Simulation of ultrasound two-dimensional array transducers using a frequency domain model

Ultrasound imaging with two-dimensional ͑2D͒ arrays has garnered broad interest from scanner manufacturers and researchers for real time three-dimensional ͑3D͒ applications. Previously the authors described a frequency domain B-mode imaging model applicable for linear and phased array transducers. In this paper, the authors extend this model to incorporate 2D array transducers. Further approximations can be made based on the fact that the dimensions of the 2D array element are small. The model is compared with the widely used ultrasound simulation program FIELD II, which utilizes an approximate form of the time domain impulse response function. In a typical application, errors in simulated RF waveforms are less than 4% regardless of the steering angle for distances greater than 2 cm, yet computation times are on the order of 1/35 of those incurred using FIELD II. The 2D model takes into account the effects of frequency-dependent attenuation, backscattering, and dispersion. Modern beam-forming techniques such as apodization, dynamic aperture, dynamic receive focusing, and 3D beam steering can also be simulated

Cross-imaging system comparison of backscatter coefficient estimates from a tissue-mimicking material

A key step toward implementing quantitative ultrasound techniques in a clinical setting is demonstrating that parameters such as the ultrasonic backscatter coefficient (BSC) can be accurately estimated independent of the clinical imaging system used. In previous studies, agreement in BSC estimates for well characterized phantoms was demonstrated across different laboratory systems. The goal of this study was to compare the BSC estimates of a tissue mimicking sample measured using four clinical scanners, each providing RF echo data in the 1-15 MHz frequency range. The sample was previously described and characterized with single-element transducer systems. Using a reference phantom for analysis, excellent quantitative agreement was observed across the four array-based imaging systems for BSC estimates. Additionally, the estimates from data acquired with the clinical systems agreed with theoretical predictions and with estimates from laboratory measurements using single-element transducers

Essentials of ultrasound physics /

Includes bibliographical references and index

In vivo classification of breast masses using features derived from axial-strain and axial-shear images

Breast cancer is currently the second leading cause of cancer deaths in women. Early detection and accurate classification of suspicious masses as benign or malignant is important for arriving at an appropriate treatment plan. In this article, we present classification results for features extracted from ultrasound-based, axial-strain and axial-shear images of breast masses. The breastmass stiffness contrast, size ratio, and a normalized axial-shear strain area feature are evaluated for the classification of in vivo breast masses using a leave-one-out classifier. Radiofrequency echo data from 123 patients were acquired using Siemens Antares or Elegra clinical ultrasound systems during freehand palpation. Data from four different institutions were analyzed. Axial displacements and strains were estimated using a multilevel, pyramid-based two-dimensional cross-correlation algorithm, with final processing block dimensions of 0.385 mm × 0.507 mm (three A-lines). Since mass boundaries on B-mode images for 21 patients could not be delineated (isoechoic), the combined feature analysis was only performed for 102 patients. Results from receiver operating characteristic (ROC) demonstrate that the area under the curve was 0.90, 0.84, and 0.52 for the normalized axial-shear strain, size ratio, and stiffness contrast, respectively. When these three features were combined using a leave-one-out classifier and support vector machine approach, the overall area under the curve improved to 0.93. © Author(s) 2012

In Vivo Classification of Breast Masses Using Features Derived From Axial-Strain and Axial-Shear Images

Breast cancer is currently the second leading cause of cancer deaths in women. Early detection and accurate classification of suspicious masses as benign or malignant is important for arriving at an appropriate treatment plan. In this article, we present classification results for features extracted from ultrasound-based, axial-strain and axial-shear images of breast masses. The breast-mass stiffness contrast, size ratio, and a normalized axial-shear strain area feature are evaluated for the classification of in vivo breast masses using a leave-one-out classifier. Radiofrequency echo data from 123 patients were acquired using Siemens Antares or Elegra clinical ultrasound systems during freehand palpation. Data from four different institutions were analyzed. Axial displacements and strains were estimated using a multilevel, pyramid-based two-dimensional cross-correlation algorithm, with final processing block dimensions of 0.385 mm × 0.507 mm (three A-lines). Since mass boundaries on B-mode images for 21 patients could not be delineated (isoechoic), the combined feature analysis was only performed for 102 patients. Results from receiver operating characteristic (ROC) demonstrate that the area under the curve was 0.90, 0.84, and 0.52 for the normalized axial-shear strain, size ratio, and stiffness contrast, respectively. When these three features were combined using a leave-one-out classifier and support vector machine approach, the overall area under the curve improved to 0.93