A hybrid CPU-GPGPU approach for real-time elastography

Ultrasound elastography is becoming a widely available clinical imaging tool. In recent years, several real- time elastography algorithms have been proposed; however, most of these algorithms achieve real-time frame rates through compromises in elastographic image quality. Cross-correlation- based elastographic techniques are known to provide high- quality elastographic estimates, but they are computationally intense and usually not suitable for real-time clinical applications. Recently, the use of massively parallel general purpose graphics processing units (GPGPUs) for accelerating computationally intense operations in biomedical applications has received great interest. In this study, we investigate the use of the GPGPU to speed up generation of cross-correlation-based elastograms and achieve real-time frame rates while preserving elastographic image quality. We propose and statistically analyze performance of a new hybrid model of computation suitable for elastography applications in which sequential code is executed on the CPU and parallel code is executed on the GPGPU. Our results indicate that the proposed hybrid approach yields optimal results and adequately addresses the trade-off between speed and quality.