GPU acceleration of a model-based iterative method for Digital Breast Tomosynthesis

R Cavicchioli,L Zanni,J Cheng Hu,E Loli Piccolomini,E Morotti

doi:10.1038/s41598-019-56920-y

Abstract

Digital Breast Tomosynthesis (DBT) is a modern 3D Computed Tomography X-ray technique for the early detection of breast tumors, which is receiving growing interest in the medical and scientific community. Since DBT performs incomplete sampling of data, the image reconstruction approaches based on iterative methods are preferable to the classical analytic techniques, such as the Filtered Back Projection algorithm, providing fewer artifacts. In this work, we consider a Model-Based Iterative Reconstruction (MBIR) method well suited to describe the DBT data acquisition process and to include prior information on the reconstructed image. We propose a gradient-based solver named Scaled Gradient Projection (SGP) for the solution of the constrained optimization problem arising in the considered MBIR method. Even if the SGP algorithm exhibits fast convergence, the time required on a serial computer for the reconstruction of a real DBT data set is too long for the clinical needs. In this paper we propose a parallel SGP version designed to perform the most expensive computations of each iteration on Graphics Processing Unit (GPU). We apply the proposed parallel approach on three different GPU boards, with computational performance comparable with that of the boards usually installed in commercial DBT systems. The numerical results show that the proposed GPU-based MBIR method provides accurate reconstructions in a time suitable for clinical trials.

Highlights

Digital Breast Tomosynthesis (DBT) is a quite recent 3D Computed Tomography (CT) technique providing a volumetric breast reconstruction as a stack of 2D images, each representing a cross-sectional slice of the breast itself[1]
Exploiting the computational power of modern Graphics Processing Unit (GPU) boards, we aim to perform the expensive operations of each Scaled Gradient Projection (SGP) iteration in a time consistent with the practical constraints imposed by the DBT application
In traditional 2D mammography, cancerous masses are often camouflaged by the superposition of dense breast tissue on the final image, 3D breast reconstructions are getting increasing interest

Summary

Introduction

Digital Breast Tomosynthesis (DBT) is a quite recent 3D Computed Tomography (CT) technique providing a volumetric breast reconstruction as a stack of 2D images, each representing a cross-sectional slice of the breast itself[1]. Among the different iterative approaches in X-ray CT (see[5] for a detailed classification of iterative methods in CT), the so called Model-Based Iterative Reconstruction (MBIR) methods are getting growing attention They try to model the acquisition process as accurately as possible, since they take into account system geometry, physical interactions of photons in the projections and prior information about the acquired volume. This approach produces better results than the traditional FBP in terms of quality of the reconstructed image and artifacts reduction, especially for low-dose or limited data X-ray CT. Since SGP showed a very fast convergence in the first iterations, we choose a similar approach for our DBT application

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