Adaptive Filtering-based Heavy-Noise Removal in Born Iterative Method

Based on scattered ultrasound measurements, cross-sectional images showing the spatial distribution of some physical properties of the target of interest can be generated in ultrasonic tomography. These measurements can obtain dense datasets at various transmitter and receiver locations. The Born approximation approach, which provides a simple linear relationship between the goal function and the scattering field, was used to solve the inverse scattering problem. The Born iterative method (BIM), which employs the first-order Born approximation, is a helpful diffraction tomography approach. In BIM, the scattering data obtained by the probes will be affected by Gaussian noise, especially noise appearing in the background environment. In this work, we propose to apply the adaptive filter, specifically the least mean squares (LMS) filter, to reduce noise. Simulation results show that when the scattered signal is heavily affected by noise, the LMS filter can still successfully remove noise from the noisy signal. The relative residual error (RRE) parameter is used to assess the performance of the proposed approach.