Iterative method to achieve noise variance stabilization in single raw digital breast tomosynthesis

Abstract

The majority of the denoising algorithms available in the literature are designed to treat signal-independent Gaussian noise. However, in digital breast tomosynthesis (DBT) systems, the noise model seldom presents signal-independence. In this scenario, variance-stabilizing transforms (VSTs) may be used to convert the signaldependent noise into approximately signal-independent noise, enabling the use of ‘off-the-shelf’ denoising techniques. The accurate stabilization of the noise variance requires a robust estimation of the system’s noise coefficients, usually obtained using calibration data. However, practical issues often arise when calibration data are required, impairing the clinical deployment of algorithms that rely on variance stabilization. In this work, we present a practical method to achieve variance stabilization by approaching it as an optimization task, with the stabilized noise variance dictating the cost function. An iterative method is used to implicitly optimize the coefficients used in the variance stabilization, leveraging a single set of raw DBT projections. The variance stabilization achieved using the proposed method is compared against the stabilization achieved using noise coefficients estimated from calibration data, considering two commercially available DBT systems and a prototype DBT system. The results showed that the average error for variance stabilization achieved using the proposed method is comparable to the error achieved through calibration data. Thus, the proposed method can be a viable alternative for achieving variance stabilization when calibration data are not easily accessible, facilitating the clinical deployment of algorithms that rely on variance stabilization.