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Fluid pressure and fluid velocity carry important information for cancerdiagnosis, prognosis and treatment. Recent work has demonstrated thatestimation of these parameters is theoretically possible using ultrasoundporoelastography. However, accurate estimation of these parameters requireshigh quality axial and lateral strain estimates from noisy ultrasound radiofrequency (RF) data. In this paper, we propose a filtering technique combiningtwo efficient filters for removal of noise from strain images, i.e., Kalman andnonlinear complex diffusion filters (NCDF). Our proposed filter is based on anovel noise model, which takes into consideration both additive and amplitudemodulation noise in the estimated strains. Using finite element and ultrasoundsimulations, we demonstrate that the proposed filtering technique cansignificantly improve image quality of lateral strain elastograms along withfluid pressure and velocity elastograms. Technical feasibility of the proposedmethod on an in vivo set of data is also demonstrated. Our results show thatthe CNRe of the lateral strain, fluid pressure and fluid velocity as estimatedusing the proposed technique is higher by at least 10.9%, 51.3% and 334.4% whencompared 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 byat least 152.3%, 1278% and 742% when compared to the results obtained using amedian filter only for all SNRs considered in this study.

A novel filter for accurate estimation of fluid pressure and fluid velocity using poroelastography