Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Pula, Michal, Kucharczyk, Emilia, Zdanowicz, Agata, Guzinski, Maciej
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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
_version_ 1785097305099075584
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
work_keys_str_mv AT pulamichal imagequalityimprovementindeeplearningimagereconstructionofheadcomputedtomographyexamination
AT kucharczykemilia imagequalityimprovementindeeplearningimagereconstructionofheadcomputedtomographyexamination
AT zdanowiczagata imagequalityimprovementindeeplearningimagereconstructionofheadcomputedtomographyexamination
AT guzinskimaciej imagequalityimprovementindeeplearningimagereconstructionofheadcomputedtomographyexamination