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Noise reduction in high resolution computed tomography data
Tkadlecová, Markéta ; Chmelík,, Jiří (oponent) ; Zikmund, Tomáš (vedoucí práce)
Due to the complexity of the computed tomography (CT) data acquisition process, the noise in captured X-ray images is inevitable and can distort acquired information. Therefore, the noise should be controlled. Noise reduction in CT data has primarily been studied in low-dose medical CT, and there is little known about the properties of noise in sub-micron CT and its suppression. The denoising strategies can take place in the X-ray images, CT slices, or during the reconstruction process. This work primarily focuses on the reduction of noise in X-ray images and CT slices. The first step to finding a complex denoising methodology is to determine the distribution and mathematical model of the noise in the X-ray images. The model was established using bright field images taken under different exposure times. With the estimated model, the artificial noise in a phantom dataset could be simulated. Selected algorithms were tested in the X-ray images and the tomogram slices of the phantom and compared subjectively and objectively, through visual inspection and image quality metrics. The denoising strategies with the best outcomes were further evaluated on measured submicron CT datasets from the CT system Rigaku nano3DX, and their advantages, limitations, and possible usage were described.
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