A dictionary learning-based fast imaging method for ultrasound elastography

Abstract

Ultrasound elastography is an imaging modality that computes the elasticity of tissue through measuring shear waves from a mechanical excitation using pulse-echo ultrasound. To better measure shear waves and reduce acquisition time, elastography would benefit from a higher framerate, which is limited by conventional focused line-by-line acquisition. This paper proposes a dictionary learning-based framework that increases the framerate of steady state elastography. The method uses patches extracted from images with higher scanline density to train a dictionary, and uses this dictionary to interpolate images with lower scanline density collected at a faster framerate. Experiments on a tissue mimicking phantom showed when the framerate is increased 8 times, the reconstructed image using the proposed method achieved a 17.6 dB Peak Signal-to-Noise Ratio. The method was also implemented on a steady state elastography system, where elasticity measurements similar to conventional methods were obtained with a shorter total acquisition time.