Real Time Deconvolution of In-Vivo Ultrasound Images

The axial resolution in medical ultrasound is directly linked to the emitted ultrasound frequency, which, due to tissue attenuation, is selected based on the depth of scanning. The resolution is determined by the transducers impulse response, which limits the attainable resolution to be between one and two wavelengths. This can be improved by deconvolution, which increase the bandwidth and equalizes the phase to increase resolution under the constraint of the electronic noise in the received signal. A fixed interval Kalman filter based deconvolution routine written in C is employed. It uses a state based model for the ultrasound pulse and can include a depth varying pulse and spatially varying signal-to-noise ratio. An autoregressive moving average (ARMA) model of orders 8 and 9 is used for the pulse, and the ARMA parameters are determined as a function of depth using a minimum variance algorithm using averaging over several RF lines. In vivo data from a 3 MHz mechanically rotating probe is used and the received signal is sampled at 20 MHz and 12 bits. In-vivo data acquired from a 16th week old fetus is used along with a scan from the liver and right kidney of a 27 years old male. The axial resolution has been determined from the in-vivo liver image using the auto-covariance function. From the envelope of the estimated pulse the axial resolution at Full-Width-Half-Max is 0.581 mm corresponding to 1.13 lambda at 3 MHz. The algorithm increases the resolution to 0.116 mm or 0.227 lambda corresponding to a factor of 5.1. The basic pulse can be estimated in roughly 0.176 seconds on a single CPU core on an Intel i5 CPU running at 1.8 GHz. An in-vivo image consisting of 100 lines of 1600 samples can be processed in roughly 0.1 seconds making it possible to perform real-time deconvolution on ultrasound data by using dual or quad core CPUs for frame-rates of 20-40 Hz.