Digital breast tomosynthesis image reconstruction using 2D and 3D total variation minimization

Metin Ertas,Mustafa Kamasak,Isa Yildirim,Aydin Akan

doi:10.1186/1475-925x-12-112

Abstract

BackgroundDigital breast tomosynthesis (DBT) is an emerging imaging modality which produces three-dimensional radiographic images of breast. DBT reconstructs tomographic images from a limited view angle, thus data acquired from DBT is not sufficient enough to reconstruct an exact image. It was proven that a sparse image from a highly undersampled data can be reconstructed via compressed sensing (CS) techniques. This can be done by minimizing the l1 norm of the gradient of the image which can also be defined as total variation (TV) minimization. In tomosynthesis imaging problem, this idea was utilized by minimizing total variation of image reconstructed by algebraic reconstruction technique (ART). Previous studies have largely addressed 2-dimensional (2D) TV minimization and only few of them have mentioned 3-dimensional (3D) TV minimization. However, quantitative analysis of 2D and 3D TV minimization with ART in DBT imaging has not been studied.MethodsIn this paper two different DBT image reconstruction algorithms with total variation minimization have been developed and a comprehensive quantitative analysis of these two methods and ART has been carried out: The first method is ART + TV2D where TV is applied to each slice independently. The other method is ART + TV3D in which TV is applied by formulating the minimization problem 3D considering all slices.ResultsA 3D phantom which roughly simulates a breast tomosynthesis image was designed to evaluate the performance of the methods both quantitatively and qualitatively in the sense of visual assessment, structural similarity (SSIM), root means square error (RMSE) of a specific layer of interest (LOI) and total error values. Both methods show superior results in reducing out-of-focus slice blur compared to ART.ConclusionsComputer simulations show that ART + TV3D method substantially enhances the reconstructed image with fewer artifacts and smaller error rates than the other two algorithms under the same configuration and parameters and it provides faster convergence rate.

Highlights

Digital breast tomosynthesis (DBT) is an emerging imaging modality which produces three-dimensional radiographic images of breast
total variation (TV) minimization has recently been adapted to DBT imaging reconstruction problems and better results were obtained than filtered back projection (FBP) and iterative methods [13,14]
Another alternative approach was introduced in [17] where, curvelets were used for reconstruction at a limited angular range and the results showed that the method was stable and edge-preserving

Summary

Introduction

Digital breast tomosynthesis (DBT) is an emerging imaging modality which produces three-dimensional radiographic images of breast. Digital tomosynthesis has become an emerging imaging modality for breast imaging where the breast is projected onto a flat panel detector from a limited view angle and a few number of projections is acquired to produce a three dimensional image of the breast. It is currently being used for both diagnosis and screening purposes [1]. Due to the limited range of view angles, the number of projections may not be sufficient to fully reconstruct the image in the Fourier space [2]. Several studies has shown that iterative algorithms can show satisfactory results over filtered back projection (FBP), modified FBP and matrix-inversion algorithms in missing data image reconstruction, they still do not give acceptable results in tomosynthesis imaging [2,4,5]

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