Work

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

Year: 2012

Type: article

Abstract: Radiation dose reduction is a very topical problem in medical X-ray CT imaging and plenty of strategies have\nbeen introduced recently. Hybrid iterative reconstruction algorithms are one of them enabling dose reduction up to\n70 %. The paper describes data preprocessing and feature extraction from iteratively reconstructed images in order\nto assess their quality in terms of image noise and compare it with quality of images reconstructed from the same\ndata by the conventional filtered back projection. The preprocessing stage consists in correction of a stair-step\nartifact and in fast, precise bone and soft tissue segmentation. Noise patterns of differently reconstructed images\ncan therefore be examined separately in these tissue types. In order to remove anatomical structures and to obtain\nthe pure noise, subtraction of images reconstructed by the iterative iDose algorithm from images reconstructed by\nthe filtered back projection is performed. The results of these subtractions called here residual noise images and\nare the used to further extract parameters of the noise. The noise parameters, which are intended to serve as input\ndata for consequent multidimensional statistical analysis, are the standard deviation and power spectrum of the\nresidual noise. This approach enables evaluation of noise properties in the whole volume of real patient data, in\ncontrast to noise analysis performed in small regions of interest or in images of phantoms.

Source: Digital Library (University of West Bohemia)

Authors: Petr Walek, Jirí Jan, Petr Ouředníček, Jarmila Skotáková, Igor Jíra

Institutions: Brno University of Technology, Philips (Finland), Masaryk University

Language: English

Topic: Radiation Dose and Imaging

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

Open Access status: green