3-D Super-Resolution Ultrasound Imaging for Monitoring Early Changes in Breast Cancer after Treatment with a Vascular-Disrupting Agent.

The purpose of this research project was to evaluate the use of 3-dimensional (3-D) super-resolution ultrasound (SR-US) imaging to assess any early changes in breast cancer after treatment with a vascular-disrupting agent (VDA). A Vevo 3100 ultrasound system (FUJIFILM VisualSonics Inc) equipped with an MX 201 transducer was used for image acquisition. A total of 2.5 × 107 microbubbles (MBs) were injected into the tail vein of anesthetized breast cancer-bearing mice using repeat bolus injections every 5 min. A total of 10 stacks of ultrasound images were collected as the transducer was mechanically moved across the tumor at 0.6 mm intervals yielding a 6-mm thick volume. At each tumor location, a stack contained 1 × 104 frames of ultrasound data that were acquired at 463 frames/sec and stored as in-phase/quadrature (IQ) format. After motion correction, each temporal stack of ultrasound images was processed separately for clutter signal removal, which was followed by MB localization and enumeration before generation of the final SR-US image. After reconstruction of the 3-D SR-US volume dataset, the tumor microvasculature was enhanced using a multiscale vessel enhancement filter. Vessels from the resultant microvascular network were then segmented using an adaptive thresholding method. Finally, mean microvascular density (MVD) measurements from each tumor volume were computed as a summarizing statistic. While no differences were found between baseline SR-US image-derived measures of MVD (p = 0.76), these same measurements were significantly lower at 24 h after VDA treatment (p < 0.001). Overall, 3-D SR-US imaging detected early tumor changes following treatment with a vascular-targeted drug.

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 300 µ m 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 150 µ m 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.

Tumor Vascular Networks Depicted in Contrast-Enhanced Ultrasound Images as a Predictor for Transarterial Chemoembolization Treatment Response.

Hepatocellular carcinoma (HCC) is prevalent worldwide. Among the various therapeutic options, transarterial chemoembolization (TACE) can be applied to the tumor vascular network by restricting the nutrients and oxygen supply to the tumor. Unique morphologic properties of this network may provide information predictive of future therapeutic responses, which would be significant for decision making during treatment planning. The extraction of morphologic features from the tumor vascular network depicted in abdominal contrast-enhanced ultrasound (CEUS) images faces several challenges, such as organ motion, limited resolution caused by clutter signal and segmentation of the vascular structures at multiple scales. In this study, we present an image processing and analysis approach for the prediction of HCC response to TACE treatment using clinical CEUS images and known pathologic responses. This method focuses on addressing the challenges of CEUS by incorporating a two-stage motion correction strategy, clutter signal removal, vessel enhancement at multiple scales and machine learning for predictive modeling. The morphologic features, namely, number of vessels (NV), number of bifurcations (NB), vessel to tissue ratio (VR), mean vessel length, tortuosity and diameter, from tumor architecture were quantified from CEUS images of 36 HCC patients before TACE treatment. Our analysis revealed that NV, NB and VR are the dominant features for the prediction of long-term TACE response. The model had an accuracy of 86% with a sensitivity and specificity of 89% and 82%, respectively. Reliable prediction of the TACE therapy response using CEUS-derived image features may help to provide personalized therapy planning, which will ultimately improve patient outcomes.

Three-dimensional evaluation of microvascular networks using contrast-enhanced ultrasound and microbubble tracking.

Evaluating tumor microvascular networks with use of contrast-enhanced ultrasound (CEUS) imaging and one-dimensional (1D) linear array transducers have inherit limitations as tumors exist in volume space. The use of a mechanical sweep allows users to overcome this limitation. To that end, we have developed a new method by which a 1D linear array transducer can be mechanically scanned over a region-of-interest to capture a volume of data allowing for the evaluation of microvasculature structures in 3D space. After intravascular injection of a microbubble (MB) contrast agent into a developing chicken embryo, a sequence of CEUS images were acquired using a Vevo 3100 scanner (VisualSonics Inc) and taken at multiple tissue cross-sections. The CEUS images were processed with a singular value filter (SVF) to help remove any clutter signal. MB localization was performed, and frame-to-frame MB movement was analyzed to produce spatial maps depicting blood flow and velocity at each tissue cross-section. Reconstruction of all images allowed visualization of microvascular networks and blood velocity distribution in volume space.

Three-dimensional super-resolution ultrasound imaging of chicken embryos - A validation framework for analysis of microvascular morphology.

