Image processing and data acquisition optimization for Acoustic Radiation Force Impulse imaging of in vivo breast masses
Acoustic Radiation Force Impulse (ARFI) imaging utilizes brief, high-energy acoustic pulses to excite tissue and ultrasonic correlation based tracking methods to monitor the resulting tissue displacement, which reflects the relative mechanical properties of tissue (i.e.. suffer tissue displaces less). ARFI image contrast is optimized utilizing tightly focused radiation force excitations at multiple axial and lateral locations throughout a 2D field of view. In an ongoing, IRB approved, clinical study, suspicious breast lesions are interrogated in vivo via multi-focal-zone ARFI prior to undergoing core biopsy. A Siemens SONOLINE Antares (TM) scanner and VF10-5 probe were configured to acquire ARFI data from multiple focal-zones and lateral locations. Data was acquired in real-time, and processed off-line. Processing included: filtering, parametric data analysis, normalization and combination of the multiple focal-zone data, and automatic edge detection. ARFI sequences were designed with varying pushing pulse frequencies and intensities. Contrast to noise ratio was evaluated in a tissue mimicking phantom for lesions at different depths using the different pushing pulse sequences. For shallower lesions (depth=10mm), CNR was higher than for deeper lesions, and did not vary appreciably for the different push sequences. For deeper lesions (depth=20mm), CNR increased with increasing push pulse intensity and decreasing push pulse frequency. With the pushing pulse transmit intensity calibrated (in a homogeneous phantom) to achieve uniform displacement at all axial depths, in vivo results yielded poor SNR at depth and did not achieve overall uniform displacement. In vivo, image quality improved with increasing push pulse intensity. To date, 27 masses have been interrogated using multi-focal-zone ARFI and overall good structural agreement exists between B-mode and ARFI images. Normalization and blending facilitate image generation from ARFI interrogation using different intensities at different focal depths.
