on a five-dimensional cardiac model, by synthesizing user-defined contrast bolus dynamics and patient electrocardiogram profiles. In this study, we investigated a scenario with one single PMP per rotation. To find the optimal PMP view angle, 20 angles were explored. For each angle, 300 virtual exams were generated from 115 human subject datasets and divided into training, validation, and testing groups. Twenty neural networks were trained and evaluated in total to find the optimal network. Finally, a simple bolus peak time estimation algorithm was developed and evaluated by comparing to the ground truth bolus peak time.

RESULTS

To evaluate the accuracy of a bolus time-intensity curve estimated by the network, the cosine similarity between the estimation and the ground truth was computed. The cosine similarity was larger than 0.97 for all projection angles. A view angle corresponding to the x-ray tube at 30 degrees from vertical (left-anterior of subject) showed the lowest errors. The amplitude of the estimated bolus curves (in Hounsfield Units) was not always correctly predicted, but the shape was accurately predicted. This resulted in an RMSE of 1.23 s for the left chambers and 0.78 s for the right chambers in the contrast peak time estimation.

CONCLUSION

In this study, we proposed an innovative real-time way to predict the contrast bolus peak in cardiac CT as well as an innovative approach to train a neural network using virtual but clinically realistic data. Our trained network successfully estimated the shape of the time-intensity curve for the target chambers, which led to accurate bolus peak time estimation. This technique could be used for autonomous diagnostic cardiac CT to trigger a diagnostic scan for optimal contrast enhancement.

Super-resolution ultrasound imaging of ischaemia flow: An in silico study

Super-resolution ultrasound (SRU) is a new ultrasound imaging mode that promises to facilitate the detection of microvascular disease by providing new vascular bio-markers that are directly linked to microvascular pathophysiology, thereby augmenting current knowledge and potentially enabling new treatment. Such a capability can be developed through thorough understanding as articulated by means of mathematical models. In this study, a 2D numerical flow model is adopted for generating flow adaptation in response to ischaemia, in order to determine the ability of SRU to register the resulting flow perturbations. The flow model results demonstrate that variations in flow behaviour in response to locally induced ischaemia can be significant throughout the entire vascular bed. Measured velocities have variations that are dependent on the location of ischaemia, with median values ranging between 2-7 mms-1. Moreover, the distinction between healthy and ischaemic networks are recorded accurately in the SRU results showing excellent agreement between SRU maps and the model. Up to 7-fold spatial resolution improvement to conventional contrast ultrasound was achieved in microbubble localisation while the detection precision and recall was consistently above 98%. The microbubble tracking precision was of a similar accuracy, whereas the recall was reduced (77%) under varying ischaemic impacted flow. Further, regions with velocities up to 30 mms-1 are in excellent agreement with SRU maps, while at regions that include a proportion of higher velocities, the median velocity values are within 1.28%-3.32% of the ground-truth. In conclusion, SRU is a highly promising methodology for the direct measurement of microvascular flow dynamics and may provide a valuable tool for the understanding and subsequent modelling of behaviour in the vascular bed.

Comparison of contrast-enhanced ultrasound imaging (CEUS) and super-resolution ultrasound (SRU) for the quantification of ischaemia flow redistribution: a theoretical study

The study of microcirculation can reveal important information related to pathology. Focusing on alterations that are represented by an obstruction of blood flow in microcirculatory regions may provide an insight into vascular biomarkers. The current in silico study assesses the capability of contrast enhanced ultrasound (CEUS) and super-resolution ultrasound imaging (SRU) flow-quantification to study occlusive actions in a microvascular bed, particularly the ability to characterise known and model induced flow behaviours. The aim is to investigate theoretical limits with the use of CEUS and SRU in order to propose realistic biomarker targets relevant for clinical diagnosis. Results from CEUS flow parameters display limitations congruent with prior investigations. Conventional resolution limits lead to signals dominated by large vessels, making discrimination of microvasculature specific signals difficult. Additionally, some occlusions lead to weakened parametric correlation against flow rate in the remainder of the network. Loss of correlation is dependent on the degree to which flow is redistributed, with comparatively minor redistribution correlating in accordance with ground truth measurements for change in mean transit time,dMTT(CEUS,R = 0.85; GT,R = 0.82) and change in peak intensity,dIp(CEUS,R = 0.87; GT,R = 0.96). Major redistributions, however, result in a loss of correlation, demonstrating that the effectiveness of time-intensity curve parameters is influenced by the site of occlusion. Conversely, results from SRU processing provides accurate depiction of the anatomy and dynamics present in the vascular bed, that extends to individual microvessels. Correspondence between model vessel structure displayed in SRU maps with the ground truth was>91%for cases of minor and major flow redistributions. In conclusion, SRU appears to be a highly promising technology in the quantification of subtle flow phenomena due ischaemia induced vascular flow redistribution.

Multi-Parametric Retinal Microvascular Functional Perfusion Imaging Based on Dynamic Fundus Fluorescence Angiography

Retinal microvascular disease has caused serious visual impairment widely in the world, which can be hopefully prevented via early and precision microvascular hemodynamic diagnosis. Due to artifacts from choroidal microvessels and tiny movements, current fundus microvascular imaging techniques including fundus fluorescein angiography (FFA) precisely identify retinal microvascular microstructural damage and abnormal hemodynamic changes difficulty, especially in the early stage. Therefore, this study proposes an FFA-based multi-parametric retinal microvascular functional perfusion imaging (RM-FPI) scheme to assess the microstructural damage and quantify its hemodynamic distribution precisely. Herein, a spatiotemporal filter based on singular value decomposition combined with a lognormal fitting model was used to remove the above artifacts. Dynamic FFAs of patients (n = 7) were collected first. The retinal time fluorescence intensity curves were extracted and the corresponding perfusion parameters were estimated after decomposition filtering and model fitting. Compared with in vivo results without filtering and fitting, the signal-to-clutter ratio of retinal perfusion curves, average contrast, and resolution of RM-FPI were up to 7.32 ± 0.43 dB, 14.34 ± 0.24 dB, and 11.0 ± 2.0 µm, respectively. RM-FPI imaged retinal microvascular distribution and quantified its spatial hemodynamic changes, which further characterized the parabolic distribution of local blood flow within diameters ranging from 9 to 400 µm. Finally, RM-FPI was used to quantify, visualize, and diagnose the retinal hemodynamics of retinal vein occlusion from mild to severe. Therefore, this study provided a scheme for early and precision diagnosis of retinal microvascular disease, which might be beneficial in preventing its development.

A Comprehensive and Repeatable Contrast-Enhanced Ultrasound Quantification Approach for Clinical Evaluations of Tumor Blood Flow

OBJECTIVE

The aim of this study is to define a comprehensive and repeatable contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to quantitatively assess lesional blood flow. Easily repeatable CEUS evaluations are essential for longitudinal treatment monitoring. The quantification method described here aims to provide a structure for future clinical studies.

MATERIALS AND METHODS

This retrospective analysis study included liver CEUS studies in 80 patients, 40 of which contained lesions (primarily hepatocellular carcinoma, n = 28). Each patient was given at least 2 injections of a microbubble contrast agent, and 60-second continuous loops were acquired for each injection to enable evaluation of repeatability. For each bolus injection, 1.2 mL of contrast was delivered, whereas continuous, stationary scanning was performed. Automated respiratory gating and motion compensation algorithms dealt with breathing motion. Similar in size regions of interest were drawn around the lesion and liver parenchyma, and time-intensity curves (TICs) with linearized image data were generated. Four bolus transit parameters, rise time (RT), mean transit time (MTT), peak intensity (PI), and area under the curve (AUC), were extracted either directly from the actual TIC data or from a lognormal distribution curve fitted to the TIC. Interinjection repeatability for each parameter was evaluated with coefficient of variation. A 95% confidence interval was calculated for all fitted lognormal distribution curve coefficient of determination (R2) values, which serves as a data quality metric. One-sample t tests were performed between values obtained from injection pairs and between the fitted lognormal distribution curve and direct extraction from the TIC calculation methods to establish there were no significant differences between injections and measurement precision, respectively.

