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Low-radiation-dose CT:quantitative research for lung volume using iterative model reconstruction / 实用放射学杂志
Journal of Practical Radiology ; (12): 1600-1604, 2017.
Artículo en Chino | WPRIM (Pacífico Occidental) | ID: wpr-660285
Biblioteca responsable: WPRO
ABSTRACT
Objective To investigate the impact of quantitative measurement for lung volume using iterative model reconstruction (IMR),hybrid iterative reconstruction (iDose4 )and filtered back projection (FBP),and to compare the image noise between different reconstruction methods.Methods 70 subjects were performed with low-dose chest CT scan (Philips Brilliance 256 iCT),and the original data were reconstructed with IMR (algorithmRoutine,SharpPlus,Soft Tissue,level1 - 3 ),iDose4 and FBP respectively.We set less than -950 HU as emphysema threshold,calculated the total lung volume (TLV),total emphysema volume (TEV),emphysema index (EI)and objective image noise (OIN),and then compared the quantitative parameters and OIN between different groups.Results All parameters showed a significantly statistical difference (P =0.000)except TLV (P =1.000).The TEV and EI are significant higher in IMR-S group than in other groups.The OIN in IMR-S-L1 group was the highest,and the FBP group was the second-highest.OIN in iDose4 group was lower than that in IMR-S groups but higher than that in IMR-R and IMR-ST group.Conclusion SharpPlus algorithm of IMR will affect the quantitative measurement of lung volume under low-radiation-dose condition,and the OIN in IMR-S groups is obvious.Therefore SharpPlus algorithm is not recommended for quantitative analysis of lung volume.The Routine and Soft Tissue algorithm will not affect the quantitative measurement,and can distinctly reduce the OIN compared with idose4 and FBP.

Texto completo: Disponible Base de datos: WPRIM (Pacífico Occidental) Idioma: Chino Revista: Journal of Practical Radiology Año: 2017 Tipo del documento: Artículo
Texto completo: Disponible Base de datos: WPRIM (Pacífico Occidental) Idioma: Chino Revista: Journal of Practical Radiology Año: 2017 Tipo del documento: Artículo
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