Ultrasonic multipath and beamforming clutter reduction: a chirp model approach

Abstract

In vivo ultrasonic imaging with transducer arrays suffers from image degradation due to beamforming limitations, which includes diffraction limited beamforming as well as beamforming degradation due to tissue inhomogeneity. Additionally, based on recent studies, multipath scattering also causes significant image degradation. To reduce degradation from both sources, we propose a model-based, signal decomposition scheme. The proposed algorithm identifies spatial frequency signatures to decompose received wavefronts into their most significant scattering sources. Scattering sources originating from a region of interest are used to reconstruct decluttered wavefronts, which are beamformed into decluttered radio frequency (RF) scan lines or A-lines. To test the algorithm, ultrasound system channel data were acquired during liver scans from 8 patients. Multiple data sets were acquired from each patient, with 55 total data sets, 43 of which had identifiable hypoechoic regions on normal B-mode images. The data sets with identifiable hypoechoic regions were analyzed. The results show the decluttered B-mode images have an average improvement in contrast over normal images of 7.3±4.6 dB. The CNR changed little on average between normal and decluttered B-mode, −0.4±5.9 dB. The in vivo speckle SNR decreased; the change was −0.65±0.28. Phantom speckle SNR also decreased but only by −0.40±0.03.

Deep Neural Networks for Ultrasound Beamforming

“…For this reason, numerous techniques have been proposed to improve ultrasound image quality [1], [2], [3], [4]. Few of these techniques have transferred to the clinic.…”

“…developed a model based beamforming method called aperture domain model image reconstruction (ADMIRE) [4], [5]. They tuned ADMIRE to improve ultrasound image quality by suppressing sources of image degradation.…”

Pseudononlinear ultrasound simulation approach for reverberation clutter

“…A primary effect of these multiply scattered waves is to delay them in time relative to their arrival time after scattering only once, despite returning from the same location. 9 …”

Frequency-space prediction filtering for acoustic clutter and random noise attenuation in ultrasound imaging

“…Since the imaging aperture is always finite in size, the beam focusing always generates a leakage of acoustic energy, which shows up as sidelobes and clutter that reduce image contrast. Several methods have been proposed in the past to address this issue and enhance image contrast [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] . For example, an extensive amount of effort has been made to develop coherence-based adaptive weighting techniques including the coherence factor (CF) 10 , the generalized coherence factor (GCF) 11,12 , the phase coherence factor (PCF) 13 , and short-lag spatial coherence (SLSC) 14 .…”

“…A great amount of effort has also been made to develop apodization-based techniques including the dual apodization with cross-correlation (DAX) 15,16 and its combined approaches with other methods [17][18][19] , the phase apodization with cross-correlation (PAX) 20 and their extensions to multiple apodizations 21,22 . Other interesting methods include the parallel adaptive receive compensation algorithm (PARCA) 23 , a Fourier transformbased approach 24 , and a chirp model-based approach 25 . In this paper, we present a new technique called frequency-space (F-X) prediction filtering (FXPF) to reduce random noise and acoustic clutter for enhanced ultrasound image contrast.…”