Parametric imaging for characterizing focal liver lesions in contrast-enhanced ultrasound

Ultrasound innovations in abdominal radiology: evaluation of focal liver lesions

Focal liver lesions (FLLs) are common and are often first identified on abdominal ultrasound examinations. Although CT and MRI were historically required to noninvasively characterize many FLLs, introduction of microbubble contrast agents produced a groundbreaking change as contrast enhanced ultrasound (CEUS) showed vascularity to the capillary level for the first time. CEUS shows specific arterial phase enhancement patterns in benign lesions and accurately differentiates malignant lesions based on the timing and intensity of washout. Parametric time of arrival and microvascular imaging techniques can demonstrate vascularity in FLLs with significantly improved sensitivity compared with conventional Doppler techniques. Shear-wave elastography and quantitative ultrasound are generally used to evaluate diffuse liver disease but show promise in evaluation of FLLs.

A Dual-Branch Cross-Modality-Attention Network for Thyroid Nodule Diagnosis Based on Ultrasound Images and Contrast-Enhanced Ultrasound Videos

Contrast-enhanced ultrasound (CEUS) has been extensively employed as an imaging modality in thyroid nodule diagnosis due to its capacity to visualise the distribution and circulation of micro-vessels in organs and lesions in a non-invasive manner. However, current CEUS-based thyroid nodule diagnosis methods suffered from: 1) the blurred spatial boundaries between nodules and other anatomies in CEUS videos, and 2) the insufficient representations of the local structural information of nodule tissues by the features extracted only from CEUS videos. In this paper, we propose a novel dual-branch network with a cross-modality-attention mechanism for thyroid nodule diagnosis by integrating the information from tow related modalities, i.e., CEUS videos and ultrasound image. The mechanism has two parts: US-attention-from-CEUS transformer (UAC-T) and CEUS-attention-from-US transformer (CAU-T). As such, this network imitates the manner of human radiologists by decomposing the diagnosis into two correlated tasks: 1) the spatio-temporal features extracted from CEUS are hierarchically embedded into the spatial features extracted from US with UAC-T for the nodule segmentation; 2) the US spatial features are used to guide the extraction of the CEUS spatio-temporal features with CAU-T for the nodule classification. The two tasks are intertwined in the dual-branch end-to-end network and optimized with the multi-task learning (MTL) strategy. The proposed method is evaluated on our collected thyroid US-CEUS dataset. Experimental results show that our method achieves the classification accuracy of 86.92%, specificity of 66.41%, and sensitivity of 97.01%, outperforming the state-of-the-art methods. As a general contribution in the field of multi-modality diagnosis of diseases, the proposed method has provided an effective way to combine static information with its related dynamic information, improving the quality of deep learning based diagnosis with an additional benefit of explainability.

A Preliminary Study on the Application of Contrast-Enhanced Ultrasonography in Children With Peripheral Neuroblastic Tumors

The purpose of this study was to retrospectively analyze the characteristics of contrast-enhanced ultrasound (CEUS) images and quantitative parameters of time-intensity curves (TICs) in children's peripheral neuroblastic tumors (pNTs). By comparing the imaging features and quantitative parameters of the TICs of neuroblastoma (NB) and ganglioneuroblastoma (GNB) patients, we attempted to identify the distinguishing points between NB and GNB. A total of 35 patients confirmed to have pNTs by pathologic examination were included in this study. Each child underwent CEUS with complete imaging data (including still images and at least 3 min of video files). Twenty-four patients were confirmed to have NB, and 11 were considered to have GNB according to differentiation. The CEUS image features and quantitative parameters of the TICs of all lesions were analyzed to determine whether there were CEUS-related differences between the two types of pNT. There was a significant difference in the enhancement patterns of the CEUS features (χ2 = 5.303, p < 0.05), with more "peripheral-central" enhancement in the NB group and more "central-peripheral" enhancement in the GNB group. In the TIC, the rise time and time to peak were significantly different (p < 0.05). The receiver operating characteristic curve showed that the probability of ganglion cell NB increased significantly after RT > 15.29, with a sensitivity of 0.636 and a specificity of 0.958. When the peak time was greater than 16.155, the probability of NB increased significantly, with a sensitivity of 0.636 and a specificity of 0.958. The CEUS features of NB and GNB patients are very similar, and it is difficult to distinguish them. Rise time and time to peak may be useful in identifying GNB and NB, but the sample size of this study was small, and the investigation was only preliminary; a larger sample size is needed to support these conclusions.

Customized m-RCNN and hybrid deep classifier for liver cancer segmentation and classification

Diagnosing liver disease presents a significant medical challenge in impoverished countries, with over 30 billion individuals succumbing to it each year. Existing models for detecting liver abnormalities suffer from lower accuracy and higher constraint metrics. As a result, there is a pressing need for improved, efficient, and effective liver disease detection methods. To address the limitations of current models, this method introduces a deep liver segmentation and classification system based on a Customized Mask-Region Convolutional Neural Network (cm-RCNN). The process begins with preprocessing the input liver image, which includes Adaptive Histogram Equalization (AHE). AHE helps dehaze the input image, remove color distortion, and apply linear transformations to obtain the preprocessed image. Next, a precise region of interest is segmented from the preprocessed image using a novel deep strategy called cm-RCNN. To enhance segmentation accuracy, the architecture incorporates the ReLU activation function and the modified sigmoid activation function. Subsequently, a variety of features are extracted from the segmented image, including ResNet features, shape features (area, perimeter, approximation, and convex hull), and enhanced median binary pattern. These extracted features are then used to train a hybrid classification model, which incorporates classifiers like SqueezeNet and DeepMaxout models. The final classification outcome is determined by averaging the scores obtained from both classifiers.

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.

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

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

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.

Monitoring Dual VEGF Inhibition in Human Pancreatic Tumor Xenografts With Dynamic Contrast-Enhanced Ultrasound

BACKGROUND

Association of drugs acting against different antiangiogenic mechanisms may increase therapeutic effect and reduce resistance. Noninvasive monitoring of changes in the antiangiogenic response of individual tumors could guide selection and administration of drug combinations. Noninvasive detection of early therapeutic response during dual, vertical targeting of the vascular endothelial growth factor pathway was investigated in an ectopic subcutaneous xenograft model for human pancreatic tumor.

