Pseudononlinear ultrasound simulation approach for reverberation clutter

Abstract

Abstract. Multipath scattering, or reverberation, takes a substantial toll on image quality in many clinical exams.We have suggested a model-based solution to this problem, which we refer to as aperture domain model image reconstruction (ADMIRE). For ADMIRE to work well, it must be trained with precisely characterized data. To solve this specific problem and the general problem of efficiently simulating reverberation, we propose an approach to simulate reverberation with linear simulation tools. Our simulation method defines total propagation time, first scattering site, and a final scattering site. We use a linear simulation package, such as Field II, to simulate scattering from the final site and then shift the simulated wavefront later in time based on the total propagation time and the geometry of the first scattering site. We validate our simulations using theoretical descriptions of clutter in the literature and data acquired from ex vivo tissue. We found that ex vivo tissue clutter had a mean speckle SNR of 1.40 AE 0.23, which we could simulate with about 2 scatterers per resolution cell. Axial clutter distributions drawn from an exponential distribution with a mean of 5 mm and at least 0.5 scatters per resolution cell resulted in clutter that was statistically indistinguishable from the van Cittert-Zernike behavior predicted by literature.

The Impact of Model-Based Clutter Suppression on Cluttered, Aberrated Wavefronts

“…In principle, the lower autocorrelation FWHM would come from greater spatial variability in the tissues generating the aberration, whereas higher RMS would be related to larger deviations in the average sound speed relative to 1540 m/s along the path to each transducer element. We generated aberrated wavefronts of FWHM = 5.0 ± 0.1 mm, and RMS = 50 ns [41], and multipath scattering was simulated using a pseudo non-linear adaptation to Field II [42], [43]. We scaled the clutter level of multipath scattering, relative to the signal of interest (SOI) to specified signal-to-clutter ratios (SCR), $$\mathrm{italicSCR}=10\mathrm{normallog}10\left(\frac{{\mathrm{P}}_{\text{SOI}}}{{\mathrm{P}}_{\text{Clutter}}}\right).$$When evaluating clutter, we considered three levels of 0, 10 and 20 dB SCR.…”

“…We also added reverberation clutter at an SCR of 0 dB using our pseudo non-linear simulation method [42]. We then performed image quality metrics—contrast and CNR measurements indicated in (16) and (17).…”