RESULTS

Average interinjection coefficient of variation with both the fitted curve and direct calculation of RT and MTT was less than 21%, whereas PI and AUC were less than 40% for lesion and parenchyma regions of interest. The 95% confidence interval for the R2 value of all fitted lognormal curves was [0.95, 0.96]. The 1-sample t test for interinjection value difference showed no significant differences, indicating there was no relationship between the order of the repeated bolus injections and the resulting parameters. The 1-sample t test between the values from the fitted lognormal distribution curve and the direct extraction from the TIC calculation found no statistically significant differences (α = 0.05) for all perfusion-related parameters except lesion and parenchyma PI and lesion MTT.

CONCLUSIONS

The scanning protocol and analysis method outlined and validated in this study provide easily repeatable quantitative evaluations of lesional blood flow with bolus transit parameters in CEUS data that were not available before. With vital features such as probe stabilization ideally performed with an articulated arm and an automated respiratory gating algorithm, we were able to achieve interinjection repeatability of blood flow parameters that are comparable or surpass levels currently established for clinical 2D CEUS scans. Similar values and interinjection repeatability were achieved between calculations from a fitted curve or directly from the data. This demonstrated not only the strength of the protocol to generate TICs with minimal noise, but also suggests that curve fitting might be avoided for a more standardized approach. Utilizing the imaging protocol and analysis method defined in this study, we aim for this methodology to potentially assist clinicians to assess true perfusion changes for treatment monitoring with CEUS in longitudinal studies.

Analysis of early response to chemotherapy for non-Hodgkin's lymphoma by quantitative contrast-enhanced ultrasound: A prospective case-control crossectional study

OBJECTIVE

To investigate the value of quantitative contrast-enhanced ultrasonography (CEUS) in assessing and predicting early therapy response of non-Hodgkin's lymphoma (NHL).

METHODS

Fifty-six cases of NHL were studied using CEUS before and after three cycles of R-CHOP / CHOP. Quantitative parameters such as arrival time (ATM), time to peak (TTP), △T = TTP-ATM, area under the gamma curve (Area), curve gradient (Grad), wash-out time (WT), base intensity (BI), peak intensity (PI) and ΔI = PI-BI were compared between the lymphoma and normal lymph nodes before and at mid-treatment, respectively. Changes in quantitative CEUS parameters were also compared between complete response (CR) and incomplete response(non-CR) groups. Besides, the correlation analysis was performed between pretreatment PI and changes in quantitative parameters.

RESULTS

After three cycles of R-CHOP/CHOP, S/L (P < 0.001), PI (P = 0.002), ΔI (P < 0.001), Grad (P < 0.001), and Area (P < 0.001) of NHL were significantly decreased. The CR group and non-CR group only differed in ATM before treatment. In contrast, there was no statistical difference in any of the parameters between the two groups at mid-treatment. Finally, a significant correlation was observed between pre-treatment PI and PI△% (r = 0.736, P < 0.001).

CONCLUSIONS

CEUS is promising for the assessment of response of NHL to R-CHOP/CHOP. Intra-lesion perfusion changes take precedence over morphological changes suggesting treatment efficacy. Pre-treatment ATM values may help to suggest efficacy outcomes and pre-treatment PI values may be a valid predictor of lymphoma perfusion response.

Motion correction of 3D dynamic contrast-enhanced ultrasound imaging without anatomical B-Mode images: Pilot evaluation in eight patients

BACKGROUND

Dynamic contrast-enhanced ultrasound (DCE-US) is highly susceptible to motion artifacts arising from patient movement, respiration, and operator handling and experience. Motion artifacts can be especially problematic in the context of perfusion quantification. In conventional 2D DCE-US, motion correction (MC) algorithms take advantage of accompanying side-by-side anatomical B-Mode images that contain time-stable features. However, current commercial models of 3D DCE-US do not provide side-by-side B-Mode images, which makes MC challenging.

PURPOSE

This work introduces a novel MC algorithm for 3D DCE-US and assesses its efficacy when handling clinical data sets.

METHODS

In brief, the algorithm uses a pyramidal approach whereby short temporal windows consisting of three consecutive frames are created to perform local registrations, which are then registered to a master reference derived from a weighted average of all frames. We applied the algorithm to imaging studies from eight patients with metastatic lesions in the liver and assessed improvements in original versus motion corrected 3D DCE-US cine using: (i) frame-to-frame volumetric overlap of segmented lesions, (ii) normalized correlation coefficient (NCC) between frames (similarity analysis), and (iii) sum of squared errors (SSE), root-mean-squared error (RMSE), and r-squared (R2) quality-of-fit from fitted time-intensity curves (TIC) extracted from a segmented lesion.

RESULTS

We noted improvements in frame-to-frame lesion overlap across all patients, from 68% ± 13% without correction to 83% ± 3% with MC (p = 0.023). Frame-to-frame similarity as assessed by NCC also improved on two different sets of time points from 0.694 ± 0.057 (original cine) to 0.862 ± 0.049 (corresponding MC cine) and 0.723 ± 0.066 to 0.886 ± 0.036 (p ≤ 0.001 for both). TIC analysis displayed a significant decrease in RMSE (p = 0.018) and a significant increase in R2 goodness-of-fit (p = 0.029) for the patient cohort.

CONCLUSIONS

Overall, results suggest decreases in 3D DCE-US motion after applying the proposed algorithm.

Quantification of Hepatocellular Carcinoma Vascular Dynamics With Contrast-Enhanced Ultrasound for LI-RADS Implementation

OBJECTIVE

The aim of this study is to describe a comprehensive contrast-enhanced ultrasound (CEUS) imaging protocol and analysis method to implement CEUS LI-RADS (Liver Imaging Reporting and Data System) in a quantifiable manner. The methods that are validated with a prospective single-center study aim to simplify CEUS LI-RADS evaluation, remove observer bias, and potentially improve the sensitivity of CEUS LI-RADS.

MATERIALS AND METHODS

This prospective single-center study enrolled patients with hepatocellular carcinoma (April 2021-June 2022; N = 31; mean age ± SD, 67 ± 6 years; 24 men/7 women). For each patient, at least 2 CEUS loops spanning over 5 minutes were collected for different lesion scan planes using an articulated arm to hold the transducer. Automatic respiratory gating and motion compensation algorithms removed errors due to breathing motion. The long axis of the lesion was measured in the contrast and fundamental images to capture nodule size. Parametric processing of time-intensity curve analysis on linearized data provided quantifiable information of the wash-in and washout dynamics via rise time ( RT ) and degree of washout ( DW ) parameters extracted from the time-intensity curve, respectively. A Welch t test was performed between lesion and parenchyma RT for each lesion to confirm statistically significant differences. P values for bootstrapped 95% confidence intervals of the relative degree of washout ( rDW ), ratio of DW between the lesion and surrounding parenchyma, were computed to quantify lesion washout. Coefficient of variation (COV) of RT , DW , and rDW was calculated for each patient between injections for both the lesion and surrounding parenchyma to gauge reproducibility of these metrics. Spearman rank correlation tests were performed among size, RT , DW , and rDW values to evaluate statistical dependence between the variables.

RESULTS

The mean ± SD lesion diameter was 23 ± 8 mm. The RT for all lesions, capturing arterial phase hyperenhancement, was shorter than that of surrounding liver parenchyma ( P < 0.05). All lesions also demonstrated significant ( P < 0.05) but variable levels of washout at both 2-minute and 5-minute time points, quantified in rDW . The COV of RT for the lesion and surrounding parenchyma were both 11%, and the COV of DW and rDW at 2 and 5 minutes ranged from 22% to 31%. Statistically significant relationships between lesion and parenchyma RT and between lesion RT and lesion DW at the 2- and 5-minute time points were found ( P < 0.05).

