Assessing data acquisition approaches in electron tomography
Abdollahzadeh, Ali (2016)
Master's Degree Programme in Biomedical Engineering
Tieto- ja sähkötekniikan tiedekunta - Faculty of Computing and Electrical Engineering
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Electron tomography (ET) is a technique to reveal the interior structures of organic-subcellular macro molecules- and inorganic materials from their 2D cross sectional transmission electron microscope (TEM) projections. However, restricted radiation dose due to specimen damage and blind region of angular sampling as a result of physical constraints deteriorate the quality of the resultant tomograms. Typically, electron tomograms suffer from low signal to noise ratio (SNR) and elongation artifact in the direction of electron radiation. Different studies propose methods to tackle the constraints of ET in the data acquisition stage. This thesis is a comparative study among different data acquisition models by analyzing the resultant tomogram of each method quantitatively. We implement each model with a TEM simulator and compare the tomograms by their root mean square (RMS) and resolution. Results of TEM settings indicate that 1) reducing the acceleration voltage and increasing the defocus value intensifies the contrast. 2) Diminishing the objective diaphragm size reduces the brightness of the projections. Comparing data acquisition models states that 1) cosine model of dose distribution homogenizes the SNR of sinograms and compared to the conventional methods enhances the resolution of the tomograms. 2) Employing Saxton model for angular sampling boosts the resolution and declines the elongation artifact. 3) Combination of the cosine method of dose distribution and Saxton's model promotes the resolution, RMS value and elongation artifact significantly: resolution enhanced 1.81 times compared to the constant dose and angle distribution models in Z-direction. To conclude, emphasis on the SNR and sampling frequency of highly tilted angles outperforms the conventional data acquisition approaches qualitatively and quantitatively.