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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,...

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Autores principales: Nadkarni, Rohan, Clark, Darin P., Allphin, Alex J., Badea, Cristian T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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