METHODS

Dynamic contrast-enhanced ultrasound 12 MHz was used to monitor tumor-bearing Naval Medical Research Institute mice beginning 15 days after tumor implantation. Mice received therapy from 15 to 29 days with sorafenib (N = 9), ziv-aflibercept (N = 11), combined antiangiogenic agents (N = 11), and placebo control (N = 14). Sorafenib (BAY 43-9006; Nexavar), a multikinase inhibitor acting on Raf kinase and receptor tyrosine kinases-including vascular endothelial growth factor receptors 2 and 3-was administered daily (60 mg/kg, per os). Ziv-aflibercept (ZALTRAP), a high-affinity ligand trap blocking the activity of vascular endothelial growth factor A, vascular endothelial growth factor B, and placental growth factor was administered twice per week (40 mg/kg, intraperitoneally).

RESULTS

Functional evaluation with dynamic contrast-enhanced ultrasound indicated stable tumor vascularization for the control group while revealing significant and sustained reduction after 1 day of therapy in the combined group (P = .007). There was no survival benefit or penalty due to drug combination. The functional progression-free survival assessed with dynamic contrast-enhanced ultrasound was significantly higher for the 3 treated groups; whereas, the progression-free survival based on tumor size did not discriminate therapeutic effect.

CONCLUSIONS

Dynamic contrast-enhanced ultrasound, therefore, presents strong potential to monitor microvascular modifications during antiangiogenic therapy, a key role to monitoring antiangiogenic combining therapy to adapt dose range drug.

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.

Differential Diagnosis of Atypical Hepatocellular Carcinoma in Contrast-Enhanced Ultrasound Using Spatio-Temporal Diagnostic Semantics

Atypical Hepatocellular Carcinoma (HCC) is very hard to distinguish from Focal Nodular Hyperplasia (FNH) in routine imaging. However little attention was paid to this problem. This paper proposes a novel liver tumor Computer-Aided Diagnostic (CAD) approach extracting spatio-temporal semantics for atypical HCC. With respect to useful diagnostic semantics, our model automatically calculates three types of semantic feature with equally down-sampled frames based on Contrast-Enhanced Ultrasound (CEUS). Thereafter, a Support Vector Machine (SVM) classifier is trained to make the final diagnosis. Compared with traditional methods for diagnosing HCC, the proposed model has the advantage of less computational complexity and being able to handle the atypical HCC cases. The experimental results show that our method obtained a pretty considerable performance and outperformed two traditional methods. According to the results, the average accuracy reaches 94.40%, recall rate 94.76%, F1-score value 94.62%, specificity 93.62% and sensitivity 94.76%, indicating good merit for automatically diagnosing atypical HCC cases.

Respiratory motion correction for liver contrast-enhanced ultrasound by automatic selection of a reference image

PURPOSE

Respiratory motion correction is necessary for the quantitative analysis of liver contrast-enhanced ultrasound (CEUS) image sequences. Most respiratory motion correction methods are based on the dual mode of CEUS image sequences, including contrast and grayscale image sequences. Due to free-breathing motion, the acquired two-dimensional (2D) ultrasound cine might show the in-plane and out-of-plane motion of tumors. The registration of an entire 2D ultrasound contrast image sequence based on out-of-plane images is ineffective. For the respiratory motion correction of CEUS sequences, the reference image is usually considered the standard for the deletion of any out-of-plane images. Most methods used for the selection of the reference image are subjective in nature. Here, a quantitative selection method for an optimal reference image from CEUS image sequences in the B mode and contrast mode was explored.

METHODS

The original high-dimensional ultrasound grayscale image data were mapped into a two-dimensional space using Laplacian Eigenmaps (LE), and K-means clustering was adopted. The center image of the larger cluster with a near-peak contrast intensity was considered the optimal ultrasound reference image. In the ultrasound grayscale image sequence, the images with the maximum correlations to the reference image in the same time interval were selected as the corrected image sequence. The effectiveness of this proposed method was then validated on 18 CEUS cases of VX2 tumors in rabbit livers.

RESULTS

Correction smoothed the time-intensity curves (TICs) extracted from the region of interest of the CEUS image sequences. Before correction, the average of the total mean structural similarity (TMSSIM) and the average of the mean correlation coefficient (MCC) from the image sequences were 0.45 ± 0.11 and 0.67 ± 0.16, respectively, and after correction, the average TMSSIM and MCC increased (P < 0.001) by 31% to 0.59 ± 0.11 and by 21% to 0.81 ± 0.11, respectively. The average deviation value (DV) index of the TICs from the image sequences prior to correction was 92.16 ± 18.12, and correction reduced the average to 31.71 ± 7.31. The average TMSSIM and MCC values after correction using the mean frame of the reference image (MBMFRI) were clearly lower than those after correction using the proposed method (P < 0.001). Moreover, the average DV after correction using the MBMFRI was obviously higher than that after correction using the proposed method (P < 0.001).

CONCLUSIONS

The breathing frequency of rabbits is notably faster than that of human beings, but the proposed correction method could reduce the effect of the respiratory motion in the CEUS image sequences. The reference image was selected quantitatively, which could improve the accuracy of the quantitative analysis of rabbit liver CEUS sequences using the reference image method based on the current standard of manual selection and the MBMFRI. This easy-to-operate method can potentially be used in both animal studies and clinical applications.

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.

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.

A fast scheme for renal microvascular perfusion functional imaging: Assessed by an imaging quality evaluation model

PURPOSE

This study aimed to develop a fast scheme of multiparametric perfusion functional imaging (PFI) based on dynamic contrast-enhanced ultrasound (DCEUS) for assessing renal microvascular hemodynamics.

METHOD

The flow process in the DCEUS-based PFI was modified step-by-step to improve its operational efficiency, which was validated through in vivo renal perfusion experiments. A multiparametric model with a comprehensive coefficient of imaging quality (CIQ) was then built on four terms of the average information entropy, contrast, gray, and noise coefficient of PFIs to evaluate the sacrifice of imaging quality during modifications of DCEUS-based PFI.

