scholarly journals A Model-Based Optimization Framework for Iterative Digital Breast Tomosynthesis Image Reconstruction

2021 ◽  
Vol 7 (2) ◽  
pp. 36
Author(s):  
Elena Loli Piccolomini ◽  
Elena Morotti
Keyword(s):  
Image Reconstruction ◽  
Regularization Parameter ◽  
Iterative Algorithms ◽  
Digital Breast Tomosynthesis ◽  
Real Data ◽  
Breast Tomosynthesis ◽  
Optimization Framework ◽  
Model Based ◽  
Volumetric Reconstruction ◽  
The Masses

Digital Breast Tomosynthesis is an X-ray imaging technique that allows a volumetric reconstruction of the breast, from a small number of low-dose two-dimensional projections. Although it is already used in the clinical setting, enhancing the quality of the recovered images is still a subject of research. The aim of this paper was to propose and compare, in a general optimization framework, three slightly different models and corresponding accurate iterative algorithms for Digital Breast Tomosynthesis image reconstruction, characterized by a convergent behavior. The suggested model-based implementations are specifically aligned to Digital Breast Tomosynthesis clinical requirements and take advantage of a Total Variation regularizer. We also tune a fully-automatic strategy to set a proper regularization parameter. We assess our proposals on real data, acquired from a breast accreditation phantom and a clinical case. The results confirm the effectiveness of the presented framework in reconstructing breast volumes, with particular focus on the masses and microcalcifications, in few iterations and in enhancing the image quality in a prolonged execution.

Backprojection Filtration Image Reconstruction Approach for Reducing High-Density Object Artifacts in Digital Breast Tomosynthesis

2019 ◽  
Vol 38 (5) ◽  
pp. 1161-1171 ◽  
Author(s):  
Hyeongseok Kim ◽  
Jongha Lee ◽  
Jeongtae Soh ◽  
Jonghwan Min ◽  
Young Wook Choi ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Digital Breast Tomosynthesis ◽  
High Density ◽  
Breast Tomosynthesis

Improving depth resolution in digital breast tomosynthesis by iterative image reconstruction

2015 ◽  
Author(s):  
Erin G. Roth ◽  
David N. Kraemer ◽  
Emil Y. Sidky ◽  
Ingrid S. Reiser ◽  
Xiaochuan Pan
Keyword(s):  
Image Reconstruction ◽  
Digital Breast Tomosynthesis ◽  
Depth Resolution ◽  
Iterative Image Reconstruction ◽  
Breast Tomosynthesis

256. Synthesized 2D image reconstruction from Digital Breast Tomosynthesis (DBT)

2018 ◽  
Vol 56 ◽  
pp. 220
Author(s):  
M. Piergentili ◽  
D. Zefiro ◽  
F. Ielo ◽  
F. Foppiano ◽  
P. Boccacci
Keyword(s):  
Image Reconstruction ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis

scholarly journals Use of a Total Variation Minimization Iterative Reconstruction Algorithm to Evaluate Reduced Projections during Digital Breast Tomosynthesis

2018 ◽  
Vol 2018 ◽  
pp. 1-14 ◽  
Author(s):  
Tsutomu Gomi ◽  
Yukio Koibuchi
Keyword(s):  
Total Variation ◽  
Convex Sets ◽  
Iterative Algorithms ◽  
Digital Breast Tomosynthesis ◽  
Reconstruction Algorithm ◽  
Structural Similarity ◽  
Lesion Size ◽  
Reconstruction Technique ◽  
Breast Tomosynthesis ◽  
Total Variation Minimization

Purpose. We evaluated the efficacies of the adaptive steepest descent projection onto convex sets (ASD-POCS), simultaneous algebraic reconstruction technique (SART), filtered back projection (FBP), and maximum likelihood expectation maximization (MLEM) total variation minimization iterative algorithms for reducing exposure doses during digital breast tomosynthesis for reduced projections. Methods. Reconstructions were evaluated using normal (15 projections) and half (i.e., thinned-out normal) projections (seven projections). The algorithms were assessed by determining the full width at half-maximum (FWHM), and the BR3D Phantom was used to evaluate the contrast-to-noise ratio (CNR) for the in-focus plane. A mean similarity measure of structural similarity (MSSIM) was also used to identify the preservation of contrast in clinical cases. Results. Spatial resolution tended to deteriorate in ASD-POCS algorithm reconstructions involving a reduced number of projections. However, the microcalcification size did not affect the rate of FWHM change. The ASD-POCS algorithm yielded a high CNR independently of the simulated mass lesion size and projection number. The ASD-POCS algorithm yielded a high MSSIM in reconstructions from reduced numbers of projections. Conclusions. The ASD-POCS algorithm can preserve contrast despite a reduced number of projections and could therefore be used to reduce radiation doses.

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

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
R. Cavicchioli ◽  
J. Cheng Hu ◽  
E. Loli Piccolomini ◽  
E. Morotti ◽  
L. Zanni
Keyword(s):  
Graphics Processing Unit ◽  
Digital Breast Tomosynthesis ◽  
Projection Algorithm ◽  
Processing Unit ◽  
Data Set ◽  
Breast Tomosynthesis ◽  
Model Based ◽  
Computational Performance ◽  
Gradient Based ◽  
Time Required

AbstractDigital 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.

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