Dynamic Contrast-enhanced Ultrasound Imaging Research Articles

Do as Sonographers Think: Contrast-enhanced Ultrasound for Thyroid Nodules Diagnosis via Microvascular Infiltrative Awareness.

Dynamic contrast-enhanced ultrasound (CEUS) imaging can reflect the microvascular distribution and blood flow perfusion, thereby holding clinical significance in distinguishing between malignant and benign thyroid nodules. Notably, CEUS offers a meticulous visualization of the microvascular distribution surrounding the nodule, leading to an apparent increase in tumor size compared to gray-scale ultrasound (US). In the dual-image obtained, the lesion size enlarged from gray-scale US to CEUS, as the microvascular appeared to be continuously infiltrating the surrounding tissue. Although the infiltrative dilatation of microvasculature remains ambiguous, sonographers believe it may promote the diagnosis of thyroid nodules. We propose a deep learning model designed to emulate the diagnostic reasoning process employed by sonographers. This model integrates the observation of microvascular infiltration on dynamic CEUS, leveraging the additional insights provided by gray-scale US for enhanced diagnostic support. Specifically, temporal projection attention is implemented on time dimension of dynamic CEUS to represent the microvascular perfusion. Additionally, we employ a group of confidence maps with flexible Sigmoid Alpha Functions to aware and describe the infiltrative dilatation process. Moreover, a self-adaptive integration mechanism is introduced to dynamically integrate the assisted gray-scale US and the confidence maps of CEUS for individual patients, ensuring a trustworthy diagnosis of thyroid nodules. In this retrospective study, we collected a thyroid nodule dataset of 282 CEUS videos. The method achieves a superior diagnostic accuracy and sensitivity of 89.52% and 93.75%, respectively. These results suggest that imitating the diagnostic thinking of sonographers, encompassing dynamic microvascular perfusion and infiltrative expansion, proves beneficial for CEUS-based thyroid nodule diagnosis.

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

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. This work introduces a novel MC algorithm for 3D DCE-US and assesses its efficacy when handling clinical data sets. 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. 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. Overall, results suggest decreases in 3D DCE-US motion after applying the proposed algorithm.

Dynamic Perfusion Representation and Aggregation Network for Nodule Segmentation using Contrast-enhanced US.

Dynamic contrast-enhanced ultrasound (CEUS) imaging has been widely applied in lesion detection and characterization, due to its offered real-time observation of microvascular perfusion. Accurate lesion segmentation is of great importance to the quantitative and qualitative perfusion analysis. In this paper, we propose a novel dynamic perfusion representation and aggregation network (DpRAN) for the automatic segmentation of lesions using dynamic CEUS imaging. The core challenge of this work lies in enhancement dynamics modeling of various perfusion areas. Specifically, we divide enhancement features into the two scales: short-range enhancement patterns and long-range evolution tendency. To effectively represent real-time enhancement characteristics and aggregate them in a global view, we introduce the perfusion excitation (PE) gate and cross-attention temporal aggregation (CTA) module, respectively. Different from the common temporal fusion methods, we also introduce an uncertainty estimation strategy to assist the model to locate the critical enhancement point first, in which a relatively distinguished enhancement pattern is displayed. The segmentation performance of our DpRAN method is validated on our collected CEUS datasets of thyroid nodules. We obtain the mean dice coefficient (DSC) and intersection of union (IoU) of 0.794 and 0.676, respectively. Superior performance demonstrates its efficacy to capture distinguished enhancement characteristics for lesion recognition. Code is available https://github.com/wanpeng16/DpRAN.

Prediction of Pathological Grades of Pancreatic Neuroendocrine Tumors Based on Dynamic Contrast-Enhanced Ultrasound Quantitative Analysis.

