Improvement of image performance in digital breast tomosynthesis (DBT) by incorporating a compressed-sensing (CS)-based deblurring scheme
| Autor: | Sungil Choi, Soyoung Park, Heemoon Cho, Yeonok Park, Taeho Woo, Hyunwoo Lim, Kyuseok Kim, Hyosung Cho, Uikyu Je, Chulkyu Park |
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| Rok vydání: | 2016 |
| Předmět: |
Deblurring
Radiation Pixel business.industry Computer science Image processing 02 engineering and technology Iterative reconstruction 030218 nuclear medicine & medical imaging Gaussian filter 03 medical and health sciences symbols.namesake 0302 clinical medicine Kernel (image processing) Gaussian noise 0202 electrical engineering electronic engineering information engineering symbols Kernel adaptive filter 020201 artificial intelligence & image processing Computer vision Artificial intelligence business |
| Zdroj: | Radiation Physics and Chemistry. 127:147-154 |
| ISSN: | 0969-806X |
| Popis: | In this work, we investigated a compressed-sensing (CS)-based deblurring scheme incorporated with the total-variation (TV) regularization penalty for image deblurring of high accuracy and adopted it into the image reconstruction in conventional digital breast tomosynthesis (DBT). We implemented the proposed algorithm and performed a systematic simulation to demonstrate its viability for improving the image performance in DBT as well as two-dimensional (2D) mammography. In the simulation, blurred noisy projection images of a 3D numerical breast phantom were generated by convolving their original (or exact) version by a designed 2D Gaussian filter kernel (standard deviation=2 in pixel unit, kernel size=11×11), followed by adding Gaussian noise (mean=0, variance=0.05), and deblurred by using the algorithm before performing the DBT reconstruction procedure. Here the projection images were taken with a half tomographic angle of θ=20° and an angle step of Δθ=2°. We investigated the image performance of the reconstructed DBT images quantitatively in terms of the modulation and the slice-sensitive profile (SSP). |
| Databáze: | OpenAIRE |
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