Xray fluoroscopy noise modeling for filter design

Authors: M Cesarelli P Bifulco T Cerciello M Romano L Paura

Publish Date: 2012/06/21

Volume: 8, Issue: 2, Pages: 269-278

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Abstract

Fluoroscopy is an invaluable tool in various medical practices such as catheterization or imageguided surgery Patient’s screen for prolonged time requires substantial reduction in Xray exposure The limited number of photons generates relevant quantum noise Denoising is essential to enhance fluoroscopic image quality and can be considerably improved by considering the peculiar noise characteristics This study presents analytical models of fluoroscopic noise to express the variance of noise as a function of gray level a practical method to estimate the parameters of the models and a possible application to improve the performance of noise filteringQuantum noise is modeled as a Poisson distribution and results strongly signaldependent However fluoroscopic devices generally apply graylevel transformations ie logarithmicmapping gammacorrection for image enhancement The resulting statistical transformations of the noise were analytically derived In addition a characterization of the statistics of noise for fluoroscopic image differences was offered by resorting to Skellam distribution Real fluoroscopic sequences of a simple stepphantom were acquired by a conventional fluoroscopic device and were utilized as actual noise measurements to compare with An adaptive spatiotemporal filter based on the local conditional average of similar pixels has been proposed The graylevel differences between the local pixel and the neighboring pixels have been assumed as measure of similarity Filter performance was evaluated by using real fluoroscopic images of a step phantom and acquired during a pacemaker implantationThe comparison between experimental data and the analytical derivation of the relationship between noise variance and mean pixel intensity noiseparameter models were presented relatively to rawimages after applying logarithmicmapping or gammacorrection and for difference images Results have confirmed a great agreement adjusted Rsquared values   08 Clipping effects of real sensors were also addressed A fine image restoration has been obtained by using a conditioned spatiotemporal average filter based on the noise statistics previously estimatedFluoroscopic noise modeling is useful to design effective procedures for noise estimation and image filtering In particular filter performance analysis has showed that the knowledge of the noise model and the accurate estimate of noise characteristics can significantly improve the image restoration especially for edge preserving Fluoroscopic image enhancement can support further Xray exposure reduction medical image analysis and automated object identification ie surgery tools anatomical structures

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