Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image Reconstruction
Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences. Since breast is sensitive to x-ray radiation, dose reduction of breast CT is an important topic, and for this...
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IEEE
2020-01-01
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Series: | IEEE Access |
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Online Access: | https://ieeexplore.ieee.org/document/9239986/ |
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author | Huidong Xie Hongming Shan Wenxiang Cong Chi Liu Xiaohua Zhang Shaohua Liu Ruola Ning Ge Wang |
author_facet | Huidong Xie Hongming Shan Wenxiang Cong Chi Liu Xiaohua Zhang Shaohua Liu Ruola Ning Ge Wang |
author_sort | Huidong Xie |
collection | DOAJ |
description | Breast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences. Since breast is sensitive to x-ray radiation, dose reduction of breast CT is an important topic, and for this purpose, few-view scanning is a main approach. In this article, we propose a Deep Efficient End-to-end Reconstruction (DEER) network for few-view breast CT image reconstruction. The major merits of our network include high dose efficiency, excellent image quality, and low model complexity. By the design, the proposed network can learn the reconstruction process with as few as (9(N) parameters, where N is the side length of an image to be reconstructed, which represents orders of magnitude improvements relative to the state-of-the-art deeplearning-based reconstruction methods that map raw data to tomographic images directly. Also, validated on a cone-beam breast CT dataset prepared by Koning Corporation on a commercial scanner, our method demonstrates a competitive performance over the state-of-the-art reconstruction networks in terms of image quality. The source code of this paper is available at: https://github.com/HuidongXie/DEER. |
first_indexed | 2024-12-17T05:01:56Z |
format | Article |
id | doaj.art-72daf4fdf183414bacd7ddf16e45f084 |
institution | Directory Open Access Journal |
issn | 2169-3536 |
language | English |
last_indexed | 2024-12-17T05:01:56Z |
publishDate | 2020-01-01 |
publisher | IEEE |
record_format | Article |
series | IEEE Access |
spelling | doaj.art-72daf4fdf183414bacd7ddf16e45f0842022-12-21T22:02:32ZengIEEEIEEE Access2169-35362020-01-01819663319664610.1109/ACCESS.2020.30337959239986Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image ReconstructionHuidong Xie0https://orcid.org/0000-0002-1124-3548Hongming Shan1https://orcid.org/0000-0002-0604-3197Wenxiang Cong2https://orcid.org/0000-0003-2908-1759Chi Liu3https://orcid.org/0000-0002-7007-1037Xiaohua Zhang4Shaohua Liu5Ruola Ning6Ge Wang7https://orcid.org/0000-0002-2656-7705Department of Biomedical Engineering, Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USADepartment of Biomedical Engineering, Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USADepartment of Biomedical Engineering, Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USADepartment of Biomedical Engineering, Yale University, New Haven, CT, USAKoning Corporation, West Henrietta, NY, USAKoning Corporation, West Henrietta, NY, USAKoning Corporation, West Henrietta, NY, USADepartment of Biomedical Engineering, Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY, USABreast CT provides image volumes with isotropic resolution in high contrast, enabling detection of small calcification (down to a few hundred microns in size) and subtle density differences. Since breast is sensitive to x-ray radiation, dose reduction of breast CT is an important topic, and for this purpose, few-view scanning is a main approach. In this article, we propose a Deep Efficient End-to-end Reconstruction (DEER) network for few-view breast CT image reconstruction. The major merits of our network include high dose efficiency, excellent image quality, and low model complexity. By the design, the proposed network can learn the reconstruction process with as few as (9(N) parameters, where N is the side length of an image to be reconstructed, which represents orders of magnitude improvements relative to the state-of-the-art deeplearning-based reconstruction methods that map raw data to tomographic images directly. Also, validated on a cone-beam breast CT dataset prepared by Koning Corporation on a commercial scanner, our method demonstrates a competitive performance over the state-of-the-art reconstruction networks in terms of image quality. The source code of this paper is available at: https://github.com/HuidongXie/DEER.https://ieeexplore.ieee.org/document/9239986/Breast CTdeep learningfew-view CTlow-dose CTX-ray CT |
spellingShingle | Huidong Xie Hongming Shan Wenxiang Cong Chi Liu Xiaohua Zhang Shaohua Liu Ruola Ning Ge Wang Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image Reconstruction IEEE Access Breast CT deep learning few-view CT low-dose CT X-ray CT |
title | Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image Reconstruction |
title_full | Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image Reconstruction |
title_fullStr | Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image Reconstruction |
title_full_unstemmed | Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image Reconstruction |
title_short | Deep Efficient End-to-End Reconstruction (DEER) Network for Few-View Breast CT Image Reconstruction |
title_sort | deep efficient end to end reconstruction deer network for few view breast ct image reconstruction |
topic | Breast CT deep learning few-view CT low-dose CT X-ray CT |
url | https://ieeexplore.ieee.org/document/9239986/ |
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