Spatially variant attenuation and backscatter coefficient estimation using a regularized linear least-squares approach

In the context of quantitative ultrasound, it is of importance to estimate the acoustic properties, primarily the attenuation and backscatter coefficients, of the tissue of interest. To this end, fast estimation techniques have recently been proposed in the literature, which fit a backscatter signal model to the data in a linear least-squares manner. The coefficient estimation problem becomes challenging when the underlying medium is heterogeneous. For such a spatially-variant/multi-layered medium, the existing methods estimate the average attenuation coefficient until a given depth, from which local attenuation coefficients need to be extracted for attenuation mapping. Based on our recent work, we propose here a method which directly estimates the local attenuation coefficient values at each depth point. Unlike our previous work, the proposed method does not rely on prior-knowledge of the layer boundaries and use a regularization strategy to enforce smoothness of the target attenuation profile. It is shown to outperform the benchmark method, both on synthetic and phantom data. We further investigate spatial and contrast resolution of the proposed solution.