Ultrasound Imaging From Sparse RF Samples Using System Point Spread Functions
This publication appears in: IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control
Authors: C. Schretter, D. Blinder, A. Dooms, J. D'Hooge and P. Schelkens
Publication Year: 2017
Upcoming phased array 2D sensors will soon enable fast high-definition 3D ultrasound imaging. Currently, the com- munication of raw radiofrequency (RF) channel data from the probe to the computer for digital beamforming is a bottleneck. For reducing the amount of transferred data samples, this work investigates the design of an adapted sparse sampling technique for image reconstruction inspired by the compressed sensing framework. Echo responses from isolated points are generated using a physically-based simulation of ultrasound wave propagation through tissues. These point spread functions (PSF) form a dictionary of shift-variant bent waves which depend on the specific sound excitation and acquisition protocols. Speckled ultrasound images can be approximatively decomposed in this dictionary where sparsity is enforced at the system matrix design. The Moore-Penrose pseudo-inverse is precomputed and used at the reconstruction stage for fast minimum-norm recovery from non-uniform pseudo-random sampled raw RF data. Results on simulated and acquired phantoms demonstrate the benefits of optimized basis function design for high-quality B-mode image recovery from few RF channel data samples.