CONCLUSIONS

The imaging protocol and analysis method presented provide robust, quantitative metrics that describe the dynamic vascular patterns of LI-RADS 5 lesions classified as hepatocellular carcinomas. The RT of the bolus transit quantifies the arterial phase hyperenhancement, and the DW and rDW parameters quantify the washout from linearized CEUS intensity data. This unique methodology is able to implement the CEUS-LIRADS scheme in a quantifiable manner for the first time and remove its existing issues of currently being qualitative and suffering from subjective evaluations.

Quantitative parameters of contrast-enhanced ultrasound effectively promote the prediction of cervical lymph node metastasis in papillary thyroid carcinoma

Papillary thyroid carcinoma (PTC), the most common endocrine tumor, often spreads to cervical lymph nodes metastasis (CLNM). Preoperative diagnosis of CLNM is important when selecting surgical strategies. Therefore, we aimed to explore the effectiveness of quantitative parameters of contrast-enhanced ultrasound (CEUS) in predicting CLNM in PTC. We retrospectively analyzed 193 patients with PTC undergoing conventional ultrasound (CUS) and CEUS. The CUS features and quantitative parameters of CEUS were evaluated according to PTC size ≤ 10 or > 10 mm, using pathology as the gold standard. For the PTC ≤ 10 mm, microcalcification and multifocality were significantly different between the CLNM (+) and CLNM (-) groups (both P < 0.05). For the PTC > 10 mm, statistical significance was noted between the two groups with respect to the margin, capsule contact, and multifocality (all P < 0.05). For PTC ≤ 10 mm, there was no significant difference between the CLNM (+) and CLNM (-) groups in all quantitative parameters of CEUS (all P > 0.05). However, for PTC > 10 mm, the peak intensity (PI), mean transit time, and slope were significantly associated with CLNM (all P < 0.05). Multivariate analysis showed that PI > 5.8 dB was an independent risk factor for predicting CLNM in patients with PTC > 10 mm (P < 0.05). The area under the curve of PI combined with CUS (0.831) was significantly higher than that of CUS (0.707) or PI (0.703) alone in the receiver operator characteristic curve analysis (P < 0.05). In conclusion, PI has significance in predicting CLNM for PTC > 10 mm; however, it is not helpful for PTC ≤ 10 mm.

Quantitative Pharmacokinetics Reveal Impact of Lipid Composition on Microbubble and Nanoprogeny Shell Fate

Microbubble-enabled focused ultrasound (MB-FUS) has revolutionized nano and molecular drug delivery capabilities. Yet, the absence of longitudinal, systematic, quantitative studies of microbubble shell pharmacokinetics hinders progress within the MB-FUS field. Microbubble radiolabeling challenges contribute to this void. This barrier is overcome by developing a one-pot, purification-free copper chelation protocol able to stably radiolabel diverse porphyrin-lipid-containing Definity® analogues (pDefs) with >95% efficiency while maintaining microbubble physicochemical properties. Five tri-modal (ultrasound-, positron emission tomography (PET)-, and fluorescent-active) [64 Cu]Cu-pDefs are created with varying lipid acyl chain length and charge, representing the most prevalently studied microbubble compositions. In vitro, C16 chain length microbubbles yield 2-3x smaller nanoprogeny than C18 microbubbles post FUS. In vivo, [64 Cu]Cu-pDefs are tracked in healthy and 4T1 tumor-bearing mice ± FUS over 48 h qualitatively through fluorescence imaging (to characterize particle disruption) and quantitatively through PET and γ-counting. These studies reveal the impact of microbubble composition and FUS on microbubble dissolution rates, shell circulation, off-target tissue retention (predominantly the liver and spleen), and FUS enhancement of tumor delivery. These findings yield pharmacokinetic microbubble structure-activity relationships that disrupt conventional knowledge, the implications of which on MB-FUS platform design, safety, and nanomedicine delivery are discussed.

Dispersion-corrected extracorporeal arterial input functions in PET studies of mice: a comparison to intracorporeal microprobe measurements

BACKGROUND

Kinetic modelling of dynamic PET typically requires knowledge of the arterial radiotracer concentration (arterial input function, AIF). Its accurate determination is very difficult in mice. AIF measurements in an extracorporeal shunt can be performed; however, this introduces catheter dispersion. We propose a framework for extracorporeal dispersion correction and validated it by comparison to invasively determined intracorporeal AIFs using implanted microprobes.

RESULTS

The response of an extracorporeal radiation detector to radioactivity boxcar functions, characterised by a convolution-based dispersion model, gave best fits using double-gamma variate and single-gamma variate kernels compared to mono-exponential kernels for the investigated range of flow rates. Parametric deconvolution with the optimal kernels was performed on 9 mice that were injected with a bolus of 39 ± 25 MBq [18F]F-PSMA-1007 after application of an extracorporeal circulation for three different flow rates in order to correct for dispersion. Comparison with synchronous implantation of microprobes for invasive aortic AIF recordings showed favourable correspondence, with no significant difference in terms of area-under-curve after 300 s and 5000 s. One-tissue and two-tissue compartment model simulations were performed to investigate differences in kinetic parameters between intra- and extracorporeally measured AIFs. Results of the modelling study revealed kinetic parameters close to the chosen simulated values in all compartment models.

CONCLUSION

The high correspondence of simultaneously intra- and extracorporeally determined AIFs and resulting model parameters establishes a feasible framework for extracorporeal dispersion correction. This should allow more precise and accurate kinetic modelling in small animal experiments.

Domain knowledge-guided adversarial adaptive fusion of hybrid breast ultrasound data

Contrast-enhanced ultrasound (CEUS), which provides more detailed microvascular information about the tumor, is always taken by radiologists in clinic diagnosis along with B-mode ultrasound (B-mode US). However, automatically analyzing breast CEUS is challenging due to the difference between the CEUS video and the natural video, e.g., sports or action videos, where the CEUS video has no positional displacements. Additionally, most existing methods rarely use the Time Intensity Curve (TIC) information of CEUS and non-imaging clinical (NIC) data. To address these issues, we propose a novel breast cancer diagnosis framework that learns the complementarity and correlation across hybrid modal data, including CEUS, B-mode US, and NIC data, by an adversarial adaptive fusion method. Furthermore, to fully exploit the CEUS information, the proposed method, inspired by the clinical processing of radiologists, first extracts the TIC parameters of CEUS. Then, we select a clip from CEUS using a frame screening strategy and finally get spatio-temporal features from these clips through a critical frame attention network. To our knowledge, this is the first AI system to use TIC parameters, NIC data, and ultrasound imaging in diagnoses. We have validated our method on a dataset collected from 554 patients. The experimental results demonstrate the excellent performance of the proposed method. The result shows that our method can achieve an accuracy of 87.73%, which is higher than that of uni-modal approaches by nearly 5%.

Prediction of renal recovery following sepsis-associated acute kidney injury requiring renal replacement therapy using contrast-enhanced ultrasonography

BACKGROUND

Microcirculatory dysfunction plays a critical role in sepsis-associated acute kidney injury (S-AKI) development; however, its impact on renal recovery remains uncertain. We investigated the association between cortical microcirculatory function assessed using contrast-enhanced ultrasonography (CEUS) and renal recovery after S-AKI needing renal replacement therapy (RRT).

METHODS

This retrospective study included 23 patients who underwent CEUS among those who underwent acute RRT for S-AKI. In addition, we acquired data from 17 healthy individuals and 18 patients with chronic kidney disease. Renal recovery was defined as sustained independence from RRT for at least 14 days.