The purpose of this present study was to improve the quantification of microvascular networks depicted in three-dimensional (3D) super-resolution ultrasound (SR-US) images and compare results with matched brightfield microscopy and B-mode ultrasound (US) images. Standard contrast-enhanced US (CEUS) images were collected using a high-frequency US scanner (Vevo 3100, FUJIFILM VisualSonics Inc) equipped with an MX250 linear array transducer. Using a developing chicken embryo as our model system, US imaging was performed after administration of a custom microbubble (MB) contrast agent. Guided by stereo microscopy, MBs were introduced into a perfused blood vessel by microinjection with a glass capillary needle. Volume data was collected by mechanically scanning the US transducer throughout a tissue volume-of-interest (VOI) in 90 µm step increments. CEUS images were collected at each increment and stored as in-phase/quadrature (IQ) data (N = 2000 at 152 frames per sec). SR-US images were created for each cross-sectional plane using established data processing methods, and all were then used to form a final 3D volume for subsequent quantification of morphological features. Vessel diameter quantifications from 3D SR-US data exhibited an average error of 1.9% when compared with microscopy images, whereas measures from B-mode US images had an average error of 75.3%. Overall, 3D SR-US images clearly depicted the microvascular network of the developing chicken embryo and measurements of microvascular morphology achieved better accuracy compared to traditional B-mode US.

Contrast-enhanced ultrasound imaging using pulse inversion spectral deconvolution.

A contrast-enhanced ultrasound (CEUS) imaging approach, termed pulse inversion spectral deconvolution (PISD), is introduced. The approach uses two Gaussian-weighted Hermite polynomials to form two inverted pulse sequences. The two inversed pulses are then used to filter ultrasound (US) backscattered data and discrimination of the linear and nonlinear signal components. A research US scanner equipped with a linear array transducer was used for data acquisition. The receive data from all channels are shaped using plane wave imaging beamforming with angular compounding (from one to nine angles). In vitro data was collected with a tissue mimicking flow phantom perfused with an US contrast agent using PISD and traditional nonlinear (NLI) US imaging as comparison. The role of imaging frequency (between 4.5 and 6.25 MHz) and mechanical index (from 0.1 to 0.3) were evaluated. Preliminary in vivo data was collected in the hindlimb of three healthy mice. Preliminary experimental findings indicate that the PISD contrast-to-tissue ratio was improved nearly ten times compared to the NLI US imaging approach. Also, the spatial resolution was improved due to the effect of deconvolution and spatial angular compounding. Overall, PISD is a promising postprocessing technique for real-time CEUS imaging.

Improved quantitative contrast-enhanced ultrasound imaging of hepatocellular carcinoma response to transarterial chemoembolization.

The purpose of this research project was to improve the quantification of microvascular networks depicted in contrast-enhanced ultrasound (CEUS) images of human hepatocellular carcinoma (HCC). Due to limited anatomical information in CEUS images, grayscale B-mode ultrasound (US) data is preferred when estimating tissue motion. Transformation functions derived from the B-mode data are one solution for registering a dynamic sequence of CEUS images. Microvessel density (MVD) can then be calculated from both the original and motion corrected CEUS images as the ratio of the number of contrast-enhanced image pixels with a value greater than zero to the number of pixels of the entire tumor space. Using US images of HCC before and after treatment with transarterial chemoembolization, results revealed that affine and non-rigid motion correction improves visualization and quantitative analysis of clinical data. Using the correlation coefficient (CC) between CEUS frames as metric of tissue motion, our motion correction strategy produced a 20% increase in the average CC from motion corrected frames compared to the data before correction (p < 0.001). Furthermore, enhanced visualization of microvascular networks in the treated liver tumor space may improve determination of treatment efficacy and need for any repeat procedures.

Contrast-enhanced ultrasound imaging of acute changes in pancreatic cancer following targeted hyaluronan treatment.

The purpose of this study was to monitor acute changes in pancreatic tumor perfusion with contrast-enhanced ultrasound (CEUS) imaging following targeted hyaluronan (HA) treatment. Intratumoral accumulation of HA is one of contributing factors that can lead to an increased tumor interstitial pressure (TIP). These elevated TIP levels can hinder delivery of chemotherapeutic drugs and cause treatment failure. For this study, pancreatic cancer-bearing mice were imaged at baseline and again at 2 h after intravenous administration of physiological saline (control group) or PEGPH20, which targets HA (therapy group). CEUS data were collected for 5 min and the temporal sequence was first analyzed using a singular value filter (SVF) to remove any background clutter signal. Given the time history of contrast agent flow, a tumor perfusion parametric analysis was performed. A series of morphological image operations was applied to quantify structural features of the tumor angiogenic network including vessel count, density, length, diameter, tortuosity, and branching points. After imaging, animals were euthanized, and tumors excised for histological processing. Acute microvascular changes were found at 2 h after drug administration as confirmed by CEUS imaging. Further, histologic analysis of tumor sections revealed lower HA accumulation in the therapy group animals. Overall, these findings suggest that CEUS imaging of acute changes in tumor perfusion may help identify an optimal window whereby follow-up chemotherapeutic drug dosing would be more effective.

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.