Optimizing cardiac CT angiography minimum detectable difference via Taguchi's dynamic algorithm, a V-shaped line gauge, and three PMMA phantoms
1
Issued Date
2022-01-01
Resource Type
ISSN
09287329
Scopus ID
2-s2.0-85126072767
Pubmed ID
35124587
Journal Title
Technology and Health Care
Volume
30
Issue
S1
Start Page
S91
End Page
S103
Rights Holder(s)
SCOPUS
Bibliographic Citation
Technology and Health Care Vol.30 No.S1 (2022) , S91-S103
Suggested Citation
Pan L.F., Chen Y.H., Wang C.C., Peng B.R., Kittipayak S., Pan L.K. Optimizing cardiac CT angiography minimum detectable difference via Taguchi's dynamic algorithm, a V-shaped line gauge, and three PMMA phantoms. Technology and Health Care Vol.30 No.S1 (2022) , S91-S103. S103. doi:10.3233/THC-228009 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/83921
Title
Optimizing cardiac CT angiography minimum detectable difference via Taguchi's dynamic algorithm, a V-shaped line gauge, and three PMMA phantoms
Other Contributor(s)
Abstract
BACKGROUND: Radiologists widely use the minimum detectable difference (MDD) concept for inspecting the imaging quality and quantify the spatial resolution of scans. OBJECTIVE: This study adopted Taguchi's dynamic algorithm to optimize the MDD of cardiac CT angiography (CTA) using a V-shaped line gauge and three PMMA phantoms (50, 70, and 90 kg). METHODS: The phantoms were customized in compliance with the ICRU-48 report, whereas the V-shaped line gauge was indigenous to solidify the cardiac CTA scan image quality by two adjacent peaks along the V-shaped slit. Accordingly, the six factors A-F assigned in this study were A (kVp), B (mAs), C (CT pitch), D (FOV), E (iDose), and F (reconstruction filter). Since each factor could have two or three levels, eighteen groups of factor combinations were organized according to Taguchi's dynamic algorithm. Three welltrained radiologists ranked the CTA scan images three times for three different phantoms. Thus, 27 (3 × 3 × 3) ranked scores were summed and averaged to imply the integrated performance of one specific group, and eventually, 18 groups of CTA scan images were analyzed. The unique signal-to-noise ratio (S/N, dB) and sensitivity in the dynamic algorithm were calculated to reveal the true contribution of assigned factors and clarify the situation in routine CTA diagnosis. RESULTS: Minimizing the cross-interactions among factors, the optimal factor combination was found to be as follows: A (100 kVp), B (600 mAs), C (pitch 0.200 mm), D (FOV 280 mm), E (iDose 5), and F (filter XCA). The respective MDD values were 2.15, 2.32, and 1.87 mm for 50, 70, and 90 kg phantoms, respectively. The MDD of the 90 kg phantom had the most precise spatial resolution, while that of the 70 kg phantom was the worst. CONCLUSION: The Taguchi static and dynamic optimization algorithms were compared, and the latter's superiority was substantiated.