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A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images
Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensive computation time. To overcome this limitation,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366887/ https://www.ncbi.nlm.nih.gov/pubmed/37489470 http://dx.doi.org/10.3390/tomography9040102 |
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author | Nadkarni, Rohan Clark, Darin P. Allphin, Alex J. Badea, Cristian T. |
author_facet | Nadkarni, Rohan Clark, Darin P. Allphin, Alex J. Badea, Cristian T. |
author_sort | Nadkarni, Rohan |
collection | PubMed |
description | Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensive computation time. To overcome this limitation, we propose a deep learning (DL) model, UnetU, which quickly estimates iterative reconstruction from wFBP. Utilizing a 2D U-net convolutional neural network (CNN) with a custom loss function and transformation of wFBP, UnetU promotes accurate material decomposition across various photon-counting detector (PCD) energy threshold settings. UnetU outperformed multi-energy non-local means (ME NLM) and a conventional denoising CNN called UnetwFBP in terms of root mean square error (RMSE) in test set reconstructions and their respective matrix inversion material decompositions. Qualitative results in reconstruction and material decomposition domains revealed that UnetU is the best approximation of iterative reconstruction. In reconstructions with varying undersampling factors from a high dose ex vivo scan, UnetU consistently gave higher structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) to the fully sampled iterative reconstruction than ME NLM and UnetwFBP. This research demonstrates UnetU’s potential as a fast (i.e., 15 times faster than iterative reconstruction) and generalizable approach for PCCT denoising, holding promise for advancing preclinical PCCT research. |
format | Online Article Text |
id | pubmed-10366887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103668872023-07-26 A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images Nadkarni, Rohan Clark, Darin P. Allphin, Alex J. Badea, Cristian T. Tomography Article Photon-counting CT (PCCT) is powerful for spectral imaging and material decomposition but produces noisy weighted filtered backprojection (wFBP) reconstructions. Although iterative reconstruction effectively denoises these images, it requires extensive computation time. To overcome this limitation, we propose a deep learning (DL) model, UnetU, which quickly estimates iterative reconstruction from wFBP. Utilizing a 2D U-net convolutional neural network (CNN) with a custom loss function and transformation of wFBP, UnetU promotes accurate material decomposition across various photon-counting detector (PCD) energy threshold settings. UnetU outperformed multi-energy non-local means (ME NLM) and a conventional denoising CNN called UnetwFBP in terms of root mean square error (RMSE) in test set reconstructions and their respective matrix inversion material decompositions. Qualitative results in reconstruction and material decomposition domains revealed that UnetU is the best approximation of iterative reconstruction. In reconstructions with varying undersampling factors from a high dose ex vivo scan, UnetU consistently gave higher structural similarity (SSIM) and peak signal-to-noise ratio (PSNR) to the fully sampled iterative reconstruction than ME NLM and UnetwFBP. This research demonstrates UnetU’s potential as a fast (i.e., 15 times faster than iterative reconstruction) and generalizable approach for PCCT denoising, holding promise for advancing preclinical PCCT research. MDPI 2023-07-02 /pmc/articles/PMC10366887/ /pubmed/37489470 http://dx.doi.org/10.3390/tomography9040102 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Nadkarni, Rohan Clark, Darin P. Allphin, Alex J. Badea, Cristian T. A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images |
title | A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images |
title_full | A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images |
title_fullStr | A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images |
title_full_unstemmed | A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images |
title_short | A Deep Learning Approach for Rapid and Generalizable Denoising of Photon-Counting Micro-CT Images |
title_sort | deep learning approach for rapid and generalizable denoising of photon-counting micro-ct images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10366887/ https://www.ncbi.nlm.nih.gov/pubmed/37489470 http://dx.doi.org/10.3390/tomography9040102 |
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