RESULTS

Of the CEUS-derived parameters, rise time, time to peak, and fall time were longer in patients with S-AKI than in healthy individuals (p = 0.045, 0.01, and 0.096, respectively). Fourteen patients (60.9%) with S-AKI receiving RRT experienced renal recovery; and these patients had higher values of peak enhancement, wash-in area under the curve (AUC), wash-in perfusion index, and washout AUC than those without recovery (p = 0.03, 0.01, 0.03, and 0.046, respectively). We evaluated the receiver operating characteristic curve and found that the peak enhancement, wash-in AUC, wash-in perfusion index, and wash-out AUC of CEUS derivatives estimated the probability of renal recovery after S-AKI requiring RRT (p = 0.03, 0.01, 0.03, and 0.04, respectively).

CONCLUSION

CEUS-assessed cortical microvascular perfusion may predict renal recovery following S-AKI that requires RRT. Further studies are essential to validate the clinical utility of microcirculatory parameters obtained from CEUS to estimate renal outcomes in various etiologies and severities of kidney disease.

Contrast-Enhanced Ultrasonography for Acute Kidney Injury: A Systematic Review and Meta-Analysis

OBJECTIVE

The aim of the work described here was to provide an evidence-based evaluation of contrast-enhanced ultrasonography (CEUS) in acute kidney injury (AKI) and assess variations in renal microperfusion with CEUS quantitative parameters in patients at a high risk of developing AKI.

METHODS

A meta-analysis and systematic review were conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the Embase, MEDLINE, Web of Science and the Cochrane Library databases were used to search the relevant articles systematically (2000-2022). Studies using CEUS to assess renal cortical microcirculation in AKI were included.

RESULTS

Six prospective studies (374 patients) were included. The overall quality of included studies was moderate to high. CEUS measures, maximum intensity (standard mean difference [SMD]: -1.37, 95% confidence interval [CI]: -1.64 to -1.09) and wash-in rate (SMD: -0.77, 95% CI: -1.09 to -0.45) were lower in the AKI+ group than in the AKI- group, and mean transit time (SMD: 0.76, 95% CI: 0.11-1.40) and time to peak (SMD: 1.63, 95% CI: 0.99-2.27) were higher in the AKI+ group. Moreover, maximum intensity and wash-in rate values changed before creatinine changed in the AKI+ group.

CONCLUSION

Patients with AKI had reduced microcirculatory perfusion, prolonged perfusion time and a reduced rising slope in the renal cortex, which occurred before serum creatinine changes. And they could be measured using CEUS, indicating that CEUS could help in the diagnosis of AKI.

Classifying Kidney Disease in a Vervet Model Using Spatially Encoded Contrast-Enhanced Ultrasound Perfusion Parameters

Early stages of diabetic kidney disease (DKD) are difficult to diagnose in patients with type 2 diabetes. This work was aimed at identifying contrast-enhanced ultrasound (CEUS) perfusion parameters, a microcirculatory biomarker indicative of early DKD progression. CEUS kidney flash-replenishment data were acquired in control, insulin resistant and diabetic vervet monkeys (N = 16). By use of a mono-exponential model, time-intensity curve parameters related to blood volume (A), velocity (β) and flow rate (perfusion index [PI]) were extracted from 10 concentric kidney layers to study spatial perfusion patterns that could serve as strong indicators of disease. Mean squared error (MSE) was used to assess model performance. Features calculated from the perfusion parameters were inputs for the linear regression models to determine which features could distinguish between cohorts. The mono-exponential model performed well, with average MSEs (±standard deviation) of 0.0254 (±0.0210), 0.0321 (±0.0242) and 0.0287 (±0.0130) for the control, insulin resistant and diabetic cohorts, respectively. Perfusion index features, with blood pressure, were the best classifiers between cohorts (p < 0.05). CEUS has the potential to detect early microvascular changes, providing insight into disease-related structural changes in the kidney. The sensitivity of this technique should be explored further by assessing various stages of DKD.

A Comprehensive Motion Compensation Method for In-Plane and Out-of-Plane Motion in Dynamic Contrast-Enhanced Ultrasound of Focal Liver Lesions

Contrast-enhanced ultrasound (CEUS) acquisitions of focal liver lesions are affected by motion, which has an impact on contrast signal quantification. We therefore developed and tested, in a large patient cohort, a motion compensation algorithm called the Iterative Local Search Algorithm (ILSA), which can correct for both periodic and non-periodic in-plane motion and can reject frames with out-of-plane motion. CEUS cines of 183 focal liver lesions in 155 patients from three hospitals were used to develop and test ILSA. Performance was evaluated through quantitative metrics, including the root mean square error and R2 in fitting time-intensity curves and standard deviation value of B-mode intensities, computed across cine frames), and qualitative evaluation, including B-mode mean intensity projection images and parametric perfusion imaging. The median root mean square error significantly decreased from 0.032 to 0.024 (p < 0.001). Median R2 significantly increased from 0.88 to 0.93 (p < 0.001). The median standard deviation value of B-mode intensities significantly decreased from 6.2 to 5.0 (p < 0.001). B-Mode mean intensity projection images revealed improved spatial resolution. Parametric perfusion imaging also exhibited improved spatial detail and better differentiation between lesion and background liver parenchyma. ILSA can compensate for all types of motion encountered during liver CEUS, potentially improving contrast signal quantification of focal liver lesions.

Normalizing tumor microenvironment with nanomedicine and metronomic therapy to improve immunotherapy

Nanomedicine offered hope for improving the treatment of cancer but the survival benefits of the clinically approved nanomedicines are modest in many cases when compared to conventional chemotherapy. Metronomic therapy, defined as the frequent, low dose administration of chemotherapeutics – is being tested in clinical trials as an alternative to the conventional maximum tolerated dose (MTD) chemotherapy schedule. Although metronomic chemotherapy has not been clinically approved yet, it has shown better survival than MTD in many preclinical studies. When beneficial, metronomic therapy seems to be associated with normalization of the tumor microenvironment including improvements in tumor perfusion, tissue oxygenation and drug delivery as well as activation of the immune system. Recent preclinical studies suggest that nanomedicines can cause similar changes in the tumor microenvironment. Here, by employing a mathematical framework, we show that both approaches can serve as normalization strategies to enhance treatment. Furthermore, employing murine breast and fibrosarcoma tumor models as well as ultrasound shear wave elastography and contrast-enhanced ultrasound, we provide evidence that the approved nanomedicine Doxil can induce normalization in a dose-dependent manner by improving tumor perfusion as a result of tissue softening. Finally, we show that pretreatment with a normalizing dose of Doxil can improve the efficacy of immune checkpoint inhibition.

Perfusion Imaging: An Advection Diffusion Approach

Perfusion imaging is of great clinical importance and is used to assess a wide range of diseases including strokes and brain tumors. Commonly used approaches for the quantitative analysis of perfusion images are based on measuring the effect of a contrast agent moving through blood vessels and into tissue. Contrast-agent free approaches, for example, based on intravoxel incoherent motion and arterial spin labeling, also exist, but are so far not routinely used clinically. Existing contrast-agent-dependent methods typically rely on the estimation of the arterial input function (AIF) to approximately model tissue perfusion. These approaches neglect spatial dependencies. Further, as reliably estimating the AIF is non-trivial, different AIF estimates may lead to different perfusion measures. In this work we therefore propose PIANO, an approach that provides additional insights into the perfusion process. PIANO estimates the velocity and diffusion fields of an advection-diffusion model best explaining the contrast dynamics without using an AIF. PIANO accounts for spatial dependencies and neither requires estimating the AIF nor relies on a particular contrast agent bolus shape. Specifically, we propose a convenient parameterization of the estimation problem, a numerical estimation approach, and extensively evaluate PIANO. Simulation experiments show the robustness and effectiveness of PIANO, along with its ability to distinguish between advection and diffusion. We further apply PIANO on a public brain magnetic resonance (MR) perfusion dataset of acute stroke patients, and demonstrate that PIANO can successfully resolve velocity and diffusion field ambiguities and results in sensitive measures for the assessment of stroke, comparing favorably to conventional measures of perfusion.

