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Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction

OBJECTIVE: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative...

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Autores principales: Otgonbaatar, Chuluunbaatar, Ryu, Jae-Kyun, Shin, Jaemin, Woo, Ji Young, Seo, Jung Wook, Shim, Hackjoon, Hwang, Dae Hyun
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Korean Society of Radiology 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614292/
https://www.ncbi.nlm.nih.gov/pubmed/36196766
http://dx.doi.org/10.3348/kjr.2022.0127
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author Otgonbaatar, Chuluunbaatar
Ryu, Jae-Kyun
Shin, Jaemin
Woo, Ji Young
Seo, Jung Wook
Shim, Hackjoon
Hwang, Dae Hyun
author_facet Otgonbaatar, Chuluunbaatar
Ryu, Jae-Kyun
Shin, Jaemin
Woo, Ji Young
Seo, Jung Wook
Shim, Hackjoon
Hwang, Dae Hyun
author_sort Otgonbaatar, Chuluunbaatar
collection PubMed
description OBJECTIVE: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods. MATERIALS AND METHODS: CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%–90% edge rise slope (ERS) and 10%–90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods. RESULTS: DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR. CONCLUSION: DLR reconstruction provided better images than FBP and hybrid IR reconstruction.
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spelling pubmed-96142922022-11-03 Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction Otgonbaatar, Chuluunbaatar Ryu, Jae-Kyun Shin, Jaemin Woo, Ji Young Seo, Jung Wook Shim, Hackjoon Hwang, Dae Hyun Korean J Radiol Cardiovascular Imaging OBJECTIVE: This study aimed to investigate whether a deep learning reconstruction (DLR) method improves the image quality, stent evaluation, and visibility of the valve apparatus in coronary computed tomography angiography (CCTA) when compared with filtered back projection (FBP) and hybrid iterative reconstruction (IR) methods. MATERIALS AND METHODS: CCTA images of 51 patients (mean age ± standard deviation [SD], 63.9 ± 9.8 years, 36 male) who underwent examination at a single institution were reconstructed using DLR, FBP, and hybrid IR methods and reviewed. CT attenuation, image noise, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and stent evaluation, including 10%–90% edge rise slope (ERS) and 10%–90% edge rise distance (ERD), were measured. Quantitative data are summarized as the mean ± SD. The subjective visual scores (1 for worst -5 for best) of the images were obtained for the following: overall image quality, image noise, and appearance of stent, vessel, and aortic and tricuspid valve apparatus (annulus, leaflets, papillary muscles, and chordae tendineae). These parameters were compared between the DLR, FBP, and hybrid IR methods. RESULTS: DLR provided higher Hounsfield unit (HU) values in the aorta and similar attenuation in the fat and muscle compared with FBP and hybrid IR. The image noise in HU was significantly lower in DLR (12.6 ± 2.2) than in hybrid IR (24.2 ± 3.0) and FBP (54.2 ± 9.5) (p < 0.001). The SNR and CNR were significantly higher in the DLR group than in the FBP and hybrid IR groups (p < 0.001). In the coronary stent, the mean value of ERS was significantly higher in DLR (1260.4 ± 242.5 HU/mm) than that of FBP (801.9 ± 170.7 HU/mm) and hybrid IR (641.9 ± 112.0 HU/mm). The mean value of ERD was measured as 0.8 ± 0.1 mm for DLR while it was 1.1 ± 0.2 mm for FBP and 1.1 ± 0.2 mm for hybrid IR. The subjective visual scores were higher in the DLR than in the images reconstructed with FBP and hybrid IR. CONCLUSION: DLR reconstruction provided better images than FBP and hybrid IR reconstruction. The Korean Society of Radiology 2022-11 2022-09-29 /pmc/articles/PMC9614292/ /pubmed/36196766 http://dx.doi.org/10.3348/kjr.2022.0127 Text en Copyright © 2022 The Korean Society of Radiology https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) ) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cardiovascular Imaging
Otgonbaatar, Chuluunbaatar
Ryu, Jae-Kyun
Shin, Jaemin
Woo, Ji Young
Seo, Jung Wook
Shim, Hackjoon
Hwang, Dae Hyun
Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction
title Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction
title_full Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction
title_fullStr Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction
title_full_unstemmed Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction
title_short Improvement in Image Quality and Visibility of Coronary Arteries, Stents, and Valve Structures on CT Angiography by Deep Learning Reconstruction
title_sort improvement in image quality and visibility of coronary arteries, stents, and valve structures on ct angiography by deep learning reconstruction
topic Cardiovascular Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614292/
https://www.ncbi.nlm.nih.gov/pubmed/36196766
http://dx.doi.org/10.3348/kjr.2022.0127
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