Contrast-enhanced Ultrasound Image Sequences Research Articles

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.

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.

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

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. 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. 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). 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. Thiseasy-to-operatemethod can potentially be used in both animal studies and clinical applications.

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.

A Temporal and Spatial Analysis Approach to Automated Segmentation of Microbubble Signals in Contrast-Enhanced Ultrasound Images: Application to Quantification of Active Vascular Density in Human Lower Limbs

Contrast-enhanced ultrasound (CEUS) using microbubble contrast agents has shown great promise in visualising and quantifying active vascular density. Most existing approaches for vascular density quantification using CEUS are calculated based on image intensity and are susceptible to confounding factors and imaging artefact. Poor reproducibility is a key challenge to clinical translation. In this study, a new automated temporal and spatial signal analysis approach is developed for reproducible microbubble segmentation and quantification of contrast enhancement in human lower limbs. The approach is evaluated in vitro on phantoms and in vivo in lower limbs of healthy volunteers before and after physical exercise. In this approach, vascular density is quantified based on the relative areas microbubbles occupy instead of their image intensity. Temporal features of the CEUS image sequences are used to identify pixels that contain microbubble signals. A microbubble track density (MTD) measure, the ratio of the segmented microbubble area to the whole tissue area, is calculated as a surrogate for active capillary density. In vitro results reveal a good correlation (r2 = 0.89) between the calculated MTD measure and the known bubble concentration. For in vivo results, a significant increase (129% in average) in the MTD measure is found in lower limbs of healthy volunteers after exercise, with excellent repeatability over a series of days (intra-class correlation coefficient = 0.96). This compares to the existing state-of-the-art approach of destruction and replenishment analysis on the same patients (intra-class correlation coefficient ≤0.78). The proposed new approach shows great potential as an accurate and highly reproducible clinical tool for quantification of active vascular density.

Computer-aided quantification of contrast agent spatial distribution within atherosclerotic plaque in contrast-enhanced ultrasound image sequences

Recent studies have revealed that the contrast-enhanced ultrasound (CEUS) correlates to the presence and degree of intraplaque neovascularization as determined histologically. However, most studies used a qualitative system to visually grade CEUS images. A computer-aided method is proposed for objective and convenient quantification of contrast agent spatial distribution within plaques in CEUS image sequences. It consists of three algorithms including cardiac cycle retrieval and sub-sequence selection, temporal mean image segmentation, and texture feature extraction. The first algorithm automatically retrieves and selects cardiac cycles from CEUS frames without electrocardiogram gating. The second is composed of three steps, i.e., temporal averaging, interactive plaque delineation, and automatic neovascularization segmentation. The third extracts eight texture features from the grayscale temporal mean images and the binary segmented images. The capability of the quantitative features in discriminating between qualitative grades is examined via the t-tests, analysis of variance (ANOVA), Fisher criterion of inter-intra class variance ratio and logistic regression classification with leave-one-out cross-validation. Experimental results on 33 carotid plaques demonstrated that the optimal feature, namely the combined area ratio, exhibited significant difference among three qualitative grades (P<0.001, ANOVA). When distinguishing between low-grade and high-grade plaques, the features improved the area under the receiver operating characteristic curve, sensitivity and specificity by 36.4%, 16.7%, and 11.1%, respectively, contrasted with a classic feature, the traditional area ratio. These results demonstrate the usefulness and advantage of the proposed method in quantifying the spatial distribution of contrast agents in CEUS.

Respiratory motion correction in free‐breathing ultrasound image sequence for quantification of hepatic perfusion

Evaluation of regional hepatic perfusion by contrast-enhanced ultrasound (CEUS) is helpful to the differential diagnosis of focal liver lesions (FLLs). Because most patients cannot hold their breath for the duration of the entire hepatic perfusion scan, ultrasonographists tend to employ free-breathing acquisition method. A new strategy using a combination of template matching and frame selection is proposed to correct the respiratory motion and improve the accuracy of the quantification evaluation of the perfusion. Considering that most commercial ultrasound machines can provide a dual display mode for simultaneously visualizing contrast and tissue images, the registration of the contrast images was achieved by the registration of the corresponding tissue images. After the template was located, the rough search space was estimated using a priori knowledge of the tumor location in the free-breathing image sequence. Then, a simple double-selection method was proposed to select the similar images from a large number of successive matched images via global and local threshold settings. In this method, alpha and m were the offset of the global threshold and the time interval for setting local sampling range, respectively. These two parameters were also investigated. The strategy was tested on ten liver CEUS acquisitions with a handle probe by using sum of absolute differences (SAD) metric. The visual evaluation for 2D image sequences and the extracted time-intensity curves from the regions of interest were performed. Simpler curve descriptors of the motion-uncorrected and motion-corrected image sequences were calculated on a pixel-by-pixel basis and evaluated as parametric perfusion maps. The quality of these parametric images was compared, in terms of both the accuracy and spatial resolution. For the corrected and uncorrected sequences, their mean deviation values (mDVs) and mean quality-of-fits (mQOFs) were measured. When alpha and m were both set to 0.5, 9.20 +/- 3.22% of the total number of frames were selected. After the motion correction, the mDVs of all the image sequences decreased from 21.69 +/- 2.80 to 13.78 +/- 2.68. The mQOFs of all the corrected sequences increased by an average of 15.32 +/- 5.13%. The quality of curve fitting and the corresponding parametric imaging computed on motion-corrected sequences were improved. On the average, the motion correction of a sequence containing about 100 frames was performed in approximately 3 min using MATLAB, whereas a completely manual approach requires approximately 10 min. The image-based strategy independent of the tumor size can quickly correct respiratory motion in CEUS image sequences. Simple manual operation is only needed, such as the selection of the template image and search space. It is user-friendly and suitable for most clinical cases affected by adverse factors of sampling. Due to the merit of the saving time, this strategy can be widely applied to clinical practice, and the diagnostic efficiency of FLLs will be improved. Moreover, the correction strategy is a key preprocessing step toward local quantification of hepatic perfusion studies.