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An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention

BACKGROUND: Cone-beam computed tomography (CBCT) acquisition during endovascular aneurysm repair is an emergent technology with more and more applications. It may provide 3-D information to achieve guidance of intervention. However, there is growing concern on the overall radiation doses delivered t...

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Autores principales: Liu, Yi, Castro, Miguel, Lederlin, Mathieu, Kaladji, Adrien, Haigron, Pascal
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6122689/
https://www.ncbi.nlm.nih.gov/pubmed/30180820
http://dx.doi.org/10.1186/s12880-018-0269-1
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author Liu, Yi
Castro, Miguel
Lederlin, Mathieu
Kaladji, Adrien
Haigron, Pascal
author_facet Liu, Yi
Castro, Miguel
Lederlin, Mathieu
Kaladji, Adrien
Haigron, Pascal
author_sort Liu, Yi
collection PubMed
description BACKGROUND: Cone-beam computed tomography (CBCT) acquisition during endovascular aneurysm repair is an emergent technology with more and more applications. It may provide 3-D information to achieve guidance of intervention. However, there is growing concern on the overall radiation doses delivered to patients, thus a low dose protocol is called when scanning. But CBCT images with a low dose protocol are degraded, resulting in streak artifacts and decreased contrast-to-noise ratio (CNR). In this paper, a Laplacian pyramid-based nonlinear diffusion is proposed to improve the quality of CBCT images. METHOD: We first transform the CBCT image into its pyramid domain, then a modified nonlinear diffusion is performed in each level to remove noise across edges while keeping edges as far as possible. The improved diffusion coefficient is a function of the gradient magnitude image; the threshold in the modified diffusion function is estimated using the median absolute deviation (MAD) estimator; the time step is automatically determined by iterative image changes and the iteration is stopped according to mean absolute error between two adjacent diffusions. Finally, we reconstruct the Laplacian pyramid using the processed pyramid images in each level. RESULT: Results from simulation show that the filtered image from the proposed method has the highest peak signal-noise ratio (81.92), the highest correlation coefficient (99.77%) and the lowest mean square error (27.61), compared with the other four methods. In addition, it has highest contrast-to-noise ratio and sharpness in ROIs. Results from real CBCT images show that the proposed method shows better smoothness in homogeneous regions meanwhile keeps bony structures clear. CONCLUSION: Simulation and patient studies show that the proposed method has a good tradeoff between noise/artifacts suppression and edge preservation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12880-018-0269-1) contains supplementary material, which is available to authorized users.
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spelling pubmed-61226892018-09-10 An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention Liu, Yi Castro, Miguel Lederlin, Mathieu Kaladji, Adrien Haigron, Pascal BMC Med Imaging Research Article BACKGROUND: Cone-beam computed tomography (CBCT) acquisition during endovascular aneurysm repair is an emergent technology with more and more applications. It may provide 3-D information to achieve guidance of intervention. However, there is growing concern on the overall radiation doses delivered to patients, thus a low dose protocol is called when scanning. But CBCT images with a low dose protocol are degraded, resulting in streak artifacts and decreased contrast-to-noise ratio (CNR). In this paper, a Laplacian pyramid-based nonlinear diffusion is proposed to improve the quality of CBCT images. METHOD: We first transform the CBCT image into its pyramid domain, then a modified nonlinear diffusion is performed in each level to remove noise across edges while keeping edges as far as possible. The improved diffusion coefficient is a function of the gradient magnitude image; the threshold in the modified diffusion function is estimated using the median absolute deviation (MAD) estimator; the time step is automatically determined by iterative image changes and the iteration is stopped according to mean absolute error between two adjacent diffusions. Finally, we reconstruct the Laplacian pyramid using the processed pyramid images in each level. RESULT: Results from simulation show that the filtered image from the proposed method has the highest peak signal-noise ratio (81.92), the highest correlation coefficient (99.77%) and the lowest mean square error (27.61), compared with the other four methods. In addition, it has highest contrast-to-noise ratio and sharpness in ROIs. Results from real CBCT images show that the proposed method shows better smoothness in homogeneous regions meanwhile keeps bony structures clear. CONCLUSION: Simulation and patient studies show that the proposed method has a good tradeoff between noise/artifacts suppression and edge preservation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12880-018-0269-1) contains supplementary material, which is available to authorized users. BioMed Central 2018-09-04 /pmc/articles/PMC6122689/ /pubmed/30180820 http://dx.doi.org/10.1186/s12880-018-0269-1 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Liu, Yi
Castro, Miguel
Lederlin, Mathieu
Kaladji, Adrien
Haigron, Pascal
An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention
title An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention
title_full An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention
title_fullStr An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention
title_full_unstemmed An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention
title_short An improved nonlinear diffusion in Laplacian pyramid domain for cone beam CT denoising during image-guided vascular intervention
title_sort improved nonlinear diffusion in laplacian pyramid domain for cone beam ct denoising during image-guided vascular intervention
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6122689/
https://www.ncbi.nlm.nih.gov/pubmed/30180820
http://dx.doi.org/10.1186/s12880-018-0269-1
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