Preprocessing for Quantitative Statistical Noise Analysis of MDCT Brain Images Reconstructed Using Hybrid Iterative (iDose) Algorithm

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F12%3APU98500" target="_blank" >RIV/00216305:26220/12:PU98500 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Preprocessing for Quantitative Statistical Noise Analysis of MDCT Brain Images Reconstructed Using Hybrid Iterative (iDose) Algorithm
Original language description
Radiation dose reduction is a very actual problem in medical X-ray CT imaging and plenty of strategies have been introduced recently. Hybrid iterative reconstruction algorithms are one of them enabling dose reduction up to 70 %. Paper describes data preprocessing and feature extraction from iteratively reconstructed images in order to assess their quality in terms of image noise and compare with quality of images reconstructed by conventional filtered back projection. Preprocessing stage is consisted from correction of the stair-step artifact and fast, precise bones and soft tissue segmentation. Noise patterns of differently reconstructed images can therefore be examined separately in these tissue types. In order to remove anatomical structures and obtain pure noise subtraction of images reconstructed by iDose from images reconstructed by filtered back projection is performed. Results of these subtractions are called residual noise images and are used to further extraction of noise par
Czech name
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Czech description
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Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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