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Objective: To optimize the minimum detectable difference (MDD) of a cardiac X-ray imaging system using the Taguchi L₈(2⁷) analysis and a precise line pair (LP) gauge. Methods: The optimal combination of the four critical factors of the cardiac X-ray imaging system, namely X-ray focus, kilovoltage (kVp), milliamper-seconds (mAs) and source image distance (SID), providing the MDD was calculated via the Taguchi analysis and experimentally verified. Two (low and high) levels were assigned for each factor, and eight combinations of four factors were used to acquire instant X-ray images using an NDT commercial LP gauge (with a gauge length of 64mm and a width of $3.5$mm). The latter had five lines and was split gradually from top to bottom for the inspection of X-ray images, whose quality was ranked by three well-trained radiologists according to the double-blind criterion. The ranking grade was given by sharp contrast, low noise and precision to distinguish the LP. Accordingly, the MDD was derived to represent the spatial resolution of instant X-ray images by the revised Student’s $t$-test analysis. The optimal combination of factors was experimentally identified and clinically verified in the follow-up inspections. Results: For the conventional setting, Group No. 7 (which obtained the highest grade among eight groups) and the optimal setting, the obtained MDD values were $0.183$, $0.167$ and $0.157$mm, respectively, while the LP (line pair/mm) interpolated from the gauge scale amounted to $2.7$, $3.1$ and $3.2$LP/mm, respectively. Conclusion: The Taguchi L₈ analysis was proved to be instrumental in optimizing the cardiac X-ray imaging system MDD and is recommended to be used jointly with the revised Student’s $t$-test analysis for improving the spatial resolution of instant X-ray images.

Objective: The minimum detectable difference (MDD) of computed tomography (CT) scanned images was quantified and optimized according to an indigenous hepatic phantom, line group gauge and Taguchi ${\text{L}}_{18}$ optimization analysis in this work. Methods: Optimal combinations of CT scan factors in every group with the level organization were judged using the Taguchi analysis, in which every factor was organized into only 18 groups, creating evaluated outcomes with the same confidence as if every factor was analyzed independently. The five practical factors of the CT scan were (1) kVp, (2) mAs, (3) pitch increment, (4) field of view (FOV) and (5) rotation time for one loop of CT scan. Insofar as each factor had two or three levels, the total number of 162 (i.e., $2\times 3\times 3\times 3\times 3$) combinations was considered. Results: The optimal setting was 120kVp, 300mAs, 0.641 pitch, 320mm FOV and 1.0s of rotation time of CT scan. The minimal MDD was 2.65mm under 0.39mm of the slit depth from the revised Student’s $t$-test with a 95% confidence level. In contrast, the MDD of conventional and the best one (no. 7) among all original 18 groups were 3.27mm and 2.93mm for 0.43mm and 0.41mm slit depths, respectively. Conclusion: The Taguchi analysis was found very lucrative for the design of imaging analysis in practical diagnosis. The indigenous line group gauge and hepatic phantom also proved to be suitable in simulating the human body in real hepatic carcinoma examination.

In this study, a projection of effective blood concentration (EBC) readings of digoxin is made using the inverse problem algorithm based on clinical data for patients with heart failure diseases. Seven factors, including body surface area (BSA), blood urine nitrogen (BUN), creatinine, sodium (Na), potassium (K), magnesium (Mg) ion readings, and mean arterial pressure (MAP) were compiled with nonlinear regression fit to develop a projection function having 29 terms obtained from an inverse problem algorithm via the default function run in STATISTICA. Accordingly, data collected from the clinical 168 heart failure patients were normalized to be included in same domain range ($-$1 to +1), and then calculated by the specific algorithm to optimize the numerical solution to evaluate EBC readings of digoxin. The evaluated first-order regression fit owned an optimal loss function ($\mathrm{\Phi }=2.1746$) coupled with correlation coefficient $r^2$ = 0.892 and variance of 89.20%. Furthermore, 45 patients having similar clinical syndromes were also adopted to verify the projection and implied with high agreement. The BUN factor dominated the projection and defined as the most significant coefficient in the analysis, and K ion, MAP, BSA, and Mg ion factors exhibited minor contributions to the projection. The repeated trials to lower number of factors from seven to a smaller number (namely 6, 5, 4, 3, 2, and 1) for simplifying method but resulting with unaccepted outcomes, with high loss function values and low linearity. However, the algorithm held its accuracy to handle the verified data that were out of the original bounds. The proposed algorithm demonstrated a useful analysis to handle the drug administration in pharmaceutical field.