Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior.
Breast cancer remains the most prevalent malignancy in women in many countries around the world, thus calling for better imaging technologies to improve screening and diagnosis. Grating interferometry (GI)-based phase contrast X-ray CT is a promising technique which could make the transition to clin...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0272963&type=printable |
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| author | Stefano van Gogh Subhadip Mukherjee Jinqiu Xu Zhentian Wang Michał Rawlik Zsuzsanna Varga Rima Alaifari Carola-Bibiane Schönlieb Marco Stampanoni |
| author_facet | Stefano van Gogh Subhadip Mukherjee Jinqiu Xu Zhentian Wang Michał Rawlik Zsuzsanna Varga Rima Alaifari Carola-Bibiane Schönlieb Marco Stampanoni |
| author_sort | Stefano van Gogh |
| collection | DOAJ |
| description | Breast cancer remains the most prevalent malignancy in women in many countries around the world, thus calling for better imaging technologies to improve screening and diagnosis. Grating interferometry (GI)-based phase contrast X-ray CT is a promising technique which could make the transition to clinical practice and improve breast cancer diagnosis by combining the high three-dimensional resolution of conventional CT with higher soft-tissue contrast. Unfortunately though, obtaining high-quality images is challenging. Grating fabrication defects and photon starvation lead to high noise amplitudes in the measured data. Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this paper, we propose a novel regularized iterative reconstruction algorithm with an improved tomographic operator and a powerful data-driven regularizer to tackle this challenging inverse problem. Our algorithm combines the L-BFGS optimization scheme with a data-driven prior parameterized by a deep neural network. Importantly, we propose a novel regularization strategy to ensure that the trained network is non-expansive, which is critical for the convergence and stability analysis we provide. We empirically show that the proposed method achieves high quality images, both on simulated data as well as on real measurements. |
| format | Article |
| id | doaj-art-e52acdf35c7d4a569c75b36acdb27ece |
| institution | DOAJ |
| issn | 1932-6203 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj-art-e52acdf35c7d4a569c75b36acdb27ece2025-08-20T03:16:18ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01179e027296310.1371/journal.pone.0272963Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior.Stefano van GoghSubhadip MukherjeeJinqiu XuZhentian WangMichał RawlikZsuzsanna VargaRima AlaifariCarola-Bibiane SchönliebMarco StampanoniBreast cancer remains the most prevalent malignancy in women in many countries around the world, thus calling for better imaging technologies to improve screening and diagnosis. Grating interferometry (GI)-based phase contrast X-ray CT is a promising technique which could make the transition to clinical practice and improve breast cancer diagnosis by combining the high three-dimensional resolution of conventional CT with higher soft-tissue contrast. Unfortunately though, obtaining high-quality images is challenging. Grating fabrication defects and photon starvation lead to high noise amplitudes in the measured data. Moreover, the highly ill-conditioned differential nature of the GI-CT forward operator renders the inversion from corrupted data even more cumbersome. In this paper, we propose a novel regularized iterative reconstruction algorithm with an improved tomographic operator and a powerful data-driven regularizer to tackle this challenging inverse problem. Our algorithm combines the L-BFGS optimization scheme with a data-driven prior parameterized by a deep neural network. Importantly, we propose a novel regularization strategy to ensure that the trained network is non-expansive, which is critical for the convergence and stability analysis we provide. We empirically show that the proposed method achieves high quality images, both on simulated data as well as on real measurements.https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0272963&type=printable |
| spellingShingle | Stefano van Gogh Subhadip Mukherjee Jinqiu Xu Zhentian Wang Michał Rawlik Zsuzsanna Varga Rima Alaifari Carola-Bibiane Schönlieb Marco Stampanoni Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. PLoS ONE |
| title | Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. |
| title_full | Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. |
| title_fullStr | Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. |
| title_full_unstemmed | Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. |
| title_short | Iterative phase contrast CT reconstruction with novel tomographic operator and data-driven prior. |
| title_sort | iterative phase contrast ct reconstruction with novel tomographic operator and data driven prior |
| url | https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0272963&type=printable |
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