To investigate whether the dynamic contrast-enhanced ultrasound (DCE-US) analysis and quantitative parameters could be helpful for predicting histopathologic grades of pancreatic neuroendocrine tumors (pNETs). This retrospective study conducted a comprehensive review of the CEUS database between March 2017 and November 2021 in Zhongshan Hospital, Fudan University. Ultrasound examinations were performed by an ACUSON Sequioa unit equipped with a 3.5 MHz 6C-1 convex array transducer, and an ACUSON OXANA2 unit equipped with a 3.5 MHz 5C-1 convex array transducer. SonoVue® (Bracco Inc., Milan, Italy) was used for all CEUS examinations. Time intensity curves (TICs) and quantitative parameters of DCE-US were created by Vuebox® software (Bracco, Italy). Inclusion criteria were: patients with histopathologically proved pNETs, patients who underwent pancreatic B-mode ultrasounds (BMUS) and CEUS scans one week before surgery or biopsy and had DCE-US imaging documented for more than 2 min, patients with solid or predominantly solid lesions and patients with definite diagnosis of histopathological grades of pNETs. Based on their prognosis, patients were categorized into two groups: pNETs G1/G2 group and pNETs G3/pNECs group. A total of 42 patients who underwent surgery (n = 38) or biopsy (n = 4) and had histopathologically confirmed pNETs were included. According to the WHO 2019 criteria, all pNETs were classified into grade 1 (G1, n = 10), grade 2 (G2, n = 21), or grade 3 (G3)/pancreatic neuroendocrine carcinomas (pNECs) (n = 11), based on the Ki-67 proliferation index and the mitotic activity. The majority of the TICs (27/31) of pNETs G1/G2 were above or equal to those of pancreatic parenchyma in the arterial phase, but most (7/11) pNETs G3/pNECs had TICs below those of pancreatic parenchyma from arterial phase to late phase (p < 0.05). Among all the CEUS quantitative parameters of DCE-US, values of relative rise time (rPE), relative mean transit time (rmTT) and relative area under the curve (rAUC) were significantly higher in pNETs G1/G2 group than those in pNETs G3/pNECs group (p < 0.05). Taking an rPE below 1.09 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [58.70% to 99.80%], 67.64% [48.61% to 83.32%] and 85.78% [74.14% to 97.42%], respectively. Taking rAUC below 0.855 as the optimal cut-off value, the sensitivity, specificity and accuracy for prediction of pNETs G3/pNECs from G1/G2 were 90.91% [66.26% to 99.53%], 83.87% [67.37% to 92.91%] and 94.72% [88.30% to 100.00%], respectively. Dynamic contrast-enhanced ultrasound analysis might be helpful for predicting the pathological grades of pNETs. Among all quantitative parameters, rPE, rmTT and rAUC are potentially useful parameters for predicting G3/pNECs with aggressive behavior.

Contrast-enhanced ultrasound for the diagnosis of hepatocellular carcinoma in adults with chronic liver disease.

