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A Subband-Specific Deconvolution Model for MTF Improvement in CT

The purpose of this research is to achieve uniform spatial resolution in CT (computed tomography) images without hardware modification. The main idea of this study is to consider geometry optics model, which can provide the approximate blurring PSF (point spread function) kernel, which varies accord...

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Detalles Bibliográficos
Autores principales: Han, Seokmin, Choi, Kihwan, Yoo, Sang Wook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5823483/
https://www.ncbi.nlm.nih.gov/pubmed/29576861
http://dx.doi.org/10.1155/2017/2193635
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author Han, Seokmin
Choi, Kihwan
Yoo, Sang Wook
author_facet Han, Seokmin
Choi, Kihwan
Yoo, Sang Wook
author_sort Han, Seokmin
collection PubMed
description The purpose of this research is to achieve uniform spatial resolution in CT (computed tomography) images without hardware modification. The main idea of this study is to consider geometry optics model, which can provide the approximate blurring PSF (point spread function) kernel, which varies according to the distance from X-ray tube to each pixel. The FOV (field of view) was divided into several band regions based on the distance from X-ray source, and each region was deconvolved with different deconvolution kernels. Though more precise calculation for the PSF for deconvolution is possible as the number of subbands increases, we set the number of subbands to 11. 11 subband settings seem to be a balancing point to reduce noise boost, while MTF (modulation transfer function) increase still remains. As the results show, subband-wise deconvolution makes image resolution (in terms of MTF) relatively uniform across the FOV. The results show that spatial resolution in CT images can be uniform across the FOV without using additional equipment. The beauty of this method is that it can be applied to any CT system as long as we know the specific system parameters and determine the appropriate PSF for deconvolution maps of the system. The proposed algorithm shows promising result in improving spatial resolution uniformity while avoiding the excessive noise boost.
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spelling pubmed-58234832018-03-25 A Subband-Specific Deconvolution Model for MTF Improvement in CT Han, Seokmin Choi, Kihwan Yoo, Sang Wook J Healthc Eng Research Article The purpose of this research is to achieve uniform spatial resolution in CT (computed tomography) images without hardware modification. The main idea of this study is to consider geometry optics model, which can provide the approximate blurring PSF (point spread function) kernel, which varies according to the distance from X-ray tube to each pixel. The FOV (field of view) was divided into several band regions based on the distance from X-ray source, and each region was deconvolved with different deconvolution kernels. Though more precise calculation for the PSF for deconvolution is possible as the number of subbands increases, we set the number of subbands to 11. 11 subband settings seem to be a balancing point to reduce noise boost, while MTF (modulation transfer function) increase still remains. As the results show, subband-wise deconvolution makes image resolution (in terms of MTF) relatively uniform across the FOV. The results show that spatial resolution in CT images can be uniform across the FOV without using additional equipment. The beauty of this method is that it can be applied to any CT system as long as we know the specific system parameters and determine the appropriate PSF for deconvolution maps of the system. The proposed algorithm shows promising result in improving spatial resolution uniformity while avoiding the excessive noise boost. Hindawi 2017 2017-10-25 /pmc/articles/PMC5823483/ /pubmed/29576861 http://dx.doi.org/10.1155/2017/2193635 Text en Copyright © 2017 Seokmin Han et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Han, Seokmin
Choi, Kihwan
Yoo, Sang Wook
A Subband-Specific Deconvolution Model for MTF Improvement in CT
title A Subband-Specific Deconvolution Model for MTF Improvement in CT
title_full A Subband-Specific Deconvolution Model for MTF Improvement in CT
title_fullStr A Subband-Specific Deconvolution Model for MTF Improvement in CT
title_full_unstemmed A Subband-Specific Deconvolution Model for MTF Improvement in CT
title_short A Subband-Specific Deconvolution Model for MTF Improvement in CT
title_sort subband-specific deconvolution model for mtf improvement in ct
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5823483/
https://www.ncbi.nlm.nih.gov/pubmed/29576861
http://dx.doi.org/10.1155/2017/2193635
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