Optimizing quality of digital mammographic imaging using Taguchi analysis with an ACR accreditation phantom

Abstract

This work demonstrated the improvement of the visualization of lesions by modulating the factors of an X-ray mammography imaging system using Taguchi analysis. Optimal combinations of X-ray operating factors in each group of level combination were determined using the Taguchi method, in which all factors were organized into only 18 groups, yielding analytical results with the same confidence as if each factor had been examined independently. The 4 considered operating factors of the X-ray machine were (1) anode material (target), (2) kVp, (3) mAs and (4) field of view (FOV). Each of these factors had 2 or 3 levels. Therefore, 54 (2£3£3£3 D 54) combinations were generated. The optimal settings were Rh as the target, 28 kVp, 80 mAs and 19£23 cm 2 FOV. The grade of exposed mammographic phantom image increased from the automatic exposure control (AEC) setting 70.92 to 72.00 under the optimal setting, meeting the minimum standard (70.00) set by Taiwan's Department of Health. The average glandular dose (AGD) of the exposed phantom, 0.182 cGy, was lower than that, 0.203 cGy, under the AEC setting. The Taguchi method was extremely promising for the design of imaging protocols in clinical diagnosis.

Taguchi dynamic analysis application to computer tomography number-mass density linear dependence optimization

“…The Taguchi dynamic analysis, as compared to other optimization techniques, determines both the optimal result from finite analytical data and the dominant factors involved in the optimization of q m -HU calibration from finite analytical data. This method has been widely applied in precise manufacturing [9,10] and other fields [11][12][13]. In this work, six essential factors in presetting the CT simulator were obtained as (1) scan time, (2) kVp, (3) mA, (4) Field of View (FOV), (5) slice thickness, and (6) imaging processing algorithm, the latter being defined as standard, detail, or soft one, respectively [14].…”

Optimizing cardiac CT angiography minimum detectable difference via Taguchi’s dynamic algorithm, a V-shaped line gauge, and three PMMA phantoms

“…The imaging quality of scans in medical facilities, such as X-ray, cardiac bi-plane X-ray, CT, CTA, mammography, and a gamma camera, is vital for the accurate diagnosis. Therefore, multiple techniques have been introduced to upgrade the spatial resolution, compromising the high quality of the acquired images and an acceptable exposed dose required for their provision [6][7][8][9][10][11][12][13]. Taguchi's optimization analysis became an assessable technique in providing quantified data for optimization due to its userfriendly and reliable realization.…”