scholarly journals Enhanced imaging of microcalcifications in digital breast tomosynthesis through improved image-reconstruction algorithms

2009 ◽  
Vol 36 (11) ◽  
pp. 4920-4932 ◽  
Author(s):  
Emil Y. Sidky ◽  
Xiaochuan Pan ◽  
Ingrid S. Reiser ◽  
Robert M. Nishikawa ◽  
Richard H. Moore ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Digital Breast Tomosynthesis ◽  
Reconstruction Algorithms ◽  
Breast Tomosynthesis ◽  
Image Reconstruction Algorithms ◽  
Enhanced Imaging

Evaluation of reconstruction algorithms for a stationary digital breast tomosynthesis system using a carbon nanotube X-ray source array

2020 ◽  
Vol 28 (6) ◽  
pp. 1157-1169
Author(s):  
Zhanli Hu ◽  
Zixiang Chen ◽  
Chao Zhou ◽  
Xuda Hong ◽  
Jianwei Chen ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Imaging System ◽  
Digital Breast Tomosynthesis ◽  
Reconstruction Technique ◽  
Reconstruction Algorithms ◽  
Breast Tomosynthesis ◽  
X Ray ◽  
Noise Ratio ◽  
Set Up ◽  
Source Array

Breast cancer is the most frequently diagnosed cancer in women worldwide. Digital breast tomosynthesis (DBT), which is based on limited-angle tomography, was developed to solve tissue overlapping problems associated with traditional breast mammography. However, due to the problems associated with tube movement during the process of data acquisition, stationary DBT (s-DBT) was developed to allow the X-ray source array to stay stationary during the DBT scanning process. In this work, we evaluate four widely used and investigated DBT image reconstruction algorithms, including the commercial Feldkamp-Davis-Kress algorithm (FBP), the simultaneous iterative reconstruction technique (SIRT), the simultaneous algebraic reconstruction technique (SART) and the total variation regularized SART (SART-TV) for an s-DBT imaging system that we set up in our own laboratory for studies using a semi-elliptical digital phantom and a rubber breast phantom to determine the most superior algorithm for s-DBT image reconstruction among the four algorithms. Several quantitative indexes for image quality assessment, including the peak signal-noise ratio (PSNR), the root mean square error (RMSE) and the structural similarity (SSIM), are used to determine the best algorithm for the imaging system that we set up. Image resolutions are measured via the calculation of the contrast-to-noise ratio (CNR) and artefact spread function (ASF). The experimental results show that the SART-TV algorithm gives reconstructed images with the highest PSNR and SSIM values and the lowest RMSE values in terms of image accuracy and similarity, along with the highest CNR values calculated for the selected features and the best ASF curves in terms of image resolution in the horizontal and vertical directions. Thus, the SART-TV algorithm is proven to be the best algorithm for use in s-DBT image reconstruction for the specific imaging task in our study.

Three dimensional deblurring of transmitted-light brightfield micrographs

1993 ◽  
Vol 51 ◽  
pp. 564-565
Author(s):  
Santosh Bhattacharyya
Keyword(s):  
Image Reconstruction ◽  
Three Dimensional ◽  
Transmitted Light ◽  
Reconstruction Algorithms ◽  
3D Image Reconstruction ◽  
Structural Details ◽  
Spread Function ◽  
Early Testing ◽  
Linearized Model ◽  
Image Reconstruction Algorithms

Three dimensional microscopic structures play an important role in the understanding of various biological and physiological phenomena. Structural details of neurons, such as the density, caliber and volumes of dendrites, are important in understanding physiological and pathological functioning of nervous systems. Even so, many of the widely used stains in biology and neurophysiology are absorbing stains, such as horseradish peroxidase (HRP), and yet most of the iterative, constrained 3D optical image reconstruction research has concentrated on fluorescence microscopy. It is clear that iterative, constrained 3D image reconstruction methodologies are needed for transmitted light brightfield (TLB) imaging as well. One of the difficulties in doing so, in the past, has been in determining the point spread function of the system.We have been developing several variations of iterative, constrained image reconstruction algorithms for TLB imaging. Some of our early testing with one of them was reported previously. These algorithms are based on a linearized model of TLB imaging.

scholarly journals Muon Tracing and Image Reconstruction Algorithms for Cosmic Ray Muon Computed Tomography

2019 ◽  
Vol 28 (1) ◽  
pp. 426-435 ◽  
Author(s):  
Zhengzhi Liu ◽  
Stylianos Chatzidakis ◽  
John M. Scaglione ◽  
Can Liao ◽  
Haori Yang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Reconstruction ◽  
Cosmic Ray ◽  
Reconstruction Algorithms ◽  
Image Reconstruction Algorithms

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
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