Cargando…
Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience
A super-resolution deep learning reconstruction (SR-DLR) algorithm trained using data acquired on the ultrahigh spatial resolution computed tomography (UHRCT) has the potential to provide better image quality of coronary arteries on the whole-heart, single-rotation cardiac coverage on a 320-detector...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617195/ https://www.ncbi.nlm.nih.gov/pubmed/37904089 http://dx.doi.org/10.1186/s12880-023-01139-7 |
_version_ | 1785129554857164800 |
---|---|
author | Orii, Makoto Sone, Misato Osaki, Takeshi Ueyama, Yuta Chiba, Takuya Sasaki, Tadashi Yoshioka, Kunihiro |
author_facet | Orii, Makoto Sone, Misato Osaki, Takeshi Ueyama, Yuta Chiba, Takuya Sasaki, Tadashi Yoshioka, Kunihiro |
author_sort | Orii, Makoto |
collection | PubMed |
description | A super-resolution deep learning reconstruction (SR-DLR) algorithm trained using data acquired on the ultrahigh spatial resolution computed tomography (UHRCT) has the potential to provide better image quality of coronary arteries on the whole-heart, single-rotation cardiac coverage on a 320-detector row CT scanner. However, the advantages of SR-DLR at coronary computed tomography angiography (CCTA) have not been fully investigated. The present study aimed to compare the image quality of the coronary arteries and in-stent lumen between SR-DLR and model-based iterative reconstruction (MBIR). We prospectively enrolled 70 patients (median age, 69 years; interquartile range [IQR], 59–75 years; 50 men) who underwent CCTA using a 320-detector row CT scanner between January and August 2022. The image noise in the ascending aorta, left atrium, and septal wall of the ventricle was measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the proximal coronary arteries were calculated. Of the twenty stents, stent strut thickness and luminal diameter were quantitatively evaluated. The image noise on SR-DLR was significantly lower than that on MBIR (median 22.1 HU; IQR, 19.3–24.9 HU vs. 27.4 HU; IQR, 24.2–31.2 HU, p < 0.01), whereas the SNR (median 16.3; IQR, 11.8–21.8 vs. 13.7; IQR, 9.9–18.4, p = 0.01) and CNR (median 24.4; IQR, 15.5–30.2 vs. 19.2; IQR, 14.1–23.2, p < 0.01) on SR-DLR were significantly higher than that on MBIR. Stent struts were significantly thinner (median, 0.68 mm; IQR, 0.61–0.78 mm vs. 0.81 mm; IQR, 0.72–0.96 mm, p < 0.01) and in-stent lumens were significantly larger (median, 1.84 mm; IQR, 1.65–2.26 mm vs. 1.52 mm; IQR, 1.28–2.25 mm, p < 0.01) on SR-DLR than on MBIR. Although further large-scale studies using invasive coronary angiography as the reference standard, comparative studies with UHRCT, and studies in more challenging population for CCTA are needed, this study’s initial experience with SR-DLR would improve the utility of CCTA in daily clinical practice due to the better image quality of the coronary arteries and in-stent lumen at CCTA compared with conventional MBIR. |
format | Online Article Text |
id | pubmed-10617195 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106171952023-11-01 Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience Orii, Makoto Sone, Misato Osaki, Takeshi Ueyama, Yuta Chiba, Takuya Sasaki, Tadashi Yoshioka, Kunihiro BMC Med Imaging Research A super-resolution deep learning reconstruction (SR-DLR) algorithm trained using data acquired on the ultrahigh spatial resolution computed tomography (UHRCT) has the potential to provide better image quality of coronary arteries on the whole-heart, single-rotation cardiac coverage on a 320-detector row CT scanner. However, the advantages of SR-DLR at coronary computed tomography angiography (CCTA) have not been fully investigated. The present study aimed to compare the image quality of the coronary arteries and in-stent lumen between SR-DLR and model-based iterative reconstruction (MBIR). We prospectively enrolled 70 patients (median age, 69 years; interquartile range [IQR], 59–75 years; 50 men) who underwent CCTA using a 320-detector row CT scanner between January and August 2022. The image noise in the ascending aorta, left atrium, and septal wall of the ventricle was measured, and the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) in the proximal coronary arteries were calculated. Of the twenty stents, stent strut thickness and luminal diameter were quantitatively evaluated. The image noise on SR-DLR was significantly lower than that on MBIR (median 22.1 HU; IQR, 19.3–24.9 HU vs. 27.4 HU; IQR, 24.2–31.2 HU, p < 0.01), whereas the SNR (median 16.3; IQR, 11.8–21.8 vs. 13.7; IQR, 9.9–18.4, p = 0.01) and CNR (median 24.4; IQR, 15.5–30.2 vs. 19.2; IQR, 14.1–23.2, p < 0.01) on SR-DLR were significantly higher than that on MBIR. Stent struts were significantly thinner (median, 0.68 mm; IQR, 0.61–0.78 mm vs. 0.81 mm; IQR, 0.72–0.96 mm, p < 0.01) and in-stent lumens were significantly larger (median, 1.84 mm; IQR, 1.65–2.26 mm vs. 1.52 mm; IQR, 1.28–2.25 mm, p < 0.01) on SR-DLR than on MBIR. Although further large-scale studies using invasive coronary angiography as the reference standard, comparative studies with UHRCT, and studies in more challenging population for CCTA are needed, this study’s initial experience with SR-DLR would improve the utility of CCTA in daily clinical practice due to the better image quality of the coronary arteries and in-stent lumen at CCTA compared with conventional MBIR. BioMed Central 2023-10-30 /pmc/articles/PMC10617195/ /pubmed/37904089 http://dx.doi.org/10.1186/s12880-023-01139-7 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Orii, Makoto Sone, Misato Osaki, Takeshi Ueyama, Yuta Chiba, Takuya Sasaki, Tadashi Yoshioka, Kunihiro Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience |
title | Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience |
title_full | Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience |
title_fullStr | Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience |
title_full_unstemmed | Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience |
title_short | Super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience |
title_sort | super-resolution deep learning reconstruction at coronary computed tomography angiography to evaluate the coronary arteries and in-stent lumen: an initial experience |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10617195/ https://www.ncbi.nlm.nih.gov/pubmed/37904089 http://dx.doi.org/10.1186/s12880-023-01139-7 |
work_keys_str_mv | AT oriimakoto superresolutiondeeplearningreconstructionatcoronarycomputedtomographyangiographytoevaluatethecoronaryarteriesandinstentlumenaninitialexperience AT sonemisato superresolutiondeeplearningreconstructionatcoronarycomputedtomographyangiographytoevaluatethecoronaryarteriesandinstentlumenaninitialexperience AT osakitakeshi superresolutiondeeplearningreconstructionatcoronarycomputedtomographyangiographytoevaluatethecoronaryarteriesandinstentlumenaninitialexperience AT ueyamayuta superresolutiondeeplearningreconstructionatcoronarycomputedtomographyangiographytoevaluatethecoronaryarteriesandinstentlumenaninitialexperience AT chibatakuya superresolutiondeeplearningreconstructionatcoronarycomputedtomographyangiographytoevaluatethecoronaryarteriesandinstentlumenaninitialexperience AT sasakitadashi superresolutiondeeplearningreconstructionatcoronarycomputedtomographyangiographytoevaluatethecoronaryarteriesandinstentlumenaninitialexperience AT yoshiokakunihiro superresolutiondeeplearningreconstructionatcoronarycomputedtomographyangiographytoevaluatethecoronaryarteriesandinstentlumenaninitialexperience |