RESULTS

The multiparametric model successfully evaluated modifications of DCEUS-based PFI from multiple perspectives (R²  = 0.73, P < 0.01). Compared with the raw scheme in the renal sagittal and coronal planes, the fast PFI scheme significantly improved its operational efficiency by 62.82 ± 1.07% (P < 0.01) and the nine PFIs simultaneously maintained a similar CIQ of 0.26 ± 0.06.

CONCLUSIONS

The inhomogeneous hemodynamic distributions with a ring-like feature in the renal microvasculature were accurately and efficiently characterized by the fast PFI scheme. The fast PFI scheme can be applied for early diagnosis, follow-up evaluation and monitoring treatment of chronic kidney disease.

US Molecular Imaging of Acute Ileitis: Anti-Inflammatory Treatment Response Monitored with Targeted Microbubbles in a Preclinical Model

Purpose To evaluate whether dual-selectin-targeted US molecular imaging allows longitudinal monitoring of anti-inflammatory treatment effects in an acute terminal ileitis model in swine. Materials and Methods The Institutional Animal Care and Use Committee approved all animal studies. Fourteen swine with chemically induced acute terminal ileitis (day 0) were randomized into the following groups: (a) an anti-inflammatory treatment group (n = 8; meloxicam, 0.25 mg per kilogram of body weight; prednisone, 0.5 mg/kg) and (b) a control group (n = 6; saline). US molecular imaging was performed with a clinical US machine after intravenous injection of clinically translatable dual P- and E-selectin-targeted microbubbles (5 × 108/kg). Three inflamed bowel segments per swine were imaged at baseline, as well as on days 1, 3, and 6 after treatment initiation. At day 6, bowel segments were analyzed ex vivo for selectin expression levels by using quantitative immunofluorescence. Results After induction of inflammation, US molecular imaging signal increased at day 1 in both animal groups (P < .001). At day 3, signal in the treatment group decreased (P < .001 vs day 1), while signal in control animals did not significantly change (P = .18 vs day 1) and was higher (P = .001) compared with that in the treatment group. At day 6, signal in the treatment group further decreased and remained lower (P = .02) compared with that in the control group. Immunofluorescence confirmed significant (P ≤ .04) downregulation of both P- and E-selectin expression levels in treated versus control bowel segments. Conclusion Dual-selectin-targeted US molecular imaging allows longitudinal monitoring of anti-inflammatory treatment effects in a large-animal model of acute ileitis. This supports further clinical development of this quantitative and radiation-free technique for monitoring inflammatory bowel disease. © RSNA, 2018 Online supplemental material is available for this article.

DCEUS-based multiparametric perfusion imaging using pulse-inversion Bubblet decorrelation

PURPOSE

This study aimed to clarify the influences of composite dynamic contrast-enhanced ultrasound (DCEUS) on multiparametric perfusion imaging (PPI) and to develop a novel PPI scheme through pulse-inversion Bubblet decorrelation (PIBD) to improve its contrast and detailed discriminability.

METHOD

In in vivo perfusion experiments on rabbit kidneys, a pair of phase-inverted "Bubblets" was constructed. Phase-inverted raw radiofrequency echoes were reconstructed by using the maximum coefficients obtained from Bubblet decorrelation analysis and summed to form DCEUS loops. Nine perfusion parameters were estimated from these loops and color coded to create the corresponding PIBD-based PPIs.

RESULTS

In addition to time-related PPIs, the contrast and detailed discriminability quantified by the average contrast and information entropy of intensity- and ratio-related PPIs were proportional to the microbubble detection sensitivity and microvascular discriminability evaluated by CTR in DCEUS techniques. Compared with the second harmonic, the CTR of DCEUS and the average contrast and information entropy of PPI were significantly improved by 9.03 ± 5.39 dB (P < 0.01), 6.39 ± 1.38 dB (P < 0.01), and 0.57 ± 0.15 (P < 0.05) in PIBD technique, respectively.

CONCLUSIONS

As a multiparametric functional imaging technique, these improvements in the proposed scheme can be beneficial to accurately quantify and depict the hemodynamic perfusion features and details of tumor angiogenesis, and further can also assist clinicians in making a confirmed diagnosis.

In-vitro evaluation of accuracy of dynamic contrast-enhanced ultrasound (DCEUS)-based parametric perfusion imaging with respiratory motion-compensation

PURPOSE

The accuracy of multi-parametric perfusion imaging (PPI) based on dynamic contrast-enhanced ultrasound is disturbed by the respiratory motion in some cases, especially during characterizing hemodynamic features of abdominal tumor angiogenesis. This study aimed to effectively remove those disturbances on PPI and evaluate its accuracy.

METHOD

The respiratory motion-compensation (rMoCo) strategy in PPI was modified by employing non-negative matrix factorization combined with phase-by-phase compensation. According to the known and controllable ground truths in in-vitro perfusion experiments, the accuracy of the modified rMoCo strategy was further evaluated from multiple perspectives in a simulated dual-vessel flow phantom.

RESULTS

Compared with that of PPIs without rMoCo, the mean correlation coefficient between six PPIs with rMoCo and the corresponding static PPIs was up to 0.98 ± 0.01 and improved by 0.17 ± 0.04 (P < 0.05). The estimated error of vascular diameter decreased from 87.85% (P < 0.05) to 7.25% (P < 0.05) after rMoCo. PPIs with rMoCo were significantly consistent with static PPIs without respiratory motion disturbances.

CONCLUSIONS

These quantitative results illustrated the disturbances induced by respiratory motion were effectively removed and the accuracy of PPIs was significantly improved. The partial parabolic and bimodal hemodynamic characteristics and the anatomical structures and sizes were accurately quantified and depicted by PPIs with rMoCo. The modified method can benefit physicians in providing accurate diagnoses and in developing appropriate therapeutic strategies for abdominal diseases.

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.

Contrast-enhanced US for characterization of focal liver lesions: a comprehensive meta-analysis

OBJECTIVES

This meta-analysis was performed to evaluate the accuracy of contrast-enhanced ultrasound (CEUS) in differentiating malignant from benign focal liver lesions (FLLs).

METHODS

Cochrane Library, PubMed and Web of Science databases were systematically searched and checked for studies using CEUS in characterization of FLLs. Data necessary to construct 2×2 contingency tables were extracted from included studies. The QUADAS tool was utilized to assess the methodologic quality of the studies. Meta-analysis included data pooling, subgroup analyses, meta-regression and investigation of publication bias was comprehensively performed.

