Fast non-blind deconvolution based on 2D point spread function database for real-time ultrasound imaging

Abstract

In the ultrasound medical imaging system, blurring which occurs after passing through ultrasound scanner system, represents Point Spread Function (PSF) that describes the response of the ultrasound imaging system to a point source distribution. So, de-blurring can be achieved by de-convolving the images with an estimated of PSF. However, it is hard to attain an accurate estimation of PSF due to the unknown properties of the tissues of the human body through the ultrasound signal propagates. In addition to, the complexity is very high in order to estimate point spread function and de-convolve the ultrasound image with estimated PSF for real-time implementation of ultrasound imaging. Therefore, conventional methods of ultrasound image restoration are based on a simple 1D PSF estimation [8] that axial direction only by restoring the performance improvement is not in the direction of Lateral. And, in case of 2D PSF estimation, PSF estimation and restoration of the high complexity is not being widely used. In this paper, we proposed new method for selection of the 2D PSF (estimated PSF of the average speed sound and depth) simultaneously with performing fast non-blind 2D de-convolution in the ultrasound imaging system. Our algorithm works on the beam-formed uncompressed radio-frequency data, with pre-measured and estimated 2D PSFs database from actual probe used. In the 2d PSF database, there are pre-measured and estimated 2D PSFs that classified the each different depth (about 5 different depths) and speed of sound (about 1450 or 1540m/s). Using a minimum variance and simple Weiner filter method, we present a novel way to select the optimal 2D PSF in pre-measured and estimated 2D PSFs database that acquired from the actual transducer being used. For de-convolution part with the chosen PSF, we focused on the low complexity issue. So, we are using the Weiner Filter and fast de-convolution technique using hyper-Laplacian priors [11], [12] which is several orders of magnitude faster than existing techniques that use hyper-Laplacian priors. Then, in order to prevent discontinuities between the differently restored each depth image regions, we use the piecewise linear interpolation on overlapping regions. We have tested our algorithm with vera-sonic system and commercial ultrasound scanner (Philips C4-2), in known speed of sound phantoms and unknown speeds in vivo scans. We have applied a non-blind de-convolution with 2D PSFs database for ultrasound imaging system. Using the real PSF from actual transducer being used, our algorithm produces a better restoration of ultrasound image than de-convolution by simulated PSF, and has low complexity for real-time ultrasound imaging. This method is robust and easy to implement. This method may be a realistic candidate for real-time implementation.

End-to-End Algorithm Research in PACT—From Signal Processing to Reconstruction Solution to Image Processing

“…Non-blind deconvolution [102][103][104] refers to the process of estimating a clear image from a blurred image using pre-measured PSF with the help of auxiliary methods.…”

Enhanced axial and lateral resolution using stabilized pulses

“…On the other hand, nonblind deconvolution approaches utilize more prior information and are thus generally more specific for the imaging system and have better performance. 23,[35][36][37][38] Within the framework of nonblind deconvolution, a specific approach to superresolution imaging for pulse-echo systems was previously described for stabilized asymmetric pulses. 38 Our more recent paper treated the lateral deconvolution for symmetric stabilized pulses.…”