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Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination
In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomography) scans of the head. The CT scans of 109 patie...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459011/ https://www.ncbi.nlm.nih.gov/pubmed/37624111 http://dx.doi.org/10.3390/tomography9040118 |
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author | Pula, Michal Kucharczyk, Emilia Zdanowicz, Agata Guzinski, Maciej |
author_facet | Pula, Michal Kucharczyk, Emilia Zdanowicz, Agata Guzinski, Maciej |
author_sort | Pula, Michal |
collection | PubMed |
description | In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomography) scans of the head. The CT scans of 109 patients were subjected to both objective and subjective evaluation of image quality. The objective evaluation was based on the SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) of the brain’s gray and white matter. The regions of interest for our study were set in the BGA (basal ganglia area) and PCF (posterior cranial fossa). Simultaneously, a subjective assessment of image quality, based on brain structure visibility, was conducted by experienced radiologists. In the assessed scans, we obtained up to a 54% increase in SNR for gray matter and a 60% increase for white matter using DLIR in comparison to ASIR-V. Moreover, we achieved a CNR increment of 58% in the BGA structures and 50% in the PCF. In the subjective assessment of the obtained images, DLIR had a mean rating score of 2.8, compared to the mean score of 2.6 for ASIR-V images. In conclusion, DLIR shows improved image quality compared to the standard iterative reconstruction of CT images of the head. |
format | Online Article Text |
id | pubmed-10459011 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104590112023-08-27 Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination Pula, Michal Kucharczyk, Emilia Zdanowicz, Agata Guzinski, Maciej Tomography Article In this study, we assess image quality in computed tomography scans reconstructed via DLIR (Deep Learning Image Reconstruction) and compare it with iterative reconstruction ASIR-V (Adaptive Statistical Iterative Reconstruction) in CT (computed tomography) scans of the head. The CT scans of 109 patients were subjected to both objective and subjective evaluation of image quality. The objective evaluation was based on the SNR (signal-to-noise ratio) and CNR (contrast-to-noise ratio) of the brain’s gray and white matter. The regions of interest for our study were set in the BGA (basal ganglia area) and PCF (posterior cranial fossa). Simultaneously, a subjective assessment of image quality, based on brain structure visibility, was conducted by experienced radiologists. In the assessed scans, we obtained up to a 54% increase in SNR for gray matter and a 60% increase for white matter using DLIR in comparison to ASIR-V. Moreover, we achieved a CNR increment of 58% in the BGA structures and 50% in the PCF. In the subjective assessment of the obtained images, DLIR had a mean rating score of 2.8, compared to the mean score of 2.6 for ASIR-V images. In conclusion, DLIR shows improved image quality compared to the standard iterative reconstruction of CT images of the head. MDPI 2023-08-09 /pmc/articles/PMC10459011/ /pubmed/37624111 http://dx.doi.org/10.3390/tomography9040118 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 Pula, Michal Kucharczyk, Emilia Zdanowicz, Agata Guzinski, Maciej Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination |
title | Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination |
title_full | Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination |
title_fullStr | Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination |
title_full_unstemmed | Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination |
title_short | Image Quality Improvement in Deep Learning Image Reconstruction of Head Computed Tomography Examination |
title_sort | image quality improvement in deep learning image reconstruction of head computed tomography examination |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10459011/ https://www.ncbi.nlm.nih.gov/pubmed/37624111 http://dx.doi.org/10.3390/tomography9040118 |
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