TOMOGRAPHIC MAMMOGRAPHY AND TOMOSYNTHESIS USING OPENGL
Computed tomography is still being intensively studied and widely used to solve a number of industrial and medical applications. The simultaneous algebraic reconstruction technique (SART) and Bayesian inference reconstruction (BIR) are considered as advantageous iteration methods that are most suita...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Belarusian National Technical University
2016-03-01
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| Series: | Системный анализ и прикладная информатика |
| Subjects: | |
| Online Access: | https://sapi.bntu.by/jour/article/view/91 |
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| Summary: | Computed tomography is still being intensively studied and widely used to solve a number of industrial and medical applications. The simultaneous algebraic reconstruction technique (SART) and Bayesian inference reconstruction (BIR) are considered as advantageous iteration methods that are most suitable for improving the quality of the reconstructed 3D-images. The paper deals with the parallel iterative algorithms to ensure the reconstruction of threedimensional images of the breast, recovered from a limited set of noisy X-ray projections. Algebraic method of reconstruction with simultaneous iterations – SART and iterative method for statistical reconstruction of BIR are deemed to be the most preferred iterative methods. We believe that these methods are particularly useful for improving the quality of breast reconstructed image. We use the graphics processor (GPU) to accelerate the process of reconstruction. Preliminary results show that all investigated methods are useful in breast reconstruction layered images. However, it was found that the method of classical tomosynthesis SAA is less efficient than iterative methods SART and BIR as the worst suppress the anatomical noise. Despite the fact that the estimated ratio of the contrast / noise ratio in the presence of internal structures with low contrast is higher for classical tomosynthesis method the SAA, its effectiveness in the presence of highly structured background is low. In our opinion the best results can be achieved using statistical iterative reconstruction BIR. |
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| ISSN: | 2309-4923 2414-0481 |