Acoustic Radiation Force Impulse Data Research Articles

Acoustic Radiation Force Impulse and Conventional Ultrasound in the Prediction of Cirrhosis Complicating Fatty Liver: Does Body Mass Index Independently Alter the Results?

We investigated whether ultrasound (US) could quantify steatosis and fibrosis in non-alcoholic fatty liver disease (NAFLD). Estimates of fat by gray-scale, hepatorenal index (HRI) and fibrosis by acoustic radiation force impulse (ARFI) were made using the interquartile range (IQR)/median for ARFI quality. Biopsy was the gold standard. US fat assessment correlated with histologic grade and predicted steatosis. HRI predicted steatosis but did not improve accuracy. ARFI of good quality was highly sensitive toward severe fibrosis. The median ARFI value depended linearly on body mass index (BMI). Poor quality ARFI data had higher histologic steatosis, leading to higher mean steatosis grades in rejected data (p = 0.018). The ARFI quality cut with IQR/median >0.15 or >0.3 excluded many more patients with severe steatosis versus normal, influenced by increasing BMI. By combining the baseline US with ARFI, patients can be concurrently diagnosed for steatosis and fibrosis, two of the key pathologies of NAFLD and non-alcoholic steatohepatitis (NASH). However, severe steatosis and high BMI may falsely alter ARFI results.

Evaluation of Liver Tumors Using Acoustic Radiation Force Impulse Elastography and Correlation With Histologic Data

Acoustic radiation force impulse (ARFI) technology represents an innovative method for the quantification of tissue elasticity. The aims of this study were to evaluate elasticity by ARFI in both liver tumors and background liver tissue and to compare ARFI measurements with histologic data in liver tumors and background liver. Seventy-nine tumors were prospectively studied: 43 benign and 36 malignant. Acoustic radiation force impulse measurements for each tumor type were expressed as mean ± standard deviation for both liver tumors and background liver; ARFI data were also correlated with histologic data. For liver tumors, the mean stiffness values were 1.90 ± 0.86 m/s for hepatocellular adenoma (n = 9), 2.14 ± 0.49 m/s for hemangioma (n = 15), 3.14 ± 0.63 m/s for focal nodular hyperplasia (n = 19), 2.4 ± 1.01 m/s for hepatocellular carcinoma (n = 24), and 3.0 ± 1.36 m/s for metastasis (n = 12). Important variations were observed within each tumor type or within a single tumor. These variations could have been due to necrosis, hemorrhage, or colloid. There was no statistically significant difference between the benign and malignant groups. Regarding background liver, it was possible to observe pathologic abnormalities in histologic analyses or liver function tests to explain the ARFI data. The degree of fibrosis was not the only determinant of liver stiffness in background liver; other factors such as portal embolization, sinusoidal obstruction syndrome caused by chemotherapy, and cholestasis, also could have interfered. Acoustic radiation force impulse elastography could not allow differentiation between benign and malignant tumors. This study provides a better understanding of the correlation between ARFI and histologic data for both tumors and background liver.

Acoustic Radiation Force Impulse Imaging of Human Prostates: Initial In Vivo Demonstration

Reliably detecting prostate cancer (PCa) has been a challenge for current imaging modalities. Acoustic radiation force impulse (ARFI) imaging is an elasticity imaging method that uses remotely generated, focused acoustic beams to probe tissue stiffness. A previous study on excised human prostates demonstrated ARFI images portray various prostatic structures and has the potential to guide prostate needle biopsy with improved sampling accuracy. The goal of this study is to demonstrate the feasibility of ARFI imaging to portray internal structures and PCa in the human prostate in vivo. Custom ARFI imaging sequences were designed and implemented using a modified Siemens Antares ™ scanner with a three-dimensional (3-D) wobbler, end-firing, trans-cavity transducer, EV9F4. Nineteen patients were consented and imaged immediately preceding surgical prostatectomy. Pathologies and anatomic structures were identified in histologic slides by a pathologist blinded to ARFI data and were then registered with structures found in ARFI images. The results demonstrated that when PCa is visible, it generally appears as bilaterally asymmetric stiff structures; benign prostatic hyperplasia (BPH) appears heterogeneous with a nodular texture; the verumontanum and ejaculatory ducts appears softer compared with surrounding tissue, which form a unique ‘V’ shape; and the boundary of the transitional zone (TZ) forms a stiff rim separating the TZ from the peripheral zone (PZ). These characteristic appearances of prostatic structures are consistent with those found in our previous study of prostate ARFI imaging on excised human prostates. Compared with the matched B-mode images, ARFI images, in general, portray prostate structures with higher contrast. With the end-firing transducer used for this study, ARFI depth penetration was limited to 22 mm. Image contrast and resolution were decreased as compared with the previous ex vivo study due to the small transducer aperture. Even with these limitations, this study suggests ARFI imaging holds promise for guidance of targeted prostate needle biopsy and focal therapy, as well as aiding assessment of changes during watchful waiting/active surveillance.

