Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose

The employment of deep learning image reconstruction with AiCE presented a significant improvement in CNR and potential dose reduction in CT angiography. This study highlights the potential of AiCE to improve vascular image quality and decrease radiation exposure risk, thereby improving diagnostic precision and patient care in vascular imaging practices.

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Publication Details

Journal
Journal of X-Ray Science and Technology
Published
2024-12-18
DOI
https://doi.org/10.1177/08953996241301696
Citations
1
Primary Topic
Advanced X-ray and CT Imaging
Type
article
Field-Weighted Citation Impact
0.18
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article

Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose

Chang-Lae Lee
1 citations
Journal of X-Ray Science and Technology
Advanced X-ray and CT Imaging
0.18
article

Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose

Chang-Lae Lee
article en
1 citations

Abstract

The employment of deep learning image reconstruction with AiCE presented a significant improvement in CNR and potential dose reduction in CT angiography. This study highlights the potential of AiCE to improve vascular image quality and decrease radiation exposure risk, thereby improving diagnostic precision and patient care in vascular imaging practices.

Journal of X-Ray Science and TechnologyVol. 33(1)
Samsung (South Korea) (KR)
Openalex Percentile: Top 49%
Advanced X-ray and CT Imaging
0.18
DOISourceOpenAlex
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Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose — Chang-Lae Lee · Journal of X-Ray Science and Technology (2024) | TGRS Research Map | TGRS