Hepatocellular carcinoma occurs mostly in people with chronic liver disease. Worldwide, it ranks sixth in terms of incidence of cancer, and fourth in terms of cancer-related deaths. Contrast-enhanced ultrasound (CEUS) is used as an add-on test to confirm the presence of focal liver lesions suspected as hepatocellular carcinoma after prior diagnostic tests such as abdominal ultrasound or measurement of alpha-foetoprotein, or both. According to guidelines, a single contrast-enhanced imaging investigation, with either computed tomography (CT) or magnetic resonance imaging (MRI), may show the typical hepatocellular carcinoma hallmarks in people with cirrhosis, which will be sufficient to diagnose hepatocellular carcinoma. However, a significant number of hepatocellular carcinomas show atypical imaging features, and therefore, are missed at imaging. Dynamic CEUS images are obtained similarly to CT and MRI images. CEUS differentiates between arterial and portal venous phases, in which sonographic hepatocellular carcinoma hallmarks, such as arterial hyperenhancement and subsequent washout appearance, are investigated. The advantages of CEUS over CT and MRI include real-time imaging, use of contrast agents that do not contain iodine and are not nephrotoxic, and quick image acquisition. Despite the advantages, the use of CEUS in the diagnostic algorithm for HCC remains controversial, with disagreement on relevant guidelines. There is no clear evidence of the benefit of surveillance programmes in terms of overall survival as the conflicting results can be a consequence of an inaccurate detection, ineffective treatment, or both. Therefore, assessing the diagnostic accuracy of CEUS may clarify whether the absence of benefit could be related to underdiagnosis. Furthermore, an assessment of the accuracy of CEUS for the diagnosis of hepatocellular carcinoma is needed for either diagnosing hepatocellular carcinoma or ruling it out in people with chronic liver disease who are not included in surveillance programmes. 1. To assess the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) for the diagnosis of hepatocellular carcinoma of any size and at any stage in adults with chronic liver disease, in a surveillance programme or in a clinical setting. 2. To assess the diagnostic accuracy of CEUS for the diagnosis of resectable hepatocellular carcinoma in people with chronic liver disease and identify potential sources of heterogeneity in the results. We used standard, extensive Cochrane search methods. The last date of search was 5 November 2021. We included studies assessing the diagnostic accuracy of CEUS for the diagnosis of hepatocellular carcinoma in adults with chronic liver disease, with cross-sectional designs, using one of the acceptable reference standards, such as pathology of the explanted liver, and histology of resected or biopsied focal liver lesion with at least a six-month follow-up. We used standard Cochrane methods to screen studies, extract data, and assess the risk of bias and applicability concerns, using the QUADAS-2 checklist. We used the bivariate model and provided estimates of summary sensitivity and specificity. We assessed the certainty of the evidence using GRADE. We presented uncertainty-of-the-accuracy estimates using 95% confidence intervals (CIs). We included 23 studies with 6546 participants. Studies were published between 2001 and 2021. We judged all 23 studies at high-risk of bias in at least one domain, and 13/23 studies at high concern for applicability. Most studies used different reference standards to exclude the presence of the target condition. The time interval between the index test and the reference standard was rarely defined. We also had major concerns on their applicability due to the characteristics of the participants. - CEUS for hepatocellular carcinoma of any size and stage: sensitivity 77.8% (95% CI 69.4% to 84.4%) and specificity 93.8% (95% CI 89.1% to 96.6%) (23 studies, 6546 participants; very low-certainty evidence). - CEUS for resectable hepatocellular carcinoma: sensitivity 77.5% (95% CI 62.9% to 87.6%) and specificity 92.7% (95% CI 86.8% to 96.1%) (13 studies, 1257 participants; low-certainty evidence). The observed heterogeneity in the results remains unexplained. The sensitivity analyses, including only studies with clearly prespecified positivity criteria and only studies in which the reference standard results were interpreted with no knowledge of the results about the index test, showed no differences in the results. We found that by using CEUS, as an add-on test following abdominal ultrasound, to diagnose hepatocellular carcinoma of any size and stage, 22% of people with hepatocellular carcinoma would be missed, and 6% of people without hepatocellular carcinoma would unnecessarily undergo further testing or inappropriate treatment. As to resectable hepatocellular carcinoma, we found that 23% of people with resectable hepatocellular carcinoma would incorrectly be unresected, while 8% of people without hepatocellular carcinoma would undergo further inappropriate testing or treatment. The uncertainty resulting from the high risk of bias of the included studies, heterogeneity, and imprecision of the results and concerns on their applicability limit our ability to draw confident conclusions.

Feasibility investigation of logarithmic Nakagami parametric imaging in recovering underestimated perfusion metrics of DCEUS in the uneven acoustic field.