RESULTS

Fifty-seven studies were included in this meta-analysis and the overall diagnostic accuracy in characterization of FLLs was as follows: pooled sensitivity, 0.92 (95%CI: 0.91-0.93); pooled specificity, 0.87 (95%CI: 0.86-0.88); diagnostic odds ratio, 104.20 (95%CI: 70.42-154.16). Subgroup analysis indicated higher diagnostic accuracy of the second-generation contrast agents (CAs) than the first-generation CA (Levovist; DOR: 118.27 vs. 62.78). Furthermore, Sonazoid demonstrated the highest diagnostic accuracy among three major CAs (SonoVue, Levovist and Sonazoid; DOR: 118.82 vs. 62.78 vs. 227.39). No potential publication bias was observed of the included studies.

CONCLUSION

CEUS is an accurate tool to stratify the risk of malignancy in FLLs. The second-generation CAs, especially Sonazoid may greatly improve diagnostic performance.

KEY POINTS

• CEUS shows excellent diagnostic accuracy in differentiating malignant from benign FLLs. • The second-generation CAs have higher diagnostic accuracy than first-generation CAs. • Sonazoid demonstrates the highest diagnostic accuracy among three major CAs.

Focal Liver Lesions: Computer-aided Diagnosis by Using Contrast-enhanced US Cine Recordings

Purpose To assess the performance of computer-aided diagnosis (CAD) systems and to determine the dominant ultrasonographic (US) features when classifying benign versus malignant focal liver lesions (FLLs) by using contrast material-enhanced US cine clips. Materials and Methods One hundred six US data sets in all subjects enrolled by three centers from a multicenter trial that included 54 malignant, 51 benign, and one indeterminate FLL were retrospectively analyzed. The 105 benign or malignant lesions were confirmed at histologic examination, contrast-enhanced computed tomography (CT), dynamic contrast-enhanced magnetic resonance (MR) imaging, and/or 6 or more months of clinical follow-up. Data sets included 3-minute cine clips that were automatically corrected for in-plane motion and automatically filtered out frames acquired off plane. B-mode and contrast-specific features were automatically extracted on a pixel-by-pixel basis and analyzed by using an artificial neural network (ANN) and a support vector machine (SVM). Areas under the receiver operating characteristic curve (AUCs) for CAD were compared with those for one experienced and one inexperienced blinded reader. A third observer graded cine quality to assess its effects on CAD performance. Results CAD, the inexperienced observer, and the experienced observer were able to analyze 95, 100, and 102 cine clips, respectively. The AUCs for the SVM, ANN, and experienced and inexperienced observers were 0.883 (95% confidence interval [CI]: 0.793, 0.940), 0.829 (95% CI: 0.724, 0.901), 0.843 (95% CI: 0.756, 0.903), and 0.702 (95% CI: 0.586, 0.782), respectively; only the difference between SVM and the inexperienced observer was statistically significant. Accuracy improved from 71.3% (67 of 94; 95% CI: 60.6%, 79.8%) to 87.7% (57 of 65; 95% CI: 78.5%, 93.8%) and from 80.9% (76 of 94; 95% CI: 72.3%, 88.3%) to 90.3% (65 of 72; 95% CI: 80.6%, 95.8%) when CAD was in agreement with the inexperienced reader and when it was in agreement with the experienced reader, respectively. B-mode heterogeneity and contrast material washout were the most discriminating features selected by CAD for all iterations. CAD selected time-based time-intensity curve (TIC) features 99.0% (207 of 209) of the time to classify FLLs, versus 1.0% (two of 209) of the time for intensity-based features. None of the 15 video-quality criteria had a statistically significant effect on CAD accuracy-all P values were greater than the Holm-Sidak α-level correction for multiple comparisons. Conclusion CAD systems classified benign and malignant FLLs with an accuracy similar to that of an expert reader. CAD improved the accuracy of both readers. Time-based features of TIC were more discriminating than intensity-based features. ^© RSNA, 2017 Online supplemental material is available for this article.

Automatic Identification of the Optimal Reference Frame for Segmentation and Quantification of Focal Liver Lesions in Contrast-Enhanced Ultrasound

Post-examination interpretation of contrast-enhanced ultrasound (CEUS) cineloops of focal liver lesions (FLLs) requires offline manual assessment by experienced radiologists, which is time-consuming and generates subjective results. Such assessment usually starts by manually identifying a reference frame, where FLL and healthy parenchyma are well-distinguished. This study proposes an automatic computational method to objectively identify the optimal reference frame for distinguishing and hence delineating an FLL, by statistically analyzing the temporal intensity variation across the spatially discretized ultrasonographic image. Level of confidence and clinical value of the proposed method were quantitatively evaluated on retrospective multi-institutional data (n = 64) and compared with expert interpretations. Results support the proposed method for facilitating easier, quicker and reproducible assessment of FLLs, further increasing the radiologists' confidence in diagnostic decisions. Finally, our method yields a useful training tool for radiologists, widening CEUS use in non-specialist centers, potentially leading to reduced turnaround times and lower patient anxiety and healthcare costs.

Quantitative Analysis of Hepatic Microcirculation in Rabbits After Liver Ischemia-Reperfusion Injury Using Contrast-Enhanced Ultrasound

