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Comparison of Filtered Back Projection, Hybrid Iterative Reconstruction, Model-Based Iterative Reconstruction, and Virtual Monoenergetic Reconstruction Images at Both Low- and Standard-Dose Settings in Measurement of Emphysema Volume and Airway Wall Thick
Korean Journal of Radiology ; : 809-817, 2018.
Article in English | WPRIM | ID: wpr-716327
ABSTRACT

OBJECTIVE:

To evaluate the accuracy of emphysema volume (EV) and airway measurements (AMs) produced by various iterative reconstruction (IR) algorithms and virtual monoenergetic images (VME) at both low- and standard-dose settings. MATERIALS AND

METHODS:

Computed tomography (CT) images were obtained on phantom at both low- (30 mAs at 120 kVp) and standard-doses (100 mAs at 120 kVp). Each CT scan was reconstructed using filtered back projection, hybrid IR (iDose4; Philips Healthcare), model-based IR (IMR-R1, IMR-ST1, IMR-SP1; Philips Healthcare), and VME at 70 keV (VME70). The EV of each air column and wall area percentage (WA%) of each airway tube were measured in all algorithms. Absolute percentage measurement errors of EV (APEvol) and AM (APEWA%) were then calculated.

RESULTS:

Emphysema volume was most accurately measured in IMR-R1 (APEvol in low-dose, 0.053 ± 0.002; APEvol in standard-dose, 0.047 ± 0.003; all p 0.05). VME70 showed a significantly higher APEvol than iDose4, IMR-R1, and IMR-ST1 (all p < 0.004). VME70 also showed a significantly higher APEWA% compared with the other algorithms (all p < 0.001).

CONCLUSION:

IMR was the most accurate technique for measurement of both EV and airway wall thickness. However, VME70 did not show a significantly better accuracy compared with other algorithms.
Subject(s)

Full text: Available Index: WPRIM (Western Pacific) Main subject: Tomography, X-Ray Computed / Emphysema Language: English Journal: Korean Journal of Radiology Year: 2018 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Main subject: Tomography, X-Ray Computed / Emphysema Language: English Journal: Korean Journal of Radiology Year: 2018 Type: Article