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Spectral computed tomography: comparison of real and virtual nativ images
Širůčková, Kateřina ; Bartušek, Karel (referee) ; Marcoň, Petr (advisor)
Advanced X-ray medical diagnostic methods massively increase the radiation exposure of patients. Therefore, it is necessary to focus on the reduction of radiation and its stochastic and deterministic effects. This project compares real native images TNC with virtual native images VNC. Virtual native images are acquired by spectral computed tomography method and it is suggested that VNC could potentially substitute real native images, thereby, the total radiation dose from multiphase spectral CT would decrease. A comparison was performed by defining certain parameters that represented the differentiation of the measured and calculated values in the images. The parameters were SNR, CNR, absolute difference, relative error and they were analysed by statistical tests using p-value and correlation analysis. Another output is a database of patients with compared parameters from real and virtual native images.
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Characterization of osteolytic lesions by low-dose spectral CT in myeloma patients
Nohel, M. ; Jan, J. ; Chmelik, J.
This paper presents a preliminary study of characterization of focal osteolytic lesions in low-dose spectral CT in myeloma patients. Spectral CT with energy decomposition into two energies allows us to use post-processing software for creating several parametric maps as well as so-called monoenergetic images. The paper includes a demonstration of the different contrast of lytic lesions in the spine and a comparison of healthy vertebrae and vertebrae affected by focal lytic lesions. It is shown that lytic lesions are better recognizable on a 40 keV monoenergetic image compared to conventional CT.
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Spectral Computed Tomagraphy: Comparison Of Real And Virtual Native Images
Širůčková, Kateřina
This paper is based on the comparison of real and virtual native images obtained from spectral computed tomography (CT). Both types of images are compared by the signal-to-noise ratio (SNR), HU/HU* ratio and in the relation of used contrast medium. Parametric and non-parametric tests were used for statistical analysis of the measurements. The main purpose of this study is to contribute on the wider issue which is radiation dose reduction on the patient. This problem has not been fully researched yet but it has been proposed that the virtual images could be computed mathematically from the images acquired by the conventional scanning protocol for spectral CT and used instead of the real images.
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Tissue Characterisation In Spectral Ct Data
Poláková, Veronika
This article deals with tissue characterisation in virtual monoenergetic images (VMI). It presents that with growing energy of VMI the median of CT number increases or decreases with different steepness depending on a type of tissue. As a consequence, some VMI enable better soft tissue distinction and therefore their better classification. To determine which VMI are best suited, Cohen d was used. After that, Random Forest classification algorithm was applied to these images. If median of pixels is considered in addition to pixels themselves, the tissues can be clasiffied correctly.
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Spectral computed tomography: comparison of real and virtual nativ images
Širůčková, Kateřina ; Bartušek, Karel (referee) ; Marcoň, Petr (advisor)
Advanced X-ray medical diagnostic methods massively increase the radiation exposure of patients. Therefore, it is necessary to focus on the reduction of radiation and its stochastic and deterministic effects. This project compares real native images TNC with virtual native images VNC. Virtual native images are acquired by spectral computed tomography method and it is suggested that VNC could potentially substitute real native images, thereby, the total radiation dose from multiphase spectral CT would decrease. A comparison was performed by defining certain parameters that represented the differentiation of the measured and calculated values in the images. The parameters were SNR, CNR, absolute difference, relative error and they were analysed by statistical tests using p-value and correlation analysis. Another output is a database of patients with compared parameters from real and virtual native images.
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