Digital Breast Tomosynthesis imaging using compressed sensing based reconstruction for 10 radiation doses real data

2019 ◽  
Vol 48 ◽  
pp. 26-34 ◽  
Author(s):  
Adem Polat ◽  
Nuno Matela ◽  
Ali Dinler ◽  
Yu Shrike Zhang ◽  
Isa Yildirim
Keyword(s):  
Compressed Sensing ◽  
Digital Breast Tomosynthesis ◽  
Real Data ◽  
Radiation Doses ◽  
Breast Tomosynthesis

Compressed-sensing (CS)-based digital breast tomosynthesis (DBT) reconstruction for low-dose, accurate 3D breast X-ray imaging

2014 ◽  
Vol 65 (4) ◽  
pp. 565-571 ◽  
Author(s):  
Yeonok Park ◽  
Hyosung Cho ◽  
Uikyu Je ◽  
Daeki Hong ◽  
Minsik Lee ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Low Dose ◽  
Digital Breast Tomosynthesis ◽  
Breast Tomosynthesis ◽  
X Ray ◽  
X Ray Imaging

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.

scholarly journals An Object-Oriented Simulator for 3D Digital Breast Tomosynthesis Imaging System

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Saeed Seyyedi ◽  
Kubra Cengiz ◽  
Mustafa Kamasak ◽  
Isa Yildirim
Keyword(s):  
Compressed Sensing ◽  
Imaging System ◽  
Digital Breast Tomosynthesis ◽  
Object Oriented ◽  
Reconstruction Technique ◽  
Data Sets ◽  
Algebraic Reconstruction Technique ◽  
Reconstruction Algorithms ◽  
Breast Tomosynthesis ◽  
Algebraic Reconstruction

Digital breast tomosynthesis (DBT) is an innovative imaging modality that provides 3D reconstructed images of breast to detect the breast cancer. Projections obtained with an X-ray source moving in a limited angle interval are used to reconstruct 3D image of breast. Several reconstruction algorithms are available for DBT imaging. Filtered back projection algorithm has traditionally been used to reconstruct images from projections. Iterative reconstruction algorithms such as algebraic reconstruction technique (ART) were later developed. Recently, compressed sensing based methods have been proposed in tomosynthesis imaging problem. We have developed an object-oriented simulator for 3D digital breast tomosynthesis (DBT) imaging system using C++ programming language. The simulator is capable of implementing different iterative and compressed sensing based reconstruction methods on 3D digital tomosynthesis data sets and phantom models. A user friendly graphical user interface (GUI) helps users to select and run the desired methods on the designed phantom models or real data sets. The simulator has been tested on a phantom study that simulates breast tomosynthesis imaging problem. Results obtained with various methods including algebraic reconstruction technique (ART) and total variation regularized reconstruction techniques (ART+TV) are presented. Reconstruction results of the methods are compared both visually and quantitatively by evaluating performances of the methods using mean structural similarity (MSSIM) values.

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