GPU-based real-time small displacement estimation with ultrasound

General purpose computing on graphics processing units (GPUs) has been previously shown to speed up computationally intensive data processing and image reconstruction algorithms for computed tomography (CT), magnetic resonance (MR), and ultrasound images. Although some algorithms in ultrasound have been converted to GPU processing, many investigative ultrasound research systems still use serial processing on a single CPU. One such ultrasound modality is acoustic radiation force impulse (ARFI) imaging, which investigates the mechanical properties of soft tissue. Traditionally, the raw data are processed offline to estimate the displacement of the tissue after the application of radiation force. It is highly advantageous to process the data in real-time to assess their quality and make modifications during a study. In this paper, we present algorithms for efficient GPU parallel processing of two widely used tools in ultrasound: cubic spline interpolation and Loupas' two-dimensional autocorrelator for displacement estimation. It is shown that a commercially available graphics card can be used for these computations, achieving speed increases up to 40x compared with single CPU processing. Thus, we conclude that the GPU-based data processing approach facilitates real-time (i.e., <1 second) display of ARFI data and is a promising approach for ultrasonic research systems.

Robust Principal Component Analysis and Clustering Methods for Automated Classification of Tissue Response to ARFI Excitation

We introduce a new method for automatic classification of acoustic radiation force impulse (ARFI) displacement profiles using what have been termed “robust” methods for principal component analysis (PCA) and clustering. Unlike classical approaches, the robust methods are less sensitive to high variance outlier profiles and require no a priori information regarding expected tissue response to ARFI excitation. We first validate our methods using synthetic data with additive noise and/or outlier curves. Second, the robust techniques are applied to classifying ARFI displacement profiles acquired in an atherosclerotic familial hypercholesterolemic (FH) pig iliac artery in vivo. The in-vivo classification results are compared with parametric ARFI images showing peak induced displacement and time to 67% recovery and to spatially correlated immunohistochemistry. Our results support that robust techniques outperform conventional PCA and clustering approaches to classification when ARFI data are inclusive of low to relatively high noise levels (up to 5 dB average signal-to-noise [SNR] to amplitude) but no outliers: for example, 99.53% correct for robust techniques vs. 97.75% correct for the classical approach. The robust techniques also perform better than conventional approaches when ARFI data are inclusive of moderately high noise levels (10 dB average SNR to amplitude) in addition to a high concentration of outlier displacement profiles (10% outlier content): for example, 99.87% correct for robust techniques vs. 33.33% correct for the classical approach. This work suggests that automatic identification of tissue structures exhibiting similar displacement responses to ARFI excitation is possible, even in the context of outlier profiles. Moreover, this work represents an important first step toward automatic correlation of ARFI data to spatially matched immunohistochemistry. (E-mail: fwm5f@virginia.edu)

Acoustic radiation force impulse imaging of in vivo vastus medialis muscle under varying isometric load.

Acoustic Radiation Force Impulse (ARFI) imaging is a method for characterizing local variations in tissue mechanical properties. In this method, a single ultrasonic transducer array is used to both apply temporally short localized radiation forces within tissue and to track the resulting displacements through time. Images of tissue displacement immediately after force cessation, maximum tissue displacement, the time it takes for the tissue to reach its maximum displacement, and the recovery time constant of the tissue are generated from the ARFI data sets. The information in each of these images demonstrates good agreement with matched B-mode images. The study presented here was designed to evaluate the relationship between changes in these ARFI parameters with known tissue mechanical properties in vivo. Utilizing a modified Siemens Elegra scanner with a 75L40 transducer array, ARFI images of vastus medialis muscle were generated in three of the authors under four levels of activation (0, 5.7, 14.5, and 23.3 N-m). Four ARFI datasets were acquired for each loading condition. The observed trends were that displacement magnitude, the time it took for the tissue to reach its maximum displacement, and recovery time constant decreased with increasing load (i.e., increasing muscle stiffness). Significant differences were observed between load levels and subjects for all parameters (p<0.01). The results indicate that ARFI imaging may be capable of quantifying tissue stiffness in real-time measurements, although further investigation is required.