Error measurement acuracy methodology

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English

New type of methods allows minimising the geometrical misalignments and they are called self-calibrating algorithms. This algorithm provides theoretically exact reconstruction when linked with the helical geometry, implying images without geometric distortion. CT systems applying such algorithms do not potentially need any additional calibration or even reference measurement. We propose a methodology of testing such a system with a self-calibrating algorithm and helical trajectory. Moreover, the length measurement error is evaluated on both spheres distance error and probing errors. Sphere distance errors are reaching 2/3 of the voxel size. Besides, we show the results for the length measurement error following the VDI/VDE 2630 guideline. The results show that the sphere distance errors has still a systematic error. This stems from the calibration being highly dependent on knowing the average voxel size with sufficient precision. The magnification is given by the self-calibration algorithm and it is associated with the detector pixel size. Therefore, the detector pixel size needs to be specified with precision down to 10 nm. This is below the manufacturing tolerance of the detector, and therefore it needs to be measured for each detector separately. In this paper, we provide a statistical method for obtaining this calibration. With this calibration, we achieve an SD error of ± (18.63 + 2.5L/100), when imaging with a voxel size of about 15μm. In addition to that, we introduce the procedure to evaluate the maximal permissible error calculation on pure statistics based on the length measurement error. Reconstruction Discussion is focused on the stability of reconstruction and the results of individual errors.

Micro Computed tomography, measurement acuracy, error estimation

BŘÍNEK, A.; BLAŽEK, P.; ZIKMUND, T.; KAISER, J.; KAREŠ, M.

25. 11. 2020

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Error_measurement_acuracy_methodology.pdf