  Fluid pressure and fluid velocity carry important information for cancer
diagnosis, prognosis and treatment. Recent work has demonstrated that
estimation of these parameters is theoretically possible using ultrasound
poroelastography. However, accurate estimation of these parameters requires
high quality axial and lateral strain estimates from noisy ultrasound radio
frequency (RF) data. In this paper, we propose a filtering technique combining
two efficient filters for removal of noise from strain images, i.e., Kalman and
nonlinear complex diffusion filters (NCDF). Our proposed filter is based on a
novel noise model, which takes into consideration both additive and amplitude
modulation noise in the estimated strains. Using finite element and ultrasound
simulations, we demonstrate that the proposed filtering technique can
significantly improve image quality of lateral strain elastograms along with
fluid pressure and velocity elastograms. Technical feasibility of the proposed
method on an in vivo set of data is also demonstrated. Our results show that
the CNRe of the lateral strain, fluid pressure and fluid velocity as estimated
using the proposed technique is higher by at least 10.9%, 51.3% and 334.4% when
compared to the results obtained using a Kalman filter only, by at least 8.9%,
27.6% and 219.5% when compared to the results obtained using a NCDF only and by
at least 152.3%, 1278% and 742% when compared to the results obtained using a
median filter only for all SNRs considered in this study.