Safety of Image-Guided Treatment of the Liver with Ultrasound and Microbubbles in an in Vivo Porcine Model

Ultrasound and microbubbles are useful for both diagnostic imaging and targeted drug delivery, making them ideal conduits for theranostic interventions. Recent reports have indicated the preclinical success of microbubble cavitation for enhancement of chemotherapy in abdominal tumors; however, there have been limited studies and variable efficacy in clinical implementation of this technique. This is likely because in contrast to the high pressures and long cycle lengths seen in successful preclinical work, current clinical implementation of microbubble cavitation for drug delivery generally involves low acoustic pressures and short cycle lengths to fit within clinical guidelines. To translate the preclinical parameter space to clinical adoption, a relevant safety study in a healthy large animal is required. Therefore, the purpose of this work was to evaluate the safety of ultrasound cavitation treatment (USCTx) in a healthy porcine model using a modified Philips EPIQ with S5-1 as the focused source. We performed USCTx on eight healthy pigs and monitored health over the course of 1 wk. We then performed an acute study of USCTx to evaluate immediate tissue damage. Contrast-enhanced ultrasound exams were performed before and after each treatment to investigate perfusion changes within the treated areas, and blood and urine were evaluated for liver damage biomarkers. We illustrate, through quantitative analysis of contrast-enhanced ultrasound data, blood and urine analyses and histology, that this technique and the parameter space considered are safe within the time frame evaluated. With its safety confirmed using a clinical-grade ultrasound scanner and contrast agent, USCTx could be easily translated into clinical trials for improvement of chemotherapy delivery. This represents the first safety study assessing the bio-effects of microbubble cavitation from relevant ultrasound parameters in a large animal model.

Mathematical Models for Blood Flow Quantification in Dialysis Access Using Angiography: A Comparative Study

Blood flow rate in dialysis (vascular) access is the key parameter to examine patency and to evaluate the outcomes of various endovascular interve7ntions. While angiography is extensively used for dialysis access–salvage procedures, to date, there is no image-based blood flow measurement application commercially available in the angiography suite. We aim to calculate the blood flow rate in the dialysis access based on cine-angiographic and fluoroscopic image sequences. In this study, we discuss image-based methods to quantify access blood flow in a flow phantom model. Digital subtraction angiography (DSA) and fluoroscopy were used to acquire images at various sampling rates (DSA—3 and 6 frames/s, fluoroscopy—4 and 10 pulses/s). Flow rates were computed based on two bolus tracking algorithms, peak-to-peak and cross-correlation, and modeled with three curve-fitting functions, gamma variate, lagged normal, and polynomial, to correct errors with transit time measurement. Dye propagation distance and the cross-sectional area were calculated by analyzing the contrast enhancement in the vessel. The calculated flow rates were correlated versus an in-line flow sensor measurement. The cross-correlation algorithm with gamma-variate curve fitting had the best accuracy and least variability in both imaging modes. The absolute percent error (mean ± SEM) of flow quantification in the DSA mode at 6 frames/s was 21.4 ± 1.9%, and in the fluoroscopic mode at 10 pulses/s was 37.4 ± 3.6%. The radiation dose varied linearly with the sampling rate in both imaging modes and was substantially low to invoke any tissue reactions or stochastic effects. The cross-correlation algorithm and gamma-variate curve fitting for DSA acquisition at 6 frames/s had the best correlation with the flow sensor measurements. These findings will be helpful to develop a software-based vascular access flow measurement tool for the angiography suite and to optimize the imaging protocol amenable for computational flow applications.

Vascularized Carotid Atherosclerotic Plaque Models for the Validation of Novel Methods of Quantifying Intraplaque Neovascularization

BACKGROUND

Intraplaque neovascularization (IPN) in advanced lesions of the carotid artery has been linked to plaque progression and risk of rupture. Quantitative measurement of IPN may provide a more powerful tool for the detection of such "vulnerable" plaque than the current visual scoring method. The aim of this study was to develop a phantom platform of a neovascularized atherosclerotic plaque within a carotid artery to assess new methods of quantifying IPN.

METHODS

Ninety-two synthetic plaque models with various IPN architectures representing different ranges of IPN scoring were created and assessed using contrast-enhanced ultrasound. Intraplaque neovascularization volume was calculated from contrast infiltration in B mode. The plaque models were used to develop a testing platform for IPN quantification. A neovascularized enhancement ratio (NER) was calculated using commercially available software. The plaque model NERs were then compared to human plaque NERs (n = 42) to assess score relationship. Parametric mapping of dynamic intensity over time was used to differentiate IPN from calcified plaque regions.

RESULTS

A positive correlation between NER and IPN volume (rho = 0.45; P < .0001) was found in the plaque models. Enhancement of certain plaque model types showed that they resembled human plaques, with visual grade scores of 0 (NER mean difference = 1.05 ± SE 2.45; P = .67), 1 (NER mean difference = 0.22 ± SE 3.26; P = .95), and 2 (NER mean difference = -0.84 ± SE 3.33; P = .80). An optimal cutoff for NER (0.355) identified grade 2 human plaques with a sensitivity of 95% and specificity of 91%.

CONCLUSIONS

We developed a carotid artery model of neovascularized plaque and established a quantitative method for IPN using commercially available technology. We also developed an analysis method to quantify IPN in calcified plaques. This novel tool has the potential to improve clinical identification of vulnerable plaques, providing objective measures of IPN for cardiovascular risk assessment.

Transit Time Measurement in Indicator Dilution Curves: Overcoming the Missing Ground Truth and Quantifying the Error

The vascular function of a vessel can be qualitatively and intraoperatively checked by recording the blood dynamics inside the vessel via fluorescence angiography (FA). Although FA is the state of the art in proving the existence of blood flow during interventions such as bypass surgery, it still lacks a quantitative blood flow measurement that could decrease the recurrence rate and postsurgical mortality. Previous approaches show that the measured flow has a significant deviation compared to the gold standard reference (ultrasonic flow meter). In order to systematically address the possible sources of error, we investigated the error in transit time measurement of an indicator. Obtaining in vivo indicator dilution curves with a known ground truth is complex and often not possible. Further, the error in transit time measurement should be quantified and reduced. To tackle both issues, we first computed many diverse indicator dilution curves using an in silico simulation of the indicator's flow. Second, we post-processed these curves to mimic measured signals. Finally, we fitted mathematical models (parabola, gamma variate, local density random walk, and mono-exponential model) to re-continualize the obtained discrete indicator dilution curves and calculate the time delay of two analytical functions. This re-continualization showed an increase in the temporal accuracy up to a sub-sample accuracy. Thereby, the Local Density Random Walk (LDRW) model performed best using the cross-correlation of the first derivative of both indicator curves with a cutting of the data at 40% of the peak intensity. The error in frames depends on the noise level and is for a signal-to-noise ratio (SNR) of 20 dB and a sampling rate of f_s = 60 Hz at fs-1·0.25(±0.18), so this error is smaller than the distance between two consecutive samples. The accurate determination of the transit time and the quantification of the error allow the calculation of the error propagation onto the flow measurement. Both can assist surgeons as an intraoperative quality check and thereby reduce the recurrence rate and post-surgical mortality.