Previous studies have shown that contrast-enhanced ultrasound (CEUS) can be used quantitatively to analyze microcirculation blood perfusion in hepatocellular carcinoma patients. However, limited data have described the application of CEUS in hepatic microcirculation after liver ischemic-reperfusion injury (IRI). The purpose of this study was to explore the use of CEUS quantitatively to assess liver microcirculation after liver IRI. We randomly sorted 45 New Zealand rabbits into 3 groups (15 in each). Group A was a control group in which the rabbits underwent laparotomy alone. In groups B and C, hepatic blood was blocked for 30 min. Simultaneously, rabbits in group C underwent left lateral lobe resection. After 30 min of ischemia, CEUS was conducted after 0 h, 1 h, 6 h and 24 h of reperfusion in the 3 groups. Time-intensity curves (TICs) for CEUS were constructed and quantitative parameters (maximum intensity [IMAX], rise time [RT], time to peak [TTP] and mean transit time [mTT]) were obtained. In addition, serum aspartate aminotransferase (AST) and alanine aminotransferase (ALT) levels were computed to estimate liver function before the operation and at 0 h, 1 h, 6 h and 24 h after reperfusion, respectively. Pathologic changes in the liver after reperfusion were also observed. Simultaneously, the correlations between serum transaminase and a variety of quantitative analysis parameters were analyzed. In groups B and C, the IMAX value decreased; whereas RT, TTP, mTT and serum ALT and AST levels increased significantly in comparison with those in group A after 0 h and 1 h of reperfusion. The pathology revealed that erythrocytes were destroyed and microcirculation was disturbed. Then, at 6 h of reperfusion, the IMAX continued to decrease. Additionally, the levels of RT, TTP, mTT and serum ALT and AST increased in comparison with those at 1 h of reperfusion. The pathologic analysis revealed inflammatory cell aggregation and leukocyte infiltration. After 24 h of reperfusion, the IMAX was reduced in comparison with that of the 6-h group. The levels of RT, TTP, mTT and serum ALT and serum AST were increased in comparison with that of the 6-h group. These findings were in accordance with the pathologic analysis. In addition, serum transaminase had a negative correlation with IMAX (p < 0.001) and a positive correlation with RT, TTP and mTT (all p < 0.001). So, in conclusion, the quantitative analysis of CEUS can be used to assess hepatic microcirculation after liver IRI.

Molecular Ultrasound Imaging of the Spinal Cord for the Detection of Acute Inflammation

Molecular ultrasound imaging provides the ability to detect physiologic processes non-invasively by targeting a wide variety of biological markers in vivo. The current study investigates the novel application of molecular ultrasound imaging for the detection of neural inflammation. Using a murine model with acutely injured spinal cords (n=31), subjects were divided into four groups, each being administered ultrasound contrast microbubbles bearing antibodies against various known inflammatory molecules (P-selectin, vascular cell adhesion protein 1 [VCAM-1], intercellular adhesion molecule 1 [ICAM-1], and isotype control) during molecular ultrasound imaging. Upon administration of the targeted contrast agent, ultrasound imaging of the injured spinal cord was performed at 40MHz for seven minutes, followed by a bursting pulse. We observed significantly enhanced signals from contrast targeted to P-selectin and VCAM-1, using a variety of outcome measures. These findings provide preclinical evidence that molecular ultrasound imaging could be a useful tool in the detection of neural inflammation.

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.

Computer-Aided Diagnosis of Focal Liver Lesions Using Contrast-Enhanced Ultrasonography With Perflubutane Microbubbles

This paper proposes an automatic classification method based on machine learning in contrast-enhanced ultrasonography (CEUS) of focal liver lesions using the contrast agent Sonazoid. This method yields spatial and temporal features in the arterial phase, portal phase, and post-vascular phase, as well as max-hold images. The lesions are classified as benign or malignant and again as benign, hepatocellular carcinoma (HCC), or metastatic liver tumor using support vector machines (SVM) with a combination of selected optimal features. Experimental results using 98 subjects indicated that the benign and malignant classification has 94.0% sensitivity, 87.1% specificity, and 91.8% accuracy, and the accuracy of the benign, HCC, and metastatic liver tumor classifications are 84.4%, 87.7%, and 85.7%, respectively. The selected features in the SVM indicate that combining features from the three phases are important for classifying FLLs, especially, for the benign and malignant classifications. The experimental results are consistent with CEUS guidelines for diagnosing FLLs. This research can be considered to be a validation study, that confirms the importance of using features from these phases of the examination in a quantitative manner. In addition, the experimental results indicate that for the benign and malignant classifications, the specificity without the post-vascular phase features is significantly lower than the specificity with the post-vascular phase features. We also conducted an experiment on the operator dependency of setting regions of interest and observed that the intra-operator and inter-operator kappa coefficients were 0.45 and 0.77, respectively.

Recognizing Focal Liver Lesions in CEUS With Dynamically Trained Latent Structured Models

This work investigates how to automatically classify Focal Liver Lesions (FLLs) into three specific benign or malignant types in Contrast-Enhanced Ultrasound (CEUS) videos, and aims at providing a computational framework to assist clinicians in FLL diagnosis. The main challenge for this task is that FLLs in CEUS videos often show diverse enhancement patterns at different temporal phases. To handle these diverse patterns, we propose a novel structured model, which detects a number of discriminative Regions of Interest (ROIs) for the FLL and recognize the FLL based on these ROIs. Our model incorporates an ensemble of local classifiers in the attempt to identify different enhancement patterns of ROIs, and in particular, we make the model reconfigurable by introducing switch variables to adaptively select appropriate classifiers during inference. We formulate the model learning as a non-convex optimization problem, and present a principled optimization method to solve it in a dynamic manner: the latent structures (e.g. the selections of local classifiers, and the sizes and locations of ROIs) are iteratively determined along with the parameter learning. Given the updated model parameters in each step, the data-driven inference is also proposed to efficiently determine the latent structures by using the sequential pruning and dynamic programming method. In the experiments, we demonstrate superior performances over the state-of-the-art approaches. We also release hundreds of CEUS FLLs videos used to quantitatively evaluate this work, which to the best of our knowledge forms the largest dataset in the literature. Please find more information at "http://vision.sysu.edu.cn/projects/fllrecog/".

Evaluation of the Accuracy of Liver Lesion DCEUS Quantification With Respiratory Gating

Confidence in the accuracy of dynamic contrast enhanced ultrasound (DCEUS) quantification parameters is imperative for the correct diagnosis of liver lesion perfusion characteristics. An important source of uncertainty in liver DCEUS acquisitions is artifacts introduced by respiratory motion. The objective of this study is to construct a respiratory motion simulation model (RMSM) of dual contrast imaging mode acquisitions of liver lesions in order to evaluate an algorithm for automatic respiratory gating (ARG). The respiratory kinetics as well as the perfusion models of the liver lesion and parenchyma used by the RMSM were solely derived from clinical data. The quality of fit (of the DCEUS data onto the bolus kinetics model) depends on the respiration amplitude. Similar trends in terms of quality of fit as a function of respiration amplitude were observed from RMSM and clinical data. The errors introduced on the DCEUS quantification under the influence of respiration were evaluated. The RMSM revealed that the error in the liver lesion DCEUS quantification parameters significantly decreased (p < 0.001) from a maximum of 32.3% to 6.2% when ARG was used. The use of RMSM clearly demonstrates the capability of the ARG algorithm in significantly reducing errors introduced from both in-plane and out-of-plane respiratory motion.