Owing to acoustic-pressure dependence, amplitudes of backscattered-echoes of encapsulated microbubbles (MBs) are unavoidably regulated by an uneven acoustic field, resulting in the misestimation of hemodynamics in conventional amplitude-coding dynamic contrast-enhanced ultrasound (DCEUS) with focused pulse transmission. This study aimed to investigate the feasibility and performance of Nakagami statistical-feature parametric imaging to recover the above misestimation. Logarithmic Nakagami parameter (m)-coding DCEUS scheme was investigated via simulation and in vitro MB phantoms as well as in vivo kidney-perfusion experiments of four rabbits in the uneven acoustic fields with two different focal depths. In vivo tissue artifacts for m estimation were suppressed by pulse-inversion second-harmonic imaging and its robustness was enhanced by multiscale moment-estimation strategy. Time-Nakagami-m curves and the corresponding perfusion metrics of intensity and volume were calculated from the logarithmic m-coding DCEUS images within the prefocal and focal regions. These curves and metrics were further compared with the perfusion curves and metrics estimated from the conventional amplitude-coding images within the same regions. Compared with amplitudes of nonlinear scattering MB echoes, their logarithmic m values were relatively independent of the changes in acoustics pressures. Compared with the fixed-scale moment-estimation, the perfusion intensity estimated from logarithmic m-coding DCEUS scheme using multiscale statistical moment-estimation had smaller differences between the prefocal and focal regions. The differences of perfusion intensity induced by an uneven acoustic field decreased to 3.47% ± 1.58 %. The differences decreased by the logarithmic m-coding DCEUS scheme were further regulated by threshold values of m estimation. The logarithmic m-coding DCEUS scheme could recover the underestimated MB backscattered-echoes and the misestimated perfusion intensity induced by the uneven acoustic field. The scheme had the potential to weaken the limitation of microvasculature identification and hemodynamic characterization marked by MBs within tissues or tumors in the uneven acoustic field.

Hierarchical Temporal Attention Network for Thyroid Nodule Recognition Using Dynamic CEUS Imaging.

Contrast-enhanced ultrasound (CEUS) has emerged as a popular imaging modality in thyroid nodule diagnosis due to its ability to visualize vascular distribution in real time. Recently, a number of learning-based methods are dedicated to mine pathological-related enhancement dynamics and make prediction at one step, ignoring a native diagnostic dependency. In clinics, the differentiation of benign or malignant nodules always precedes the recognition of pathological types. In this paper, we propose a novel hierarchical temporal attention network (HiTAN) for thyroid nodule diagnosis using dynamic CEUS imaging, which unifies dynamic enhancement feature learning and hierarchical nodules classification into a deep framework. Specifically, this method decomposes the diagnosis of nodules into an ordered two-stage classification task, where diagnostic dependency is modeled by Gated Recurrent Units (GRUs). Besides, we design a local-to-global temporal aggregation (LGTA) operator to perform a comprehensive temporal fusion along the hierarchical prediction path. Particularly, local temporal information is defined as typical enhancement patterns identified with the guidance of perfusion representation learned from the differentiation level. Then, we leverage an attention mechanism to embed global enhancement dynamics into each identified salient pattern. In this study, we evaluate the proposed HiTAN method on the collected CEUS dataset of thyroid nodules. Extensive experimental results validate the efficacy of dynamic patterns learning, fusion and hierarchical diagnosis mechanism.

Multiscale and morphological analysis of microvascular patterns depicted in contrast-enhanced ultrasound images.

Purpose: Impaired insulin-induced microvascular recruitment in skeletal muscle contributes to insulin resistance in type 2 diabetic disease. Previously, quantification of microvascular recruitment at the capillary level has been performed with either the full image or manually selected region-of-interests. These subjective approaches are imprecise, time-consuming, and unsuitable for automated processes. Here, an automated multiscale image processing approach was performed by defining a vessel diameter threshold for an objective and reproducible analysis at the microvascular level. Approach: A population of C57BL/6J male mice fed standard chow and studied at age 13 to 16 weeks comprised the lean group and 24- to 31-week-old mice who received a high-fat diet were designated the obese group. A clinical ultrasound scanner (Acuson Sequoia 512) equipped with an 15L8-S linear array transducer was used in a nonlinear imaging mode for sensitive detection of an intravascular microbubble contrast agent. Results: By eliminating large vessels from the dynamic contrast-enhanced ultrasound (DCE-US) images (above in diameter), obesity-related changes in perfusion and morphology parameters were readily detected in the smaller vessels, which are known to have a greater impact on skeletal muscle glucose disposal. The results from the DCE-US images including all of the vessels were compared for three different-sized vessel groups, namely, vessels smaller than 300, 200, and in diameter. Conclusions: Our automated image processing provides objective and reproducible results by focusing on a particular size of vessel, thereby allowing for a selective evaluation of longitudinal changes in microvascular recruitment for a specific-sized vessel group between diseased and healthy microvascular networks.

