An Automated System for Full Angle Spatial Compounding in Ultrasound Breast Imaging

Full Angle Spatial Compound of ARFI images for breast cancer detection

Automated ultrasound breast imaging would overcome most of the limitations that precludes conventional hand-held echography to be an effective screening method for breast cancer diagnosis. If a three dimensional (3D) ultrasound dataset is acquired without manual intervention of the technician, repeatability and patient follow-up could be improved. Furthermore, depending on the system configuration, resolution and contrast could be enhanced with regard to conventional echography, improving lesion detectability and evaluation. Having multiple modalities is another major advantage of these automated systems, currently under development by several research groups. Because of their circular structure, some of them include through-transmission measurements that allow constructing speed of sound and attenuation maps, which adds complementary information to the conventional reflectivity B-Mode image. This work addresses the implementation of the Acoustic Radiation Force Impulse (ARFI) imaging technique in a Full Angle Spatial Compound (FASC) automated breast imaging system. It is of particular interest because of the high specificity of ARFI for breast cancer diagnosis, by representing tissue elasticity differences rather than acoustic reflectivity. First, the image formation process is analyzed and a compounding strategy is proposed for ARFI-FASC. Then, experimental results with a prototype system and two gelatin phantoms are presented: Phantom A with a hard inclusion in a soft background, and phantom B with three soft inclusions in a hard background and with three steel needles. It is demonstrated that the full angle composition of ARFI images improves image quality, enhancing Contrast to Noise Ratio (CNR) from 4.9 to 20.6 and 3.6 to 13.5 in phantoms A and B respectively. Furthermore, this CNR increase improved detectability of small structures (needles) with regard to images obtained from a single location, in which image texture masked their presence.

Imaging of sound speed using reflection ultrasound tomography

OBJECTIVES

The goal of this work was to obtain and evaluate measurements of tissue sound speed in the breast, particularly dense breasts, using backscatter ultrasound tomography.

METHODS

An automated volumetric breast ultrasound scanner was constructed for imaging the prone patient. A 5- to 7-MHz linear array transducer acquired 17,920 radiofrequency pulse echo A-lines from the breast, and a back-wall reflector rotated over 360° in 25 seconds. Sound speed images used reflector echoes that after preprocessing were uploaded into a graphics processing unit for filtered back-projection reconstruction. A velocimeter also was constructed to measure the sound speed and attenuation for comparison to scanner performance. Measurements were made using the following: (1) deionized water from 22°C to 90°C; (2) various fluids with sound speeds from 1240 to 1904 m/s; (3) acrylamide gel test objects with features from 1 to 15 mm in diameter; and (4) healthy volunteers.

RESULTS

The mean error ± SD between sound speed reference and image data was -0.48% ± 9.1%, and the error between reference and velocimeter measurements was -1.78% ± 6.50%. Sound speed image and velocimeter measurements showed a difference of 0.10% ± 4.04%. Temperature data showed a difference between theory and imaging performance of -0.28% ± 0.22%. Images of polyacrylamide test objects showed detectability of an approximately 1% sound speed difference in a 2.4-mm cylindrical inclusion with a contrast to noise ratio of 7.9 dB.

CONCLUSIONS

An automated breast scanner offers the potential to make consistent automated tomographic images of breast backscatter, sound speed, and attenuation, potentially improving diagnosis, particularly in dense breasts.

Stability analysis and breast tumor classification from 2D ARMA models of ultrasound images

Two-dimensional (2D) autoregressive moving average (ARMA) random fields have been proven to be accurate models of ultrasound breast images. However, the stability properties of these models have not been examined. In this paper, we investigate the stability of 2D ARMA models in ultrasound breast images, and use the estimated 2D ARMA coefficients as a basis for statistical inference using artificial neural networks. Specifically, we use the estimated 2D ARMA coefficients as inputs to a Multi layer perceptron (MLP) neural network to classify the ultrasound breast image into three regions: healthy tissue, benign tumor, and cancerous tumor. Our simulation results on various cancerous and benign ultrasound breast images illustrate the power of the proposed algorithm as attested by different learning algorithms and classification accuracy measures.