Evaluating the quality characteristics of TLD-100T and TLD-100H exposed to diagnostic X-rays and 64 multislice CT using Taguchi's quality loss function

Yeh, Da Ming; Wang, Tzu Hwei; Pan, Lung‐Kwang

doi:10.1016/j.radmeas.2015.06.010

Cited by 11 publications

(4 citation statements)

References 16 publications

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“…The better MAR technique was desired greater expected value. The FOM is basically revised from the definition of signal to noise ratio (S/N) in the Taguchi's optimal analysis [26].…”

Section: Statistical Methods and Figure Of Merit (Fom)mentioning

confidence: 99%

Comparison of quantitative measurements of four manufacturer’s metal artifact reduction techniques for CT imaging with a self-made acrylic phantom

Chou

Chi

Lin

et al. 2020

THC

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BACKGROUND: Metal artifact reduction (MAR) techniques can improve metal artifacts of computed tomography (CT) images. OBJECTIVE: This work focused on conducting a quantitative analysis to compare the effectiveness of four commercial MAR techniques on three types of metal implants (hip implant, spinal implant, and dental filling) with a self-made acrylic phantom. METHODS: A cylindrical phantom was made from acrylic with a groove in the middle, and then three types of metal implants were placed in the groove. The phantom was scanned by four CT scanners and four commercialized MAR techniques were used to analyze the images. The techniques used were single-energy metal artifact reduction (SEMAR, Canon), smart metal artifact reduction software (Smart-MAR, GE), iterative metal artifact reduction (IMAR, Siemens), and metal artifact reduction for orthopedic implants (OMAR, Philips). Quantitative analysis methods included objective and subjective analysis. RESULTS: The expected value of SEMAR, Smart-MAR, IMAR, and OMAR were 36.6, 37.8, 5.0, and 2.3, respectively. SEMAR and Smart-MAR achieved optimal results. CONCLUSION: This study successfully evaluated the effects of four commercial MAR techniques on three types of metal implants in a phantom. All MAR techniques effectively reduced metal artifacts, but the effect was not significant with dental fillings due to high-density material.

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“…The better MAR technique was desired greater expected value. The FOM is basically revised from the definition of signal to noise ratio (S/N) in the Taguchi's optimal analysis [26].…”

Section: Statistical Methods and Figure Of Merit (Fom)mentioning

confidence: 99%

Comparison of quantitative measurements of four manufacturer’s metal artifact reduction techniques for CT imaging with a self-made acrylic phantom

Chou

Chi

Lin

et al. 2020

THC

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“…( 9) contains a conventional definition of the routine dynamic algorithm, which is inappropriate in our present scenario. Figure 6 reveals the respective fish-bone plot of S/Ns constructed via Eqs (8) and (9). As clearly illustrated, the variation trend of S/N versus different factors may mislead the interpretation of dynamic algorithm and fail to achieve the required optimization.…”

Section: Sensitivity In the Dynamic Algorithmmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Pan

Chen

Wang

et al. 2022

THC

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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.

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“…The signal-to-noise ratio, S/N [dB], is proposed herein to quantify the integrated performance of the q m-HU regression. The S/N ratio is revised based on the dynamic optimization method developed by Taguchi [16]. The parameter S/N integrates both a superior conversion constant and a low standard deviation, thus pursuing the highest S/N in the optimization always yields the crucial point.…”

Section: Signal-to-noise Ratio S/nmentioning

confidence: 99%

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

Kuo

Juei-En

Chen

et al. 2017

Computer Assisted Surgery

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The Taguchi dynamic analysis was applied to optimize the linear dependence between computer tomography (CT) number and mass density. The Taguchi unique L(2 × 3) orthogonal array was utilized in the dynamic analysis of a commercial Catphan 600 phantom with CTP 404 module, in order to optimize eighteen combinations of six factors controlling the CT simulator operation, i.e. scan time, kVp, mA, field of view (FOV), slice thickness, and imaging processing algorithm. Each factor being assigned two or three different levels made it possible to organize the required number of combinations (18). The seven materials involved in the phantom possessed different mass densities, which were incorporated into the dynamic analysis. The revised signal-to-noise ratio (S/N) was utilized to describe the integrated performance of various factors' combinations in pursuing the optimal ρ-CT number (HU) calibration curve. The optimal option was found to be: 1 s scan time, 130 kVp, 200 mA, 40 cm FOV, 3 cm of slice thickness, and soft type of algorithm for maintaining the ρ-HU calibration. Factors kVp and FOV dominated the performance either by providing a significant change in S/N value or strongly improving the reproducibility in daily quality assurance. The ρ-HU calibration (HU = 1016.9 × density + 1029.5, r = 0.9954) was further verified by the treatment planning systematic default (TPS) (HU= 1242.1 × density+ 1054.8, r = 0.9755). The well-calibrated ρ-HU curve was successfully applied to clinical examination of the simulated oral cancer from CT scanned slice via a Rando phantom. A significant disagreement between optimal and default isodose curves was observed for doses exceeding 7000 cGy, while a good fit was exhibited by doses below 5000 cGy.

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Evaluating the quality characteristics of TLD-100T and TLD-100H exposed to diagnostic X-rays and 64 multislice CT using Taguchi's quality loss function

Cited by 11 publications

References 16 publications

Comparison of quantitative measurements of four manufacturer’s metal artifact reduction techniques for CT imaging with a self-made acrylic phantom

Comparison of quantitative measurements of four manufacturer’s metal artifact reduction techniques for CT imaging with a self-made acrylic phantom

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

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

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