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Sinogram Interpolation Inspired by Single-Image Super Resolution

Computed tomography is a medical imaging procedure used to estimate the interior of a patient or an object. Radiation scans are taken at regularly spaced angles around the object, forming a sinogram. This sinogram is then reconstructed into an image representing the contents of the object. This resu...

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Autores principales: Christiansen, Carolyn, Zeng, Gengsheng L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270693/
https://www.ncbi.nlm.nih.gov/pubmed/37323429
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author Christiansen, Carolyn
Zeng, Gengsheng L.
author_facet Christiansen, Carolyn
Zeng, Gengsheng L.
author_sort Christiansen, Carolyn
collection PubMed
description Computed tomography is a medical imaging procedure used to estimate the interior of a patient or an object. Radiation scans are taken at regularly spaced angles around the object, forming a sinogram. This sinogram is then reconstructed into an image representing the contents of the object. This results in a fair amount of radiation exposure for the patient, which increases the risk of cancer. Less radiation and fewer views, however, leads to inferior image reconstruction. To solve this sparse-view problem, a deep-learning model is created that takes as input a sparse sinogram and outputs a sinogram with interpolated data for additional views. The architecture of this model is based on the super-resolution convolutional neural network. The reconstruction of model-interpolated sinograms has less mean-squared error than the reconstruction of the sparse sinogram. It also has less mean-squared error than a reconstruction of a sinogram interpolated using the popular bilinear image-resizing algorithm. This model can be easily adapted to different image sizes, and its simplicity translates into efficiency in both time and memory requirements.
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spelling pubmed-102706932023-06-15 Sinogram Interpolation Inspired by Single-Image Super Resolution Christiansen, Carolyn Zeng, Gengsheng L. J Biotechnol Appl Article Computed tomography is a medical imaging procedure used to estimate the interior of a patient or an object. Radiation scans are taken at regularly spaced angles around the object, forming a sinogram. This sinogram is then reconstructed into an image representing the contents of the object. This results in a fair amount of radiation exposure for the patient, which increases the risk of cancer. Less radiation and fewer views, however, leads to inferior image reconstruction. To solve this sparse-view problem, a deep-learning model is created that takes as input a sparse sinogram and outputs a sinogram with interpolated data for additional views. The architecture of this model is based on the super-resolution convolutional neural network. The reconstruction of model-interpolated sinograms has less mean-squared error than the reconstruction of the sparse sinogram. It also has less mean-squared error than a reconstruction of a sinogram interpolated using the popular bilinear image-resizing algorithm. This model can be easily adapted to different image sizes, and its simplicity translates into efficiency in both time and memory requirements. 2023 2023-05-15 /pmc/articles/PMC10270693/ /pubmed/37323429 Text en https://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution 4.0 International License
spellingShingle Article
Christiansen, Carolyn
Zeng, Gengsheng L.
Sinogram Interpolation Inspired by Single-Image Super Resolution
title Sinogram Interpolation Inspired by Single-Image Super Resolution
title_full Sinogram Interpolation Inspired by Single-Image Super Resolution
title_fullStr Sinogram Interpolation Inspired by Single-Image Super Resolution
title_full_unstemmed Sinogram Interpolation Inspired by Single-Image Super Resolution
title_short Sinogram Interpolation Inspired by Single-Image Super Resolution
title_sort sinogram interpolation inspired by single-image super resolution
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10270693/
https://www.ncbi.nlm.nih.gov/pubmed/37323429
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