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 [Formula: see text] 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., [Formula: see text]) combinations was considered. Results: The optimal setting was 120[Formula: see text]kVp, 300[Formula: see text]mAs, 0.641 pitch, 320[Formula: see text]mm FOV and 1.0[Formula: see text]s of rotation time of CT scan. The minimal MDD was 2.65[Formula: see text]mm under 0.39[Formula: see text]mm of the slit depth from the revised Student’s [Formula: see text]-test with a 95% confidence level. In contrast, the MDD of conventional and the best one (no. 7) among all original 18 groups were 3.27[Formula: see text]mm and 2.93[Formula: see text]mm for 0.43[Formula: see text]mm and 0.41[Formula: see text]mm 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.
A five-compartmental biokinetic model of I-131 radioiodine based on in-vivo gamma camera scanning results was developed and successfully applied to nine thyroid cancer patients who were administered 1,110 MBq I-131 in capsules for the residual thyroid gland ablation. The I-131 solution activity among internal organs was analyzed via the revised biokinetic model of iodine recommended by the ICRP-30 and-56 reports. Accordingly, a five-compartmental (stomach, body fluid, thyroid, whole body, and excretion) model was established to simulate the metabolic mechanism of I-131 in thyroid cancer patients, whereas the respective four simultaneous differential equations were solved via a self-developed program run in MATLAB. This made it possible to provide a close correlation between MATLAB simulation results and empirical data. The latter data were collected through in-vivo gamma camera scans of nine patients obtained after 1, 4, 24, 48, 72, and 168 hours after radioactive I-131 administration. The average biological half-life values for the stomach, body fluid, thyroid, and whole body of thyroid cancer patients under study were 0.54±0.32, 12.6±1.8, 42.8±5.1, and 12.6±1.8 h, respectively. The corresponding branching ratios I 12 , I 23 , I 25 , I 34 , I 42 , and I 45 as denoted in the biokinetic model of iodine were 1.0, 0.21±0.14, 0.79±0.14, 1.0, 0.1, and 0.9, respectively. The average values of the AT dimensionless index used to verify the agreement between empirical and numerical simulation results were 0.056±0.017, 0.017±0.014, 0.044±0.023, and 0.045±0.009 for the stomach, thyroid, body fluid + whole body, and total, respectively. The results obtained were considered quite instrumental in the elucidation of metabolic mechanisms in the human body, particularly in thyroid cancer patients.
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.
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 ([Formula: see text]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 ([Formula: see text]) coupled with correlation coefficient [Formula: see text] = 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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.