Pixel-based approach to assess contrast-enhanced ultrasound kinetics parameters for differential diagnosis of rheumatoid arthritis

Inflammatory rheumatic diseases are the leading causes of disability and constitute a frequent medical disorder, leading to inability to work, high comorbidity, and increased mortality. The standard for diagnosing and differentiating arthritis is based on clinical examination, laboratory exams, and imaging findings, such as synovitis, bone edema, or joint erosions. Contrast-enhanced ultrasound (CEUS) examination of the small joints is emerging as a sensitive tool for assessing vascularization and disease activity. Quantitative assessment is mostly performed at the region of interest level, where the mean intensity curve is fitted with an exponential function. We showed that using a more physiologically motivated perfusion curve, and by estimating the kinetic parameters separately pixel by pixel, the quantitative information gathered is able to more effectively characterize the different perfusion patterns. In particular, we demonstrated that a random forest classifier based on pixelwise quantification of the kinetic contrast agent perfusion features can discriminate rheumatoid arthritis from different arthritis forms (psoriatic arthritis, spondyloarthritis, and arthritis in connective tissue disease) with an average accuracy of 97%. On the contrary, clinical evaluation (DAS28), semiquantitative CEUS assessment, serological markers, or region-based parameters do not allow such a high diagnostic accuracy.

2-tier in-plane motion correction and out-of-plane motion filtering for contrast-enhanced ultrasound

OBJECTIVES

Contrast-enhanced ultrasound (CEUS) cines of focal liver lesions (FLLs) can be quantitatively analyzed to measure tumor perfusion on a pixel-by-pixel basis for diagnostic indication. However, CEUS cines acquired freehand and during free breathing cause nonuniform in-plane and out-of-plane motion from frame to frame. These motions create fluctuations in the time-intensity curves (TICs), reducing the accuracy of quantitative measurements. Out-of-plane motion cannot be corrected by image registration in 2-dimensional CEUS and degrades the quality of in-plane motion correction (IPMC). A 2-tier IPMC strategy and adaptive out-of-plane motion filter (OPMF) are proposed to provide a stable correction of nonuniform motion to reduce the impact of motion on quantitative analyses.

MATERIALS AND METHODS

A total of 22 cines of FLLs were imaged with dual B-mode and contrast specific imaging to acquire a 3-minute TIC. B-mode images were analyzed for motion, and the motion correction was applied to both B-mode and contrast images. For IPMC, the main reference frame was automatically selected for each cine, and subreference frames were selected in each respiratory cycle and sequentially registered toward the main reference frame. All other frames were sequentially registered toward the local subreference frame. Four OPMFs were developed and tested: subsample normalized correlation (NC), subsample sum of absolute differences, mean frame NC, and histogram. The frames that were most dissimilar to the OPMF reference frame using 1 of the 4 above criteria in each respiratory cycle were adaptively removed by thresholding against the low-pass filter of the similarity curve. Out-of-plane motion filter was quantitatively evaluated by an out-of-plane motion metric (OPMM) that measured normalized variance in the high-pass filtered TIC within the tumor region-of-interest with low OPMM being the goal. Results for IPMC and OPMF were qualitatively evaluated by 2 blinded observers who ranked the motion in the cines before and after various combinations of motion correction steps.

RESULTS

Quantitative measurements showed that 2-tier IPMC and OPMF improved imaging stability. With IPMC, the NC B-mode metric increased from 0.504 ± 0.149 to 0.585 ± 0.145 over all cines (P < 0.001). Two-tier IPMC also produced better fits on the contrast-specific TIC than industry standard IPMC techniques did (P < 0.02). In-plane motion correction and OPMF were shown to improve goodness of fit for pixel-by-pixel analysis (P < 0.001). Out-of-plane motion filter reduced variance in the contrast-specific signal as shown by a median decrease of 49.8% in the OPMM. Two-tier IPMC and OPMF were also shown to qualitatively reduce motion. Observers consistently ranked cines with IPMC higher than the same cine before IPMC (P < 0.001) as well as ranked cines with OPMF higher than when they were uncorrected.

CONCLUSION

The 2-tier sequential IPMC and adaptive OPMF significantly reduced motion in 3-minute CEUS cines of FLLs, thereby overcoming the challenges of drift and irregular breathing motion in long cines. The 2-tier IPMC strategy provided stable motion correction tolerant of out-of-plane motion throughout the cine by sequentially registering subreference frames that bypassed the motion cycles, thereby overcoming the lack of a nearly stationary reference point in long cines. Out-of-plane motion filter reduced apparent motion by adaptively removing frames imaged off-plane from the automatically selected OPMF reference frame, thereby tolerating nonuniform breathing motion. Selection of the best OPMF by minimizing OPMM effectively reduced motion under a wide variety of motion patterns applicable to clinical CEUS. These semiautomated processes only required user input for region-of-interest selection and can improve the accuracy of quantitative perfusion measurements.

Multimodal approach to assess tumour vasculature and potential treatment effect with DCE-US and DCE-MRI quantification in CWR22 prostate tumour xenografts

The aim of this study was to compare intratumoural heterogeneity and longitudinal changes assessed by dynamic contrast-enhanced ultrasound (DCE-US) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in prostate tumour xenografts. In vivo DCE-US and DCE-MRI were obtained 24 h pre- (day 0) and post- (day 2) radiation treatment with a single dose of 7.5 Gy. Characterization of the tumour vasculature was determined by Brix pharmacokinetic analysis of the time-intensity curves. Histogram analysis of voxels showed significant changes (p < 0.001) from day 0 to day 2 in both modalities for kep , the exchange rate constant from the extracellular extravascular space to the plasma, and kel , the elimination rate constant of the contrast. In addition, kep and kel values from DCE-US were significantly higher than those derived from DCE-MRI at day 0 (p < 0.0001) for both groups. At day 2, kel followed the same tendency for both groups, whereas kep showed this tendency only for the treated group in intermediate-enhancement regions. Regarding kep median values, longitudinal changes were not found for any modality. However, at day 2, kep linked to DCE-US was correlated to MVD in high-enhancement areas for the treated group (p = 0.05). In contrast, correlation to necrosis was detected for the control group in intermediate-enhancement areas (p < 0.1). Intratumoural heterogeneity and longitudinal changes in tumour vasculature were assessed for both modalities. Microvascular parameters derived from DCE-US seem to provide reliable biomarkers during radiotherapy as validated by histology. Furthermore, DCE-US could be a stand-alone or a complementary technique.