Contrast-enhanced ultrasound imaging for assessing organ perfusion in rainbow trout (Oncorhynchus mykiss)

Contrast-enhanced ultrasound (CEUS) imaging has great potential as a non-lethal, inexpensive monitoring tool in aquatic toxicology. It is a well-established clinical imaging approach that combines real-time, quantitative assessment of organ blood flow, with morphological data. In humans, it has been extensively used to measure changes in blood flow that can be attributed to cancer, inflammation, and other biological abnormalities. However, it has yet to be explored as a tool for fish physiology or environmental toxicology. In this study, our goal was to determine if CEUS could be used to visualize and measure blood flow in the liver of a rainbow trout. All rainbow trout received two injections of an ultrasound contrast agent, microbubbles. A subset received a third injection after administration of propranolol, a non-specific beta1 & 2-blocker, to determine if changes in blood flow could be detected. Ultrasound contrast time-intensity curves (TIC) were obtained, fit to a lognormal model, and different perfusion parameters were calculated. Contrast enhancement was observed in all rainbow trout livers, with high percentage between repeated measurements, including blood flow (80.6 ± 27.3%), area under the curve (73.2 ± 14%), blood volume (84 ± 14.2%) and peak enhancement (86.7 ± 7.5%). After administration of propranolol, we detected a non-significant (p > 0.05) increase in area under the curve (102.6 ± 44.2%), peak enhancement (77.3 ± 106.4), blood volume (48.2 ± 74.5%), and decrease in hepatic blood flow (-17.3 ± 37.1%). These data suggest that CEUS imaging is suitable to measure organ blood flow in fish, and demonstrates tremendous potential for exploring different organs, fish species, and effects of chemical contaminants in future studies.

Image-Guided Treatment of Primary Liver Cancer in Mice Leads to Vascular Disruption and Increased Drug Penetration

Despite advances in interventional procedures and chemotherapeutic drug development, hepatocellular carcinoma (HCC) is still the fourth leading cause of cancer-related deaths worldwide with a <30% 5-year survival rate. This poor prognosis can be attributed to the fact that HCC most commonly occurs in patients with pre-existing liver conditions, rendering many treatment options too aggressive. Patient survival rates could be improved by a more targeted approach. Ultrasound-induced cavitation can provide a means for overcoming traditional barriers defining drug uptake. The goal of this work was to evaluate preclinical efficacy of image-guided, cavitation-enabled drug delivery with a clinical ultrasound scanner. To this end, ultrasound conditions (unique from those used in imaging) were designed and implemented on a Philips EPIQ and S5-1 phased array probe to produced focused ultrasound for cavitation treatment. Sonovue^® microbubbles which are clinically approved as an ultrasound contrast agent were used for both imaging and cavitation treatment. A genetically engineered mouse model was bred and used as a physiologically relevant preclinical analog to human HCC. It was observed that image-guided and targeted microbubble cavitation resulted in selective disruption of the tumor blood flow and enhanced doxorubicin uptake and penetration. Histology results indicate that no gross morphological damage occurred as a result of this process. The combination of these effects may be exploited to treat HCC and other challenging malignancies and could be implemented with currently available ultrasound scanners and reagents.

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.

Spatial Characterization of Tumor Perfusion Properties from 3D DCE-US Perfusion Maps are Early Predictors of Cancer Treatment Response

There is a need for noninvasive repeatable biomarkers to detect early cancer treatment response and spare non-responders unnecessary morbidities and costs. Here, we introduce three-dimensional (3D) dynamic contrast enhanced ultrasound (DCE-US) perfusion map characterization as inexpensive, bedside and longitudinal indicator of tumor perfusion for prediction of vascular changes and therapy response. More specifically, we developed computational tools to generate perfusion maps in 3D of tumor blood flow, and identified repeatable quantitative features to use in machine-learning models to capture subtle multi-parametric perfusion properties, including heterogeneity. Models were developed and trained in mice data and tested in a separate mouse cohort, as well as early validation clinical data consisting of patients receiving therapy for liver metastases. Models had excellent (ROC-AUC > 0.9) prediction of response in pre-clinical data, as well as proof-of-concept clinical data. Significant correlations with histological assessments of tumor vasculature were noted (Spearman R > 0.70) in pre-clinical data. Our approach can identify responders based on early perfusion changes, using perfusion properties correlated to gold-standard vascular properties.

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.

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.

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.

Differentiation of Vascular Characteristics Using Contrast-Enhanced Ultrasound Imaging

Ultrasound contrast imaging has been used to assess tumour growth and regression by assessing the flow through the macro- and micro-vasculature. Our aim was to differentiate the blood kinetics of vessels such as veins, arteries and microvasculature within the limits of the spatial resolution of contrast-enhanced ultrasound imaging. The highly vascularised ovine ovary was used as a biological model. Perfusion of the ovary with SonoVue was recorded with a Philips iU22 scanner in contrast imaging mode. One ewe was treated with prostaglandin to induce vascular regression. Time-intensity curves (TIC) for different regions of interest were obtained, a lognormal model was fitted and flow parameters calculated. Parametric maps of the whole imaging plane were generated for 2 × 2 pixel regions of interest. Further analysis of TICs from selected locations helped specify parameters associated with differentiation into four categories of vessels (arteries, veins, medium-sized vessels and micro-vessels). Time-dependent parameters were associated with large veins, whereas intensity-dependent parameters were associated with large arteries. Further development may enable automation of the technique as an efficient way of monitoring vessel distributions in a clinical setting using currently available scanners.

Time-intensity-curve Analysis and Tumor Extravasation of Nanobubble Ultrasound Contrast Agents

Our group recently presented a simple strategy using the non-ionic surfactant, Pluronic, as a size control excipient to produce nanobubbles in the 100-nm range, which exhibited stability and echogenicity on par with clinically available microbubbles. The objective of the present study was to evaluate biodistribution and extravasation of the Pluronic-stabilized lipid nanobubbles compared with microbubbles in 2 experimental tumor models in mice. Standard lipid-stabilized perfluoropropane bubbles (Pluronic L10) and lipid-stabilized perfluoropropane nanobubbles were intravenously injected into mice bearing either an orthotopic mouse breast cancer (BC4 T1) or subcutaneous mouse ovarian cancer (OVCAR-3) through the tail vein to perform perfusion dynamic studies. No significant differences between the nanobubble and microbubble groups were observed in the peak enhancement of the 3 tested regions (tumor, liver and kidney). However, the decay rates of nanobubble in the tumor and kidney of BC4 T1-bearing mice, as well as in mice with OVRCAR-3 tumors were significantly slower than those of the microbubble. To quantify extravasation, fluorescently labeled bubbles were intravenously injected into mice bearing the same tumors. Histologic analysis showed that nanobubbles were retained in tumor tissue to a greater extent compared with microbubbles in both tumor models at the 3-h time point. Our results demonstrate unique nanobubble behavior compared with microbubbles and support augmented application of these agents in ultrasound molecular imaging and drug delivery beyond the tumor vasculature.

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.

Radiation treatment monitoring with DCE-US in CWR22 prostate tumor xenografts

BACKGROUND

Longitudinal monitoring of potential radiotherapy treatment effects can be determined by dynamic contrast-enhanced ultrasound (DCE-US).

PURPOSE

To assess functional parameters by means of DCE-US in a murine subcutaneous model of human prostate cancer, and their relationship to dose deposition and time-frame after treatment. A special focus has been placed to evaluate the vascular heterogeneity of the tumor and on the most suitable data analysis approach that reflects this heterogeneity.

MATERIAL AND METHODS

In vivo DCE-US was acquired 24 h and 48 h after radiation treatment with a single dose of 7.5 Gy and 10 Gy, respectively. Tumor vasculature was characterized pixelwise using the Brix pharmacokinetic analysis of the time-intensity curves.

