Purpose Fluoroscopy is an invaluable tool in various medical practices such as catheterization or image-guided surgery.Patient’sscreenforprolongedtimerequiressubstantial reductioninX-rayexposure:Thelimitednumberofphotons 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 modelsandapossibleapplicationtoimprovetheperformanceof noise ﬁltering. Methods QuantumnoiseismodeledasaPoissondistribution andresultsstronglysignal-dependent.However,ﬂuoroscopic devicesgenerallyapplygray-leveltransformations(i.e.,logarithmic-mapping, gamma-correction) for image enhancement. The resulting statistical transformations of the noise were analytically derived. In addition, a characterization of thestatisticsofnoiseforﬂuoroscopicimagedifferenceswas offered by resorting to Skellam distribution. Real ﬂuoroscopic sequences of a simple step-phantom were acquired by a conventional ﬂuoroscopic device and were utilized as actual noise measurements to compare with. An adaptive spatio-temporal ﬁlter based on the local conditional average of similar pixels has been proposed. The gray-level differencesbetweenthelocalpixelandtheneighboringpixelshave been assumed as measure of similarity. Filter performance was evaluated by using real ﬂuoroscopic images of a step phantom and acquired during a pacemaker implantation. Results The comparison between experimental data and the analytical derivation of the relationship between noise variance and mean pixel intensity (noise-parameter models) were presented relatively to raw-images, after applying logarithmic-mapping or gamma-correction and for difference images. Results have conﬁrmed a great agreement (adjusted R-squaredvalues>0.8).Clippingeffectsofrealsensorswere also addressed. A ﬁne image restoration has been obtained by using a conditioned spatio-temporal average ﬁlter based on the noise statistics previously estimated. Discussion Fluoroscopicnoisemodelingisusefultodesign effectiveproceduresfornoiseestimationandimageﬁltering. Inparticular,ﬁlterperformance analysishas showed thatthe knowledge of the noise model and the accurate estimate of noisecharacteristicscansignificantlyimprovetheimagerestoration, especially for edge preserving. Fluoroscopic image enhancement can support further X-ray exposure reduction, medical image analysis and automated object identiﬁcation (i.e., surgery tools, anatomical structures).
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