Cargando…
Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence
BACKGROUND: The image quality of computed tomography (CT) can be adversely affected by a low radiation dose, and reconstruction algorithms of an appropriate level may be useful in reducing this impact. METHODS: Eight sets of CT images of a phantom were reconstructed with filtered back projection (FB...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167467/ https://www.ncbi.nlm.nih.gov/pubmed/37179954 http://dx.doi.org/10.21037/qims-22-1227 |
_version_ | 1785038684291072000 |
---|---|
author | Yang, Chun Wang, Wenzhe Cui, Dingye Zhang, Jinliang Liu, Ling Wang, Yuxin Li, Wei |
author_facet | Yang, Chun Wang, Wenzhe Cui, Dingye Zhang, Jinliang Liu, Ling Wang, Yuxin Li, Wei |
author_sort | Yang, Chun |
collection | PubMed |
description | BACKGROUND: The image quality of computed tomography (CT) can be adversely affected by a low radiation dose, and reconstruction algorithms of an appropriate level may be useful in reducing this impact. METHODS: Eight sets of CT images of a phantom were reconstructed with filtered back projection (FBP); adaptive statistical iterative reconstruction-Veo (ASiR-V) at 30% (AV-30), 50% (AV-50), 80% (AV-80), and 100% (AV-100); and deep learning image reconstruction (DLIR) at low (DL-L), medium (DL-M), and high (DL-H) levels. The noise power spectrum (NPS) and task transfer function (TTF) were measured. Thirty consecutive patients underwent low-dose radiation contrast-enhanced abdominal CT scans that were reconstructed using FBP, AV-30, AV-50, AV-80, and AV-100, and three levels of DLIR. The standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the hepatic parenchyma and paraspinal muscle were evaluated. Two radiologists assessed the subjective image quality and lesion diagnostic confidence using a 5-point Likert scale. RESULTS: In the phantom study, both a higher DLIR and ASiR-V strength and a higher radiation dose led less noise. The NPS peak and average spatial frequency of the DLIR algorithms were closer to those of FBP, as the tube current increased and declined as the level of ASiR-V and DLIR strengthened. The NPS average spatial frequency of DL-L were higher than those of AISR-V. In clinical studies, AV-30 demonstrated a higher SD and lower SNR and CNR compared to DL-M and DL-H (P<0.05). For qualitative assessment, DL-M produced the highest qualitative image quality scores, with the exception of overall image noise (P<0.05). The NPS peak, average spatial frequency, and SD were the highest and the SNR, CNR, and subjective scores were the lowest with FBP. CONCLUSIONS: Compared with FBP and ASiR-V, DLIR provided better image quality and noise texture both in the phantom and clinical studies, and DL-M maintained the best image quality and lesion diagnostic confidence in low-dose radiation abdominal CT. |
format | Online Article Text |
id | pubmed-10167467 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-101674672023-05-10 Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence Yang, Chun Wang, Wenzhe Cui, Dingye Zhang, Jinliang Liu, Ling Wang, Yuxin Li, Wei Quant Imaging Med Surg Original Article BACKGROUND: The image quality of computed tomography (CT) can be adversely affected by a low radiation dose, and reconstruction algorithms of an appropriate level may be useful in reducing this impact. METHODS: Eight sets of CT images of a phantom were reconstructed with filtered back projection (FBP); adaptive statistical iterative reconstruction-Veo (ASiR-V) at 30% (AV-30), 50% (AV-50), 80% (AV-80), and 100% (AV-100); and deep learning image reconstruction (DLIR) at low (DL-L), medium (DL-M), and high (DL-H) levels. The noise power spectrum (NPS) and task transfer function (TTF) were measured. Thirty consecutive patients underwent low-dose radiation contrast-enhanced abdominal CT scans that were reconstructed using FBP, AV-30, AV-50, AV-80, and AV-100, and three levels of DLIR. The standard deviation (SD), signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the hepatic parenchyma and paraspinal muscle were evaluated. Two radiologists assessed the subjective image quality and lesion diagnostic confidence using a 5-point Likert scale. RESULTS: In the phantom study, both a higher DLIR and ASiR-V strength and a higher radiation dose led less noise. The NPS peak and average spatial frequency of the DLIR algorithms were closer to those of FBP, as the tube current increased and declined as the level of ASiR-V and DLIR strengthened. The NPS average spatial frequency of DL-L were higher than those of AISR-V. In clinical studies, AV-30 demonstrated a higher SD and lower SNR and CNR compared to DL-M and DL-H (P<0.05). For qualitative assessment, DL-M produced the highest qualitative image quality scores, with the exception of overall image noise (P<0.05). The NPS peak, average spatial frequency, and SD were the highest and the SNR, CNR, and subjective scores were the lowest with FBP. CONCLUSIONS: Compared with FBP and ASiR-V, DLIR provided better image quality and noise texture both in the phantom and clinical studies, and DL-M maintained the best image quality and lesion diagnostic confidence in low-dose radiation abdominal CT. AME Publishing Company 2023-03-28 2023-05-01 /pmc/articles/PMC10167467/ /pubmed/37179954 http://dx.doi.org/10.21037/qims-22-1227 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | Original Article Yang, Chun Wang, Wenzhe Cui, Dingye Zhang, Jinliang Liu, Ling Wang, Yuxin Li, Wei Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence |
title | Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence |
title_full | Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence |
title_fullStr | Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence |
title_full_unstemmed | Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence |
title_short | Deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence |
title_sort | deep learning image reconstruction algorithms in low-dose radiation abdominal computed tomography: assessment of image quality and lesion diagnostic confidence |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10167467/ https://www.ncbi.nlm.nih.gov/pubmed/37179954 http://dx.doi.org/10.21037/qims-22-1227 |
work_keys_str_mv | AT yangchun deeplearningimagereconstructionalgorithmsinlowdoseradiationabdominalcomputedtomographyassessmentofimagequalityandlesiondiagnosticconfidence AT wangwenzhe deeplearningimagereconstructionalgorithmsinlowdoseradiationabdominalcomputedtomographyassessmentofimagequalityandlesiondiagnosticconfidence AT cuidingye deeplearningimagereconstructionalgorithmsinlowdoseradiationabdominalcomputedtomographyassessmentofimagequalityandlesiondiagnosticconfidence AT zhangjinliang deeplearningimagereconstructionalgorithmsinlowdoseradiationabdominalcomputedtomographyassessmentofimagequalityandlesiondiagnosticconfidence AT liuling deeplearningimagereconstructionalgorithmsinlowdoseradiationabdominalcomputedtomographyassessmentofimagequalityandlesiondiagnosticconfidence AT wangyuxin deeplearningimagereconstructionalgorithmsinlowdoseradiationabdominalcomputedtomographyassessmentofimagequalityandlesiondiagnosticconfidence AT liwei deeplearningimagereconstructionalgorithmsinlowdoseradiationabdominalcomputedtomographyassessmentofimagequalityandlesiondiagnosticconfidence |