Synchrotron and Free electron laser Radiation: generation and application (SFR-2018)

Method preprocessing data acquired in tomography experiment

Not scheduled

15m

Conference Hall (Budker INP)

Oral X-ray structural analysis

Speaker

Mr Anastasia Ingacheva (HSE)

Description

For correct numerical interpretation of tomographic experiment i.e. estimation of objects attenuation coefficients it is important to obtain reconstruction of high quality which depends directly on methods of processing registered data during experiment. Data processing flow begins with its preparation for application of reconstruction algorithm. Necessary part of data processing contains subtraction of black field, normalization considering empty data and taking logarithm. This part is not enough for obtaining reconstruction of high quality when working with real data since it is not ideal. Real data includes noise and distortions due to changes of the set-up geometrical parameters during the experiment. We have analyzed five possible types of data corruptions during experiment and suggested corrections for them. The first one addresses effects from beam intensity instability during the experiment. The second one corrects shifts regarding beam decentralization. The third one is used to find rotation axis of object. The fourth one considers that radiation is polychromatic. The fifth one is about ring artefacts due to defective pixels in the detector. We have also developed a method of blind automatic adjustment of parameters for every suggested method. All these methods were tested with both real and synthetic data. For synthetic data creation we have simulated all mentioned corruptions. Both synthetic and real experiments show that suggested methods improve reconstruction quality. In real experiments level of agreement between automatic parameters adjustment and experts is about 90%. This work was partially supported by the Federal Agency of Scientific Organizations (Agreement № 007-ГЗ/Ч3363/26) in part of tomography setup construction and by the Russian Foundation for Basic Research (project № 17-29-03492) in images corrections and tomography reconstruction algorithms.