Determination of the Image Quality in Computed Tomography and its Standardisation
Source abstract
Abstract
X-Ray Computed tomography (XCT) has become an important non-destructive quality assurance technique in industry. Consequently, standards for quality insurance of XCT and on its performance are required to support industrial XCT users for reliable production. This performance is determined by analysis of the quality of the images produced and by the dimensional measurement accuracy achieved for a given XCT parameter setting. Until recently, standards assessed image quality solely in terms of contrast sensitivity and spatial resolution. Detection limits could not be predicted until now. A new term is introduced: The Detail Detection Sensitivity (DDS). It depends on the contrast sensitivity as a function of contrast and noise, and on the spatial resolution. The spatial frequency needs to be implemented into the analysis to consider sensitivity as a function of the size of an indication. The contrast sensitivity is quantified by the Contrast Discrimination Function (CDF) and the spatial resolution by the Modulation Transfer Function (MTF). The numerical DDS is determined for air flaws from the Contrast Detection Diagram (CDD) at 100% contrast. However, some XCT operators prefer visual determinations rather than numerical ones. To face this need, the SensMonCTII project proposes a new Image Quality Indicator (IQI), consisting of a disk with holes of different sizes for visual DDS determination. The project aims to produce a new ISO standard draft providing a practice to evaluate numerically the XCT image quality via MTF, CDF, CDD and DDS, as well as to evaluate visually the DDS from the hole visibility of a disk IQI. The paper does not address the performance of XCT in terms of dimensional measurement accuracy, but focuses on the performance of XCT in terms of image quality. It describes the methodology to evaluate the image quality, including DDS for the first time.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Advanced X-ray and CT Imaging · Radiation Dose and Imaging · Medical Imaging Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (13)
Show source-provided references
1. De Chiffre, L., Carmignato, S., Kruth, J.-P., Schmitt, R., Weckenmann, A.: Industrial applications of computed tomography. CIRP Annals - Manuf. Technol. 63, 655–677 (2014). https://doi.org/10.1016/j.cirp.2014.05.01110.1016/j.cirp.2014.05.011 ↗
2. Carmignato, S., Dewulf, W., Leach, R.: Industrial X-Ray Computed Tomography. Springer (2018). https://doi.org/10.1007/978-3-319-59573-310.1007/978-3-319-59573-3 ↗
3. Obaton, A.-F., Fain, J., Meinel, D., Tsamos, A., Léonard, F., Lécuelle, B., Djemaï, M.: In vivo bone progression in and around lattice implants additively manufactured with a new titanium alloy. Appl. Sci. 13(12), 7282 (2023). https://doi.org/10.3390/app1312728210.3390/app13127282 ↗
4. Du Plessis, A., Yadroitsava, I., Yadroitsev, I.: Effects of defects on mechanical properties in metal additive manufacturing: A review focusing on X-ray tomography insights. Mater. Des. 187, 108385 (2020). https://doi.org/10.1016/j.matdes.2019.10838510.1016/j.matdes.2019.108385 ↗
5. Gobert, C., Kudzal, A., Sietins, J., Mock, C., Sun, J., McWilliams, B.: Porosity segmentation in X-ray computed tomography scans of metal additively manufactured specimens with machine learning. Addit. Manuf. 36 (2020). https://doi.org/10.1016/j.addma.2020.10146010.1016/j.addma.2020.101460 ↗
6. Ferrucci, M., Ametova, E.: Charting the course towards dimensional measurement traceability by X-ray computed tomography. Meas. Sci. Technol. 32(9), 092001 (2021). https://doi.org/10.1088/1361-6501/abf05810.1088/1361-6501/abf058 ↗
7. Villarraga-Gómez, H., Smith, S.T.: Effect of geometric magnification on dimensional measurements with a metrology-grade X-ray computed tomography system. Prec Eng. 73, 488–503 (2022). https://doi.org/10.1016/j.precisioneng.2021.10.01510.1016/j.precisioneng.2021.10.015 ↗
8. Obaton, A.F., Tanich, A., Fischer, N., Antona, S., Montagner, F., Genot, S., Brzuchacz, S., De Soete, P., Duboeuf, K., Beuvier, T., Nanjareddy, R., Coutant, N., Cochennec, N., Gay, L.: Dimensional XCT comparison campaign on an aluminium object. Meas. Sci. Technol. 34(9), 094004 (2023). https://doi.org/10.1088/1361-6501/acd8dd10.1088/1361-6501/acd8dd ↗
9. Ewert, U., Herold, F., Roth, H., Wohlgemuth, F.: Numeric Prediction of the Detail Visibility in industrial X-Ray Computed Tomography by Human Observers. 3th European Conference on Non-Destructive Testing 2023. e-Journal of NDT 28(8) (paper 412) (2023). https://doi.org/10.58286/2843710.58286/28437 ↗
10. Villarraga Gómez, H., Körner, L., Leach, R., Smith, S.: Amplitude-wavelength maps for X-ray computed tomography systems. Prec Eng. 64, 228–242 (2020). https://www.researchgate.net/publication/33980031910.1016/j.precisioneng.2020.03.005 ↗
11. ISO 17296-2: 2016 Additive manufacturing - General principles - Part 2: Overview of process categories and feedstock
12. Schwentenwein, M., Homa, J.: Additive manufacturing of dense alumina ceramics. Int. J. Appl. Ceram. Technol. 12(1), 1–7 (2015). https://doi.org/10.1111/ijac.1231910.1111/ijac.12319 ↗
13. Bircher, B.A., Meli, F., Küng, A., Thalmann, R.: METAS-CT: metrological x-ray computed tomography at sub-micrometre precision. The 20th EUSPEN Int. Conf. pp 281–4 (2020)