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

Renann F. Brandão; Lucas R. Borges; Bruno Barufaldi; Trevor L. Vent; Renato F. Caron; Bruno B. Oliveira; Andrew D. A. Maidment; Marcelo A. C. Vieira

doi:10.1117/12.2580965

15 February 2021 Iterative method to achieve noise variance stabilization in single raw digital breast tomosynthesis

Renann F. Brandão, Lucas R. Borges, Bruno Barufaldi, Trevor L. Vent, Renato F. Caron, Bruno B. Oliveira, Andrew D. A. Maidment, Marcelo A. C. Vieira

Author Affiliations +

Proceedings Volume 11595, Medical Imaging 2021: Physics of Medical Imaging; 115954R (2021) https://doi.org/10.1117/12.2580965
Event: SPIE Medical Imaging, 2021, Online Only

Conference Poster

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.

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Renann F. Brandão, Lucas R. Borges, Bruno Barufaldi, Trevor L. Vent, Renato F. Caron, Bruno B. Oliveira, Andrew D. A. Maidment, and Marcelo A. C. Vieira "Iterative method to achieve noise variance stabilization in single raw digital breast tomosynthesis", Proc. SPIE 11595, Medical Imaging 2021: Physics of Medical Imaging, 115954R (15 February 2021); https://doi.org/10.1117/12.2580965

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