Perfusion Quantification of Liver Metastases of Colorectal Cancer Treated with Anti-angiogenic-Based Therapy: Assessment of Intra- and Inter-observer Reproducibility of Parameters in Three Regions of Interest Outlining Lesions

This study evaluated the reproducibility of dynamic contrast-enhanced ultrasound (DCEUS) parameters outlining liver metastases of colorectal cancer in 45 patients, before and after anti-angiogenic-based therapy. Tumor enhancement was quantified by drawing three regions of interest (ROIs): (i) outlining the tumor based on portal phase DCEUS images, (ii) in the hypo-enhanced center of the lesion and (iii) outlining the lesion using parametric imaging. Perfusion parameters were extracted from time–intensity curves. Another ROI was drawn in healthy liver parenchyma for normalization. Intra- and inter-observer reproducibility of these parameters was evaluated using intra-class correlation coefficients (ICCs). For the three ROIs, both intra- and inter-observer reproducibility were excellent (ICCs ≥0.9) for 50.8% absolute parameters and were moderate to good (0.7 ≤ ICC < 0.9) for 26.7% of them. In healthy liver parenchyma and for normalized parameters, reproducibility was moderate to excellent for 59.4% of intensity parameters and was low (ICC <0.7) for almost all temporal parameters. This study indicates that DCEUS is a reproducible tool for evaluating perfusion parameters.

Multiscale quantification of tumor microarchitecture for predicting therapy response using dynamic contrast-enhanced ultrasound imaging.

Hepatocellular carcinoma (HCC) is the most common liver cancer with 1 million cases globally. A current clinical challenge is to determine which patients will respond to transarterial chemoembolization (TACE) as effective delivery of the embolic material may be influenced by the tumor vascular supply. The purpose of this study is to develop a novel image processing algorithm for improved quantification of tumor microvascular morphology features using contrast-enhanced ultrasound (CEUS) images and to predict the TACE response based on these biomarkers before treatment. A temporal sequence of CEUS images was corrected from rigid and non-rigid motion artifacts using affine and free form deformation models. Subsequently, a principal component analysis based singular value filter was applied to remove the clutter signal from each frame. A maximum intensity projection was created from high-resolution images. A multiscale vessel enhancement filter was first utilized to enhance the tubular structures as a preprocessing step before segmentation. Morphological image processing methods are used to extract the morphology features, namely, number of vessels (NV) and branching points (NB), vessel-to-tissue ratio (VR), and the mean vessel length (VL), tortuosity (VT), and diameter (VD) from the tumor vascular network. Finally, a support vector machine (SVM) is trained and validated using leave-one-out cross-validation technique. The proposed image analysis strategy was able to predict the patient outcome with 90% accuracy when the SVM was trained with the three features together (NB, NV, VR). Experimental results indicated that morphological features of tumor microvascular networks may be significant predictors for TACE response. Reliable prediction of the TACE therapy response may help provide effective therapy planning.

Ultrasomics for Early Evaluation of the Tumor Response to MicroRNA-122 in a Nude Mouse Hepatocellular Carcinoma Model.

To explore the value of ultrasomics in temporal monitoring of tumor changes in response to gene therapy in hepatocellular carcinoma compared with methods according to the Response Evaluation Criteria in Solid Tumors (RECIST) and modified RECIST (mRECIST). Hepatocellular carcinoma-bearing mice were injected intratumorally with microRNA-122 (miR-122) mimics and an miR-122 negative control in the treatment and control groups, respectively. The injections were performed every 3 days for 5 times (on days 0, 3, 6, 9, and 12). Before each injection and at the experiment ending, 2-dimensional ultrasound imaging was performed for tumor size measurement with RECIST and computing a quantitative imaging analysis with ultrasomics. To analyze the tumor perfusion by mRECIST, perfusion parameters were analyzed offline based on dynamic contrast-enhanced ultrasound image videos using SonoLiver software (TomTec, Unterschleissheim, Germany) on day 13. Tumor miR-122 expression was then analyzed by real-time reverse transcription-polymerase chain reaction experiments. Tumors in mice treated with miR-122 mimics demonstrated a mean ± SD 763- ± 60-fold increase in miR-122 levels compared with tumors in the control group. With RECIST, a significant therapeutic response evaluated by tumor size changes was detected after day 9 (days 9, 12, and 13; P < .001). With mRECIST, no parameters showed significant differences (P > .05). Significant different features of the 2-dimensional ultrasound images between the groups were detected by the ultrasomics analysis, and the model could be successfully built. The ultrasomics score values between the groups were statistically significant after day 6 (days 6, 9, 12, and 13; P < .05). Ultrasomics revealed significant changes after the second injection of miR-122, showing the potential as an important imaging biomarker for gene therapy.

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.

PT112 - A multiparametric approach for dynamic contrast-enhanced ultrasound imaging of prostate cancer

PT112 - A multiparametric approach for dynamic contrast-enhanced ultrasound imaging of prostate cancer

Early prediction of tumor response to bevacizumab treatment in murine colon cancer models using three-dimensional dynamic contrast-enhanced ultrasound imaging.

Due to spatial tumor heterogeneity and consecutive sampling errors, it is critically important to assess treatment response following antiangiogenic therapy in three dimensions as two-dimensional assessment has been shown to substantially over- and underestimate treatment response. In this study, we evaluated whether three-dimensional (3D) dynamic contrast-enhanced ultrasound (DCE-US) imaging allows assessing early changes in tumor perfusion following antiangiogenic treatment (bevacizumab administered at a dose of 10mg/kg b.w.), and whether these changes could predict treatment response in colon cancer tumors that either are responsive (LS174T tumors) or none responsive (CT26) to the proposed treatment. Our results showed that the perfusion parameters of 3D DCE-US including peak enhancement (PE) and area under curve (AUC) significantly decreased by up to 69 and 77%, respectively, in LS174T tumors within 1day after antiangiogenic treatment (P=0.005), but not in CT26 tumors (P>0.05). Similarly, the percentage area of neovasculature significantly decreased in treated versus control LS174T tumors (P<0.001), but not in treated versus control CT26 tumors (P=0.796). Early decrease in both PE and AUC by 45-50% was predictive of treatment response in 100% (95% CI 69.2, 100%) of responding tumors, and in 100% (95% CI 88.4, 100%) and 86.7% (95% CI 69.3, 96.2%), respectively, of nonresponding tumors. In conclusion, 3D DCE-US provides clinically relevant information on the variability of tumor response to antiangiogenic therapy and may be further developed as biomarker for predicting treatment outcomes.

Measuring Absolute Blood Perfusion in Mice Using Dynamic Contrast-Enhanced Ultrasound

We investigated the feasibility of estimating absolute tissue blood perfusion using dynamic contrast-enhanced ultrasound (CEUS) imaging in mice. We developed a novel method of microbubble administration and a model-free approach to estimate absolute kidney perfusion, and explored the kidney as a reference organ to estimate absolute perfusion of a neuroblastoma tumor. We performed CEUS on the kidneys of CD1 nude mice using the VisualSonics VEVO 2100 imaging system. We estimated individual kidney blood perfusion using the burst–replenishment (BR) technique. We repeated the kidney imaging on the mice after a week. We performed CEUS imaging of a neuroblastoma mouse xenograft tumor along with its right kidney using two sets of microbubble administration parameters to estimate absolute tumor blood perfusion. We performed statistical tests at a significance level of 0.05. Our estimated absolute kidney perfusion (425 ± 123 mL/min/100 g) was within the range of previously reported values. There was no statistical difference between the estimated absolute kidney blood perfusions from the 2 wk of imaging (paired t-test, p = 0.09). We estimated the absolute blood perfusion in the neuroblastoma tumor to be 16.49 and 16.9 mL/min/100 g for the two sets of microbubble administration parameters (Wilcoxon rank-sum test, p = 0.6). We have established the kidney as a reliable reference organ in which to estimate absolute perfusion of other tissues. Using a neuroblastoma tumor, we have determined the feasibility of estimating absolute blood perfusion in tissues using contrast-enhanced ultrasound imaging.

Effects of a mono-disperse bubble population on the cumulative phase delay between second harmonic and fundamental component

A positive cumulative phase delay (CPD) between the second-harmonic and fundamental component of ultrasound waves is a marker specific to ultrasound contrast agents. Dynamic contrast-enhanced ultrasound images generated by using this marker have already been reported in the literature. However, only results obtained with a poly-disperse contrast agent (SonoVue®) have been presented. In this study, we compared CPD values obtained with standard SonoVue® and with a mono-disperse contrast agent (MDCA); the latter consisted of 4-μm microbubbles produced with a MicroSphere Creator®. The same as with SonoVue®, MDCA microbubbles were made of SF6 gas encapsulated in a monolayer phospholipid shell. An ULA-OP research platform equipped with an LA332 linear-array probe was employed to image a dedicated gelatin flow-phantom. Different frequencies (1.5 to 3.5 MHz) with a mechanical index of 0.7 were used. Images were reconstructed in a tomographic fashion, and the contrast to tissue ratio (CTR) evaluated. When imaging ...

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.

Early Prediction of Tumor Response to Bevacizumab Treatment in Murine Colon Cancer Models Using Three-Dimensional Dynamic Contrast-Enhanced Ultrasound Imaging

To evaluate whether three-dimensional (3D) dynamic contrast-enhanced ultrasound (DCE-US) imaging allows assessing early changes in tumor perfusion following antiangiogenic treatment, and whether these changes could predict treatment response in two animal models of colon cancer that simulate clinical responders and nonresponders. Clinically responding (n=20; LS174T) and nonresponding (n=20; CT26) colon cancer xenografts were established in 40 mice. Ten mice from each group received either antiangiogenic therapy (anti-VEGF blocking with bevacizumab, 10mg/kg) or saline as controls and were imaged longitudinally with 3D DCE-US at baseline (day 0) and days 1, 3, 7, and 10 following treatment. The change in three perfusion parameters (peak enhancement [PE], area under curve [AUC], time to peak [TTP]) relative to baseline was compared to the percentage area of neovasculature on ex vivo quantitative immunofluorescence and with response at day 10 assessed by tumor volume. PE and AUC significantly decreased in LS174T tumors within 1 day after antiangiogenic treatment (P=0.005), but not in CT26 tumors (P>0.05). Similarly, the percentage area of neovasculature on immunofluorescence significantly decreased in treated vs. control LS174T tumors (P<0.001), but not in treated vs. control CT26 tumors (P=0.796). Early decrease in both PE and AUC by 45-50% were predictive of treatment response in 100% (95% CI, 69.2%, 100%) of responding tumors, and in 100% (95% CI, 88.4%, 100%) and 86.7% (95% CI, 69.3%, 96.2%), respectively, of nonresponding tumors. 3D DCE-US provides clinically relevant information on the variability of tumor response to antiangiogenic therapy and could eventually serve as a biomarker for predicting treatment outcomes.

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

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

Functional Flow Patterns and Static Blood Pooling in Tumors Revealed by Combined Contrast-Enhanced Ultrasound and Photoacoustic Imaging.

Alterations in tumor perfusion and microenvironment have been shown to be associated with aggressive cancer phenotypes, raising the need for noninvasive methods of tracking these changes. Dynamic contrast-enhanced ultrasound (DCEUS) and photoacoustic (PA) imaging serve as promising candidates-one has the ability to measure tissue perfusion, whereas the other can be used to monitor tissue oxygenation and hemoglobin concentration. In this study, we investigated the relationship between the different functional parameters measured with DCEUS and PA imaging, using two morphologically different hind-limb tumor models and drug-induced alterations in an orthotopic breast tumor model. Imaging results showed some correlation between perfusion and oxygen saturation maps and the ability to sensitively monitor antivascular treatment. In addition, DCEUS measurements revealed different vascular densities in the core of specific tumors compared with their rims. Noncorrelated perfusion and hemoglobin concentration measurements facilitated discrimination between blood lakes and necrotic areas. Taken together, our results illustrate the utility of a combined contrast-enhanced ultrasound method with photoacoustic imaging to visualize blood flow patterns in tumors. Cancer Res; 76(15); 4320-31. ©2016 AACR.