RESULTS

Longitudinal changes were detected ( P < 0.001) at 24 h and 48 h after treatment. At 48 h, the eliminating rate constant of the contrast agent from the plasma, k_el, was correlated ( P ≤ 0.05) positively with microvessel density (MVD; r_τ = 0.7) and negatively with necrosis (r_τ = -0.6) for the treated group. Furthermore, Ak_ep, a parameter related to transcapillary transport properties, was also correlated to MVD (r_τ = 0.6, P ≤ 0.05).

CONCLUSION

DCE-US has been shown to detect vascular changes at a very early stage after radiotherapy, which is a great advantage since DCE-US is non-invasive, available at most hospitals, and is low in cost compared to other techniques used in clinical practice.

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.

A multi-model framework to estimate perfusion parameters using contrast-enhanced ultrasound imaging

PURPOSE

Contrast-enhanced ultrasound imaging has expanded the diagnostic potential of ultrasound by enabling real-time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least-squares approach has traditionally been applied for curve fitting, it can be inadequate for noisy and complex data. Moreover, previous research suggests that certain perfusion models may be more relevant depending on the organ or tissue imaged. We propose a multi-model framework to select the most appropriate perfusion model and curve fitting method for each diagnostic application.

METHODS

Our multi-model approach uses a system identification method, which estimates perfusion parameters from the model with the best fit to a given time-intensity curve. We compared current perfusion quantification methods that use a single perfusion model and curve fitting method and our proposed multi-model framework on bolus 3D dynamic contrast-enhanced ultrasound (DCE-US) in vivo images obtained in mice implanted with a colon cancer, as well as on simulation data. The quality of fit in estimating perfusion parameters was evaluated using the Spearman correlation coefficient, the coefficient of determination (R² ), and the normalized root-mean-square error (NRMSE) to ensure that the multi-model framework finds the best perfusion model and curve fitting algorithm.

RESULTS

Our multi-model framework outperforms conventional single perfusion model approaches with least-squares optimization, providing more robust perfusion parameter estimation. R² and NRMSE are 0.98 and 0.18, respectively, for our proposed method. By comparison, the performance of the traditional approach is much more dependent upon the selection of the appropriate model. The R² and NRMSE are 0.91 and 0.31, respectively.

CONCLUSIONS

The proposed multi-model framework for perfusion modeling outperforms the current approach of single perfusion modeling using least-squares optimization and more robustly estimates perfusion parameters when using empiric data labeled by an expert as the gold standard. Our technique is minimally sensitive to issues affecting the accuracy of perfusion parameter estimation, including rise time, noise, region of interest size, and frame rate. This framework could be of key utility in modeling different perfusion systems in different tissues and organs.

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.

In vitro pharmacokinetic phantom for two-compartment modeling in DCE-MRI

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an established minimally-invasive method for assessment of extravascular leakage, hemodynamics, and tissue viability. However, differences in acquisition protocols, variety of pharmacokinetic models, and uncertainty on physical sources of MR signal hamper the reliability and widespread use of DCE-MRI in clinical practice. Measurements performed in a controlled in vitro setup could be used as a basis for standardization of the acquisition procedure, as well as objective evaluation and comparison of pharmacokinetic models. In this paper, we present a novel flow phantom that mimics a two-compartmental (blood plasma and extravascular extracellular space/EES) vascular bed, enabling systemic validation of acquisition protocols. The phantom consisted of a hemodialysis filter with two compartments, separated by hollow fiber membranes. The aim of this phantom was to vary the extravasation rate by adjusting the flow in the two compartments. Contrast agent transport kinetics within the phantom was interpreted using two-compartmental pharmacokinetic models. Boluses of gadolinium-based contrast-agent were injected in a tube network connected to the hollow fiber phantom; time-intensity curves (TICs) were obtained from image series, acquired using a T1-weighted DCE-MRI sequence. Under the assumption of a linear dilution system, the TICs obtained from the input and output of the system were then analyzed by a system identification approach to estimate the trans-membrane extravasation rates in different flow conditions. To this end, model-based deconvolution was employed to determine (identify) the impulse response of the investigated dilution system. The flow rates in the EES compartment significantly and consistently influenced the estimated extravasation rates, in line with the expected trends based on simulation results. The proposed phantom can therefore be used to model a two-compartmental vascular bed and can be employed to test and optimize DCE-MRI acquisition sequences in order to determine a standardized acquisition procedure leading to consistent quantification results.

Dynamic contrast-enhanced EUS for quantification of tumor perfusion in colonic cancer: a prospective cohort study

BACKGROUND AND AIMS

Dynamic contrast-enhanced EUS (CE-EUS) for quantification of perfusion in colonic tumors has not previously been reported in the literature. The aim of this study was to investigate correlations between perfusion parameters and vessel density assessed by immunohistochemical staining with antibodies toward CD31 and CD105.

METHODS

We conducted a prospective clinical study of 28 patients with left-sided colonic adenocarcinoma who underwent CE-EUS and left-sided hemicolectomy within 2 weeks. CE-EUS recordings were analyzed in 2 regions of interest: the entire tumor and the most enhanced area. Immunohistochemical staining with CD31 and CD105 was performed on tumor tissue sections. The slides were manually scanned for highly vascularized areas, and counting of vessels was performed in hotspots within the tumor and invasive front. New vasculature was assessed by CD105. Associations between CE-EUS and CD31 and CD105 were investigated using Spearman correlation.

RESULTS

We found significant P values for the correlation between CD31 and rise time (rho = .603 [95% confidence interval (95% CI), .238-.816]; P = .001) in tumor tissue and for the correlation between CD31 and rise time (rho = .50 [95% CI, .201-.695]; P = .008) and fall time (rho = .52 [95% CI, .204-.723]; P = .006) corresponding to the invasive front. We found no correlations between perfusion values evaluated by CE-EUS and CD105.

CONCLUSIONS

Our results show a significant correlation for vessel density evaluated by CD31 and perfusion parameters evaluated by CE-EUS. This may be the first step toward using real-time CE-EUS for monitoring antiangiogenic therapies in colonic cancer. (Clinical trial registration number: NCT02324023.).

DCEUS-based focal parametric perfusion imaging of microvessel with single-pixel resolution and high contrast

This study aimed to develop a focal microvascular contrast-enhanced ultrasonic parametric perfusion imaging (PPI) scheme to overcome the tradeoff between the resolution, contrast, and accuracy of focal PPI in the tumor. Its resolution was limited by the low signal-to-clutter ratio (SCR) of time-intensity-curves (TICs) induced by multiple limitations, which deteriorated the accuracy and contrast of focal PPI. The scheme was verified by the in-vivo perfusion experiments. Single-pixel TICs were first extracted to ensure PPI with the highest resolution. The SCR of focal TICs in the tumor was improved using respiratory motion compensation combined with detrended fluctuation analysis. The entire and focal PPIs of six perfusion parameters were then accurately created after filtrating the valid TICs and targeted perfusion parameters. Compared with those of the conventional PPIs, the axial and lateral resolutions of focal PPIs were improved by 30.29% (p < .05) and 32.77% (p < .05), respectively; the average contrast and accuracy evaluated by SCR improved by 7.24 ± 4.90 dB (p < .05) and 5.18 ± 1.28 dB (p < .05), respectively. The edge, morphostructure, inhomogeneous hyper-enhanced distribution, and ring-like perfusion features in intratumoral microvessel were accurately distinguished and highlighted by the focal PPIs. The developed focal PPI can assist clinicians in making confirmed diagnoses and in providing appropriate therapeutic strategies for liver tumor.

