Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography
Objectives Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its i...
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Veröffentlicht in: | European radiology 2022-04, Vol.32 (4), p.2620-2628 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Objectives
Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification.
Methods
This prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm
3
) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis ( |
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ISSN: | 0938-7994 1432-1084 1432-1084 |
DOI: | 10.1007/s00330-021-08367-x |