Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta

To evaluate the impact of a novel, deep-learning-based image reconstruction (DLIR) algorithm on image quality in CT angiography of the aorta, we retrospectively analyzed 51 consecutive patients who underwent ECG-gated chest CT angiography and non-gated acquisition for the abdomen on a 256-dectector-...

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Autores principales:	Andra Heinrich, Felix Streckenbach, Ebba Beller, Justus Groß, Marc-André Weber, Felix G. Meinel
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning image processing angiography computed tomography aorta Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/abdf5f268faf4604bbc062a8cdd069f8
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spelling	oai:doaj.org-article:abdf5f268faf4604bbc062a8cdd069f82021-11-25T17:21:00ZDeep Learning-Based Image Reconstruction for CT Angiography of the Aorta10.3390/diagnostics111120372075-4418https://doaj.org/article/abdf5f268faf4604bbc062a8cdd069f82021-11-01T00:00:00Zhttps://www.mdpi.com/2075-4418/11/11/2037https://doaj.org/toc/2075-4418To evaluate the impact of a novel, deep-learning-based image reconstruction (DLIR) algorithm on image quality in CT angiography of the aorta, we retrospectively analyzed 51 consecutive patients who underwent ECG-gated chest CT angiography and non-gated acquisition for the abdomen on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction (ASIR-V) and DLIR. Intravascular image noise, the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) were quantified for the ascending aorta, the descending thoracic aorta, the abdominal aorta and the iliac arteries. Two readers scored subjective image quality on a five-point scale. Compared to ASIR-V, DLIR reduced the median image noise by 51–54% for the ascending aorta and the descending thoracic aorta. Correspondingly, median CNR roughly doubled for the ascending aorta and descending thoracic aorta. There was a 38% reduction in image noise for the abdominal aorta and the iliac arteries, with a corresponding improvement in CNR. Median subjective image quality improved from good to excellent at all anatomical levels. In CT angiography of the aorta, DLIR substantially improved objective and subjective image quality beyond what can be achieved by state-of-the-art iterative reconstruction. This can pave the way for further radiation or contrast dose reductions.Andra HeinrichFelix StreckenbachEbba BellerJustus GroßMarc-André WeberFelix G. MeinelMDPI AGarticledeep learningimage processingangiographycomputed tomographyaortaMedicine (General)R5-920ENDiagnostics, Vol 11, Iss 2037, p 2037 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning image processing angiography computed tomography aorta Medicine (General) R5-920
spellingShingle	deep learning image processing angiography computed tomography aorta Medicine (General) R5-920 Andra Heinrich Felix Streckenbach Ebba Beller Justus Groß Marc-André Weber Felix G. Meinel Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta
description	To evaluate the impact of a novel, deep-learning-based image reconstruction (DLIR) algorithm on image quality in CT angiography of the aorta, we retrospectively analyzed 51 consecutive patients who underwent ECG-gated chest CT angiography and non-gated acquisition for the abdomen on a 256-dectector-row CT. Images were reconstructed with adaptive statistical iterative reconstruction (ASIR-V) and DLIR. Intravascular image noise, the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) were quantified for the ascending aorta, the descending thoracic aorta, the abdominal aorta and the iliac arteries. Two readers scored subjective image quality on a five-point scale. Compared to ASIR-V, DLIR reduced the median image noise by 51–54% for the ascending aorta and the descending thoracic aorta. Correspondingly, median CNR roughly doubled for the ascending aorta and descending thoracic aorta. There was a 38% reduction in image noise for the abdominal aorta and the iliac arteries, with a corresponding improvement in CNR. Median subjective image quality improved from good to excellent at all anatomical levels. In CT angiography of the aorta, DLIR substantially improved objective and subjective image quality beyond what can be achieved by state-of-the-art iterative reconstruction. This can pave the way for further radiation or contrast dose reductions.
format	article
author	Andra Heinrich Felix Streckenbach Ebba Beller Justus Groß Marc-André Weber Felix G. Meinel
author_facet	Andra Heinrich Felix Streckenbach Ebba Beller Justus Groß Marc-André Weber Felix G. Meinel
author_sort	Andra Heinrich
title	Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta
title_short	Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta
title_full	Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta
title_fullStr	Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta
title_full_unstemmed	Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta
title_sort	deep learning-based image reconstruction for ct angiography of the aorta
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/abdf5f268faf4604bbc062a8cdd069f8
work_keys_str_mv	AT andraheinrich deeplearningbasedimagereconstructionforctangiographyoftheaorta AT felixstreckenbach deeplearningbasedimagereconstructionforctangiographyoftheaorta AT ebbabeller deeplearningbasedimagereconstructionforctangiographyoftheaorta AT justusgroß deeplearningbasedimagereconstructionforctangiographyoftheaorta AT marcandreweber deeplearningbasedimagereconstructionforctangiographyoftheaorta AT felixgmeinel deeplearningbasedimagereconstructionforctangiographyoftheaorta
_version_	1718412489839869952

Deep Learning-Based Image Reconstruction for CT Angiography of the Aorta

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