The effect of resonance on transient microbubble acoustic response: Experimental observations and numerical simulations

A large number of acoustic signals from single lipid-shelled Definity^® (Lantheus Medical Imaging, N. Billerica, MA) microbubbles have been measured using a calibrated microacoustic system, and a unique transient characteristic of resonance has been identified in the onset of scatter. Comparison of the numerically obtained response of microbubbles with acoustic measurements provides good agreement for a soft shell that is characterized by small area dilatation modulus and strain softening behavior, and identifies time to maximum radial excursion and scatter as a robust marker of resonance during transient response. As the sound amplitude increases a two-population pattern emerges in the time delay vs the fundamental acoustic scatter plots, consisting of an initial part pertaining to microbubbles with less than resonant rest radii, which corresponds to the weaker second harmonic resonance, and the dominant resonant envelope pertaining to microbubbles with resonant and greater than resonant rest radii, which corresponds to the primary and subharmonic resonances. Consequently, a wider resonant spectrum is observed. It is a result of the strain softening nature of soft lipid shells, based on which the microbubble sizes corresponding to the above resonances decrease as the sound amplitude increases. This bares an impact on the selection of an optimal microbubble size pertaining to subharmonic imaging.

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.

Parameters for Contrast-Enhanced Ultrasound (CEUS) of Enlarged Superficial Lymph Nodes for the Evaluation of Therapeutic Response in Lymphoma: A Preliminary Study

BACKGROUND The aims of this preliminary study were to evaluate contrast-enhanced ultrasound (CEUS) imaging and the therapeutic response of enlarged superficial lymph nodes in patients with lymphoma before and after chemotherapy and to determine the most useful CEUS response parameters. MATERIAL AND METHODS Forty-three patients with lymphoma, with 43 enlarged superficial lymph nodes, underwent CEUS and conventional ultrasound (US), before treatment and after the first three cycles of chemotherapy. Clinical responses included overall response (OR) and no response (NR). Imaging parameters by time-intensity curve (TIC) included basic intensity (B), wash-out slope and/or decent slope (K), wash-in slope or rise slope (C), time to peak (TTP), area under the gamma curve (Area), arrive time(ATM), peak intensity (PI), change of peak intensity (I) were compared. And receiver operating characteristic (ROC) curve analysis was operated. RESULTS Quantitative parameters of CEUS before and after the first three cycles of chemotherapy showed a significant difference in the AreaΔ, PID, and IΔ in the OR group compared with NR group (P<0.05). There was a significant difference in the Cpre, Areain, PIin, Iin, AreaΔ, PIΔ, and IΔ in the OR group compared with NR group (P<0.05). The effectiveness of the therapeutic response was predicted by the CEUS parameters of IΔ (P<0.05). And ΔArea has the highest diagnostic performance of ineffectiveness. CONCLUSIONS The findings of this study have shown that quantitative analysis by CEUS may be a useful, and objective, imaging method for the evaluation of the therapeutic response of enlarged superficial lymph nodes in lymphoma before and after chemotherapy.

Assessment of liver fibrosis by ultrasound elastography and contrast-enhanced ultrasound: a randomized prospective animal study

This study aimed to assess liver fibrosis by contrast-enhanced ultrasound (CEUS) and point shear-wave elastography (pSWE) in rabbits and compare the performance of the two techniques. Eighty rabbits were divided into experimental (n=60) and control group (n=20). In the experimental group, liver fibrosis (F1-F4) was induced by subcutaneous injection of carbon tetrachloride. CEUS and pSWE of the liver was performed for the two groups at a 4-week interval for 40 weeks. The portal vein rise time (PV-RT), time to peak (PV-TTP), mean transit time (PV-MTT) and the maximum signal intensity (PV-Imax) were analyzed with time-intensity curves (TICs). Liver stiffness value (LSV) was obtained through pSWE. Histologic examination of liver specimens of the rabbits was performed to evaluate the fibrosis stage. PV-RT, PV-TTP, PV-Imax and LSV were significantly different among five liver fibrosis stages (F0-F4) (P<0.01). PV-Imax and LSV displayed better diagnostic performance than PV-RT, PV-TTP, PV-MTT. For diagnosing≥F1 stage fibrosis, the area under the receiver operating characteristic curve (AUROC) of PV-Imax was 0.870, which was similar to that of LSV 0.874 (P=0.94). For diagnosing ≥F2, ≥F3 and ≥F4 stage fibrosis, the AUROC of PV-Imax and LSV was 0.845 vs. 0.956 (P=0.04), 0.789 vs. 0.954 (P=0.01) and 0.707 vs. 0.933 (P=0.03). Both CEUS and pSWE had the potential to be complementary imaging tools in the evaluation of liver fibrosis. The performance of pSWE may be better than CEUS.

Reproducibility of Contrast-Enhanced Ultrasound in Mice with Controlled Injection

PURPOSE

Sensitivity of contrast-enhanced ultrasound (CEUS) to microvascular flow modifications can be limited by intra-injection variability (injected dose, rate, volume).

PROCEDURES

To evaluate the effect of injection variability on microvascular flow evaluation, CEUS was compared between controlled and manual injections where enhancement was assessed in vitro within a flow phantom, in normal murine kidney (N = 12) and in murine ectopic tumors (N = 10).

RESULTS

For both in vitro and in vivo measurements in the renal cortex, controlled injections significantly improved reproducibility of functional parameter estimation. Their coefficient of variation (CV) in the renal cortex ranged from 4 to 19 % for controlled injection vs. 5 to 43 % for manual injections. For measurements in tumors, controlled injection only decreased the CV significantly for the mean transit time. In tumors, multiple injections of contrast agent with a 15-min delay between each were shown to strongly modify contrast uptake by facilitating penetration of microbubbles.

CONCLUSION

Improved reproducibility of CEUS assessments in murine models should provide more robust quantification of flow parameters and more sensitive evaluation of tumor modifications in therapeutic models.

Quantitative Three-Dimensional Dynamic Contrast-Enhanced Ultrasound Imaging: First-In-Human Pilot Study in Patients with Liver Metastases

Purpose: To perform a clinical assessment of quantitative three-dimensional (3D) dynamic contrast-enhanced ultrasound (DCE-US) feasibility and repeatability in patients with liver metastasis, and to evaluate the extent of quantitative perfusion parameter sampling errors in 2D compared to 3D DCE-US imaging.

Materials and Methods: Twenty consecutive 3D DCE-US scans of liver metastases were performed in 11 patients (45% women; mean age, 54.5 years; range, 48-60 years; 55% men; mean age, 57.6 years; range, 47-68 years). Pairs of repeated disruption-replenishment and bolus DCE-US images were acquired to determine repeatability of parameters. Disruption-replenishment was carried out by infusing 0.9 mL of microbubbles (Definity; Latheus Medical Imaging) diluted in 35.1 mL of saline over 8 min. Bolus consisted of intravenous injection of 0.2 mL microbubbles. Volumes-of-interest (VOI) and regions-or-interest (ROI) were segmented by two different readers in images to extract 3D and 2D perfusion parameters, respectively. Disruption-replenishment parameters were: relative blood volume (rBV), relative blood flow (rBF). Bolus parameters included: time-to-peak (TP), peak enhancement (PE), area-under-the-curve (AUC), and mean-transit-time (MTT).

Results: Clinical feasibility and repeatability of 3D DCE-US using both the destruction-replenishment and bolus technique was demonstrated. The repeatability of 3D measurements between pairs of repeated acquisitions was assessed with the concordance correlation coefficient (CCC), and found to be excellent for all parameters (CCC > 0.80), except for the TP (0.74) and MTT (0.30) parameters. The CCC between readers was found to be excellent (CCC > 0.80) for all parameters except for TP (0.71) and MTT (0.52). There was a large Coefficient of Variation (COV) in intra-tumor measurements for 2D parameters (0.18-0.52). Same-tumor measurements made in 3D were significantly different (P = 0.001) than measurements made in 2D; a percent difference of up to 86% was observed between measurements made in 2D compared to 3D in the same tumor.

Conclusions: 3D DCE-US imaging of liver metastases with a matrix array transducer is feasible and repeatable in the clinic. Results support 3D instead of 2D DCE US imaging to minimize sampling errors due to tumor heterogeneity.

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.