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A Novel Task-Based Reconstruction Approach for Digital Breast Tomosynthesis

Abstract : The reconstruction of a volumetric image from Digital Breast Tomosynthesis (DBT) measurements is an ill-posed inverse problem, for which existing iterative regularized approaches can provide a good solution. However, the clinical task is somehow omitted in the derivation of those techniques, although it plays a primary role in the radiologist diagnosis. In this work, we address this issue by introducing a novel variational formulation for DBT reconstruction, tailored for a specific clinical task, namely the detection of microcalcifications. Our method aims at simultaneously enhancing the detectability performance and enabling a high-quality restoration of the background breast tissues. Our contribution is threefold. First, we introduce an original task-based reconstruction framework through the proposition of a detectability function inspired from mathematical model observers. Second, we propose a novel total-variation regularizer where the gradient field accounts for the different morphological contents of the imaged breast. Third, we integrate the two developed measures into a cost function, minimized thanks to a new form of the Majorize Minimize Memory Gradient (3MG) algorithm. We conduct a numerical comparison of the convergence speed of the proposed method with those of standard convex optimization algorithms. Experimental results show the interest of our DBT reconstruction approach, qualitatively and quantitatively.
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Submitted on : Tuesday, October 20, 2020 - 1:20:37 PM
Last modification on : Thursday, May 27, 2021 - 10:34:37 AM
Long-term archiving on: : Thursday, January 21, 2021 - 6:59:09 PM


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  • HAL Id : hal-02972386, version 1


Maissa Sghaier, Emilie Chouzenoux, Jean-Christophe Pesquet, Serge Muller. A Novel Task-Based Reconstruction Approach for Digital Breast Tomosynthesis. 2020. ⟨hal-02972386⟩



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