Role of portal vein tumor thrombosis in quantitative perfusion analysis of contrast-enhanced ultrasound of hepatocellular carcinoma

The goal of our study was to evaluate the differences between quantitative parameters of hepatocellular carcinoma (HCC) with or without portal vein tumor thrombosis (PVTT) on contrast-enhanced ultrasound (CEUS). Twenty-four patients with HCC with PVTT and 48 without PVTT underwent CEUS using sulfur hexafluoride microbubbles. Dynamic images were analyzed with quantification software. Time-intensity curves were obtained for HCC and surrounding liver parenchyma, and parameters including the intensity maximum (IMAX), rising time (RT), mean transit time and time to peak (TTP) were compared within and between the PVTT and control groups, respectively. RT and TTP of HCC were significantly faster than those of surrounding liver parenchyma in both the PVTT and control groups. IMAX of HCC was significantly stronger than that of surrounding liver in the control group, but not significantly different from that of liver parenchyma in the PVTT group. RT and TTP of HCC and surrounding liver were significantly faster in the PVTT group compared with the control group, whereas IMAX values of HCC in the PVTT group were lower than those in the control group. HCC with PVTT presents different hemodynamic parameters, with faster RT and TTP and lower IMAX than those for HCC without PVTT. Quantitative perfusion analysis of CEUS may be a potential method for predicting PVTT.

Automatic motion estimation using flow parameters for dynamic contrast-enhanced ultrasound

Dynamic contrast-enhanced ultrasound (DCE-US) sequences are subject to motion which can disturb functional flow quantification. This can make estimated parameters more variable or unreliable. Methods that compensate for motion are therefore desirable. The most commonly used motion correction techniques in DCE-US register the images in the sequence with respect to a user-selected reference image. However, this image may not include all features that are representative of the whole sequence. Moreover, image-based registration neglects pertinent, functional-flow information contained in the DCE-US sequence. An operator-free method is proposed that combines the motion estimation and flow-parameter quantification (M/Q method) in a single mathematical framework. This method is based on a realistic multiplicative model of the DCE-US noise. By computing likelihood in this model, motion and flow parameters are both estimated iteratively. First, the maximization is accomplished by estimating functional and motion parameters. Then, a final registration based on a non-parametric temporal smoothing of the sequence is performed. This method is compared to a conventional (mutual information) registration method where all the images of the sequence are registered with respect to a reference image chosen by an expert. The two methods are evaluated on simulated sequences and DCE-US sequences acquired in patients (N = 15). The M/Q method demonstrates significantly (p < 0.05) lower Dice coefficients and Hausdorff distance than the conventional method on the simulated data sets. On the in vivo sequences analysed, the M/Q methods outperformed the conventional method in terms of mean Dice and Hausdorff distance on 80% of the sequences, and in terms of standard deviation of Dice and Hausdorff distance on 87% of the sequences.

Dual-mode registration of dynamic contrast-enhanced ultrasound combining tissue and contrast sequences

This study proposes a new method for automatic, iterative image registration in the context of dynamic contrast-enhanced ultrasound (DCE-US) imaging. By constructing a cost function of image registration using a combination of the tissue and contrast-microbubble responses, this new method, referred to as dual-mode registration, performs alignment based on both tissue and vascular structures. Data from five focal liver lesions (FLLs) were used for the evaluation. Automatic registration based on the dual-mode registration technique and tissue-mode registration obtained using the linear response image sequence alone were compared to manual alignment of the sequence by an expert. Comparison of the maximum distance between the transformations applied by the automatic registration techniques and those from expert manual registration reference showed that the dual-mode registration provided better precision than the tissue-mode registration for all cases. The reduction of maximum distance ranged from 0.25 to 9.3mm. Dual-mode registration is also significantly better than tissue-mode registration for the five sequences with p-values lower than 0.03. The improved sequence alignment is also demonstrated visually by comparison of images from the sequences and the video playbacks of the motion-corrected sequences. This new registration technique better maintains a selected region of interest (ROI) within a fixed position of the image plane throughout the DCE-US sequence. This should reduce motion-related variability of the echo-power estimations and, thus, contribute to more robust perfusion quantification with DCE-US.

Automatic respiratory gating for contrast ultrasound evaluation of liver lesions

Dynamic contrast-enhanced ultrasound (DCEUS) has been used in radiology for many years for lesion detection and characterization. In recent years, more emphasis has been placed on tumor perfusion quantification with DCEUS. To ensure accuracy in both quantitative and qualitative evaluation of liver tumors with DCEUS, sources of noise in clinical data must be identified and, if possible, removed. One of the major sources of such noise is respiratory motion. A new automatic respiratory gating (ARG) algorithm is presented and evaluated with clinical data. The results of the evaluation demonstrate the potential of the ARG algorithm for clinical use as a fast and easy-to-implement method for removing respiratory motion from DCEUS loops.

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

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

Acoustic characterization and pharmacokinetic analyses of new nanobubble ultrasound contrast agents

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

CREANUIS: a non-linear radiofrequency ultrasound image simulator

Nonlinear ultrasound methods are widely used in clinical applications for tissue or contrast harmonic imaging. Accurate non-linear imaging simulation tools are required in research studies for the development of new methods. However, in existing simulators, the possible inhomogeneity of the coefficient of non-linearity, which is generally observed in tissue and in particular when contrast agents are involved, has not yet been implemented. This article describes a new ultrasound simulator, called CREANUIS, devoted to the computation of B-mode images where both linear and non-linear propagation in media is considered, with a possible inhomogeneous coefficient of non-linearity. The resulting fundamental images, based on a spatially variant and non-linear point spread function, are in accordance with those obtained through the reference linear FieldII simulator, with computation time reduced by a factor of at least 1.8. Non-linear images of media exhibiting inhomogeneous coefficients of non-linearity are also provided. The simulation software can be freely downloaded from our website.

