IJERT-The Method of Using slices to Estimate the Noise Power Spectrum of A Medical X-Ray Imaging System (original) (raw)
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The Method of Using slices to Estimate the Noise Power Spectrum of A Medical X-Ray Imaging System
International journal of engineering research and technology, 2015
This paper presents Dobbin's method to estimate the noise power spectrum using a screen film system. The one-dimensional spectral estimate was obtained by extracting thick and thin slices from two-dimensional noise power. The slices were made parallel to the primary axis of ROI, but did not include the axis. We measured NPS using one slice, two slices, four slices, eight slices,upper eight slices (a) and eight slices (b) of data in the 128×128 two-dimensional NPS space which were extracted to generate the one- dimensional NPS curves in horizontal and vertical directions and they were compared with Dobbin's method. Very little was found in the NPS shape with regards to the two- dimensional space only and the slice which contained one row and one column was sufficient to study NPS in the two- dimensional space Keywords—X-ray, Noise power spectrum, screen film system, fast Fourier transform
This paper presents Dobbin's method to estimate the noise power spectrum using a screen film system. The one-dimensional spectral estimate was obtained by extracting thick and thin slices from two-dimensional noise power. The slices were made parallel to the primary axis of ROI, but did not include the axis. We measured NPS using one slice, two slices, four slices, eight slices,upper eight slices (a) and eight slices (b) of data in the 128×128 two-dimensional NPS space which were extracted to generate the one-dimensional NPS curves in horizontal and vertical directions and they were compared with Dobbin's method. Very little was found in the NPS shape with regards to the two-dimensional space only and the slice which contained one row and one column was sufficient to study NPS in the two-dimensional space Keywords—X-ray, Noise power spectrum, screen film system, fast Fourier transform
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/indirect-method-of-measurement-and-evaluating-the-noise-power-spectrum-of-a-medical-x-ray-imaging-system https://www.ijert.org/research/indirect-method-of-measurement-and-evaluating-the-noise-power-spectrum-of-a-medical-x-ray-imaging-system-IJERTV4IS061060.pdf The purpose of this paper is to estimate the noise power spectrum by autocorrelation function AFC using a different radiographic film and full field digital mammography. The autocorrelation function is a measure of similarity between a data set and a shifted copy data as a function of shift magnitude. This method has the advantage of providing the value of the noise power at zero frequency and the NPS calculated via autocorrelation function ACF is smoother than NPS, which is calculated by the direct fast Fourier method. Noise power spectrum computations using different images have been attempted using codes written in MATLAB® Version 7.8.0.347 (Math Works, 2009).
The purpose of this paper is to estimate the noise power spectrum by autocorrelation function AFC using a different radiographic film and full field digital mammography. The autocorrelation function is a measure of similarity between a data set and a shifted copy data as a function of shift magnitude. This method has the advantage of providing the value of the noise power at zero frequency and the NPS calculated via autocorrelation function ACF is smoother than NPS, which is calculated by the direct fast Fourier method. Noise power spectrum computations using different images have been attempted using codes written in MATLAB® Version 7.8.0.347 (Math Works, 2009).
The noise power spectrum of CT images
Physics in Medicine and Biology, 1987
An expression for the noise power spectrum of images reconstructed by the discrete filtered backprojection algorithm has been derived.
Practical considerations for noise power spectra estimation for clinical CT scanners
Journal of applied clinical medical physics, 2016
Local noise power spectra (NPS) have been commonly calculated to represent the noise properties of CT imaging systems, but their properties are significantly affected by the utilized calculation schemes. In this study, the effects of varied calculation parameters on the local NPS were analyzed, and practical suggestions were provided regarding the estimation of local NPS for clinical CT scanners. The uniformity module of a Catphan phantom was scanned with a Philips Brilliance 64 slice CT simulator with varied scanning protocols. Images were reconstructed using FBP and iDose(4) iterative reconstruction with noise reduction levels 1, 3, and 6. Local NPS were calculated and compared for varied region of interest (ROI) locations and sizes, image background removal methods, and window functions. Additionally, with a predetermined NPS as a ground truth, local NPS calculation accuracy was compared for computer simulated ROIs, varying the aforementioned parameters in addition to ROI number....
Noise power spectra of images from digital mammography detectors
Medical Physics, 1999
Noise characterization through estimation of the noise power spectrum ͑NPS͒ is a central component of the evaluation of digital x-ray systems. We begin with a brief review of the fundamentals of NPS theory and measurement, derive explicit expressions for calculation of the one-and twodimensional ͑1D and 2D͒ NPS, and discuss some of the considerations and tradeoffs when these concepts are applied to digital systems. Measurements of the NPS of two detectors for digital mammography are presented to illustrate some of the implications of the choices available. For both systems, two-dimensional noise power spectra obtained over a range of input fluence exhibit pronounced asymmetry between the orthogonal frequency dimensions. The 2D spectra of both systems also demonstrate dominant structures both on and off the primary frequency axes indicative of periodic noise components. Although the two systems share many common noise characteristics, there are significant differences, including markedly different dark-noise magnitudes, differences in NPS shape as a function of both spatial frequency and exposure, and differences in the natures of the residual fixed pattern noise following flat fielding corrections. For low x-ray exposures, quantum noise-limited operation may be possible only at low spatial frequency. Depending on the method of obtaining the 1D NPS ͑i.e., synthetic slit scanning or slice extraction from the 2D NPS͒, on-axis periodic structures can be misleadingly smoothed or missed entirely. Our measurements indicate that for these systems, 1D spectra useful for the purpose of detective quantum efficiency calculation may be obtained from thin cuts through the central portion of the calculated 2D NPS. On the other hand, low-frequency spectral values do not converge to an asymptotic value with increasing slit length when 1D spectra are generated using the scanned synthetic slit method. Aliasing can contribute significantly to the digital NPS, especially near the Nyquist frequency. Calculation of the theoretical presampling NPS and explicit inclusion of aliased noise power shows good agreement with measured values.
Methodology for Estimation of Tissue Noise Power Spectra in Iteratively Reconstructed MDCT Data
Iterative reconstruction algorithms have been recently introduced into X-ray computed tomography imaging. Enabling patient dose reduