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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2023
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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. |
format | Online Article Text |
id | pubmed-10270693 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT christiansencarolyn sinograminterpolationinspiredbysingleimagesuperresolution AT zenggengshengl sinograminterpolationinspiredbysingleimagesuperresolution |