Echo-power estimation from log-compressed video data in dynamic contrast-enhanced ultrasound imaging

Ultrasound (US) scanners typically apply lossy, non-linear modifications to the US data for visualization purposes. The resulting images are then stored as compressed video data. Some system manufacturers provide dedicated software for quantification purposes to eliminate such processing distortions, at least partially. This is currently the recommended approach for quantitatively assessing changes in contrast-agent concentration from clinical data. However, the machine-specific access to US data and the limited set of analysis functionalities offered by each dedicated-software package make it difficult to perform comparable analyses with different US systems. The objective of this work was to establish if linearization of compressed video images obtained with an arbitrary US system can provide an alternative to dedicated-software analysis of machine-specific files for the estimation of echo-power. For this purpose, an Aplio 50 system (Toshiba Medical Systems, Tochigi, Japan), coupled with dedicated CHI-Q (Contrast Harmonic Imaging Quantification) software by Toshiba Medical Systems, was used. Results were compared with two approaches that apply algorithms to estimate relative echo-power from compressed video images: commercially available VueBox software by Bracco Suisse SA (Geneva, Switzerland) and in-laboratory software called PixPower. The echo-power estimated by CHI-Q analysis indicated a strong linear relationship versus agent concentration in vitro (R(2) ≥ 0.9996) for dynamic range (DR) settings of DR60 and DR80, with slopes between 9.22 and 9.57 dB/decade (p = 0.05). These values approach the theoretically predicted dependence of 10.0 dB/decade (equivalent to 3 dB for each concentration doubling). Echo-power estimations obtained from compressed video images with VueBox and PixPower also exhibited strong linear proportionality with concentration (R(2) ≥ 0.9996), with slopes between 9.30 and 9.68 dB/decade (p = 0.05). On an independent in vivo data set (N = 24), the difference in echo-power estimation between CHI-Q and each of the other two approaches was calculated after excluding regions that contain pixels affected by saturated or thresholded pixel values. The mean difference in estimates (expressed in decibels) was -0.25 dB between VueBox and CHI-Q (95% confidence interval: -0.75 to 0.26 dB) and -0.17 dB between PixPower and CHI-Q (95% confidence interval: -0.67 to 0.13 dB). To achieve linearization of data, one of the approaches (VueBox) requires calibration files provided by the software manufacturer for each machine type and setting. The other (PixPower) requires empirical correction of the imaging dynamic range based on ground truth data. These requirements could potentially be removed if US system manufacturers were willing to make relevant information on the applied processing publically available. Reliable echo-power estimation from linearized data would facilitate inclusion of different US systems in multicentric studies and more widespread implementation of emerging techniques for quantitative analysis of contrast ultrasound.

Nanobubble ultrasound contrast agents for enhanced delivery of thermal sensitizer to tumors undergoing radiofrequency ablation

PURPOSE

Pluronic has been shown to sensitize various tumor cell lines to chemotherapy and hyperthermia by altering the membrane fluidity, depleting ATP, and modulating the heat shock protein 70 expression. In our prior work, Pluronic was also used to formulate nanosized ultrasound contrast agents. In the current study we evaluate the use of these contrast agents as vehicles for image-guided delivery of Pluronic to improve outcomes of tumor radiofrequency (RF) ablation.

METHODS

Lipid-shelled Pluronic nanobubbles were prepared and examined for size distribution, zeta potential, stability, biodistribution, accumulation of nanobubbles in the tumor, and treatment efficacy. LS174-T xenograft tumor-bearing mice were used to evaluate tumor growth suppression and measure treatment efficacy after RF ablation.

RESULTS

The average diameter of Pluronic bubbles was 230 nm, and initial bubble echogenicity was 16 dB. In vitro, cells exposed to Pluronic nanobubbles exhibited low cytotoxicity in the absence of ultrasound, even if heat (43 ºC) was applied. When the cells were exposed to Pluronic nanobubbles, heat, and ultrasound; viability was significantly reduced. In vivo, tumors treated with ultrasound-modulated nanobubbles prior to RF ablation showed a significant reduction in growth compared to the RF alone (P<0.05).

CONCLUSION

Lipid and Pluronic-shelled, echogenic nanobubbles combined with ultrasound modulation can serve as an effective theranostic method for sensitization of tumors to RF ablation.

Hepatocellular carcinoma treated with sorafenib: early detection of treatment response and major adverse events by contrast-enhanced US

BACKGROUND & AIMS

Early prediction of tumour response and major adverse events (AEs), especially liver failure, in patients with hepatocellular carcinoma (HCC) is essential for maximizing the clinical benefits of sorafenib. To evaluate the usefulness of dynamic contrast-enhanced ultrasound (DCE-US) for the early prediction of tumour response and major AEs in HCC patients.

METHODS

Thirty-seven HCC patients were started on a reduced dosage of sorafenib, subsequently increased to the standard dosage. Tumour response at 1 month was assessed by CT using the Response Evaluation Criteria in Solid Tumors (RECIST). Major AEs were defined as grade 3 or higher. DCE-US was performed before treatment (day 0) and on days 7, 14 and 28. Changes in perfusion parameters in the tumour and liver parenchyma between day 0 and later time points were compared between treatment responders and nonresponders based on RECIST and between patients who experienced major AEs and those who did not. Tumour results were also compared with progression-free survival (PFS) and overall survival (OS).

RESULTS

Tumour perfusion parameters based on the area under the time-intensity curve (AUC) were statistically significant, with AUC during washin on day 14, the most relevant for tumour response (P = 0.0016) and AUC during washin on day 7, the most relevant for both PFS (P = 0.009) and OS (P = 0.037). A decrease in total AUC between days 0 and 7 in the liver parenchyma was strongly correlated with major AEs (P = 0.0002).

CONCLUSION

DCE-US may be useful for the early prediction of tumour response and major AEs in patients with HCC.