C23: Preprocessing for Quantitative Statistical Noise Analysis of MDCT Brain Images Reconstructed Using Hybrid Iterative (iDose) Algorithm

Walek,P., Jan,J., Ourednicek,P., Skotakova,J., Jira,I.

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 a 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 parameters. The noise parameters which are intended to serve as input data for further multidimensional statistical analysis are standard deviation and noise power spectrum of residual noise. Performed approach enables evaluation of noise properties in whole volume of real patient data in contrast with noise analysis performed in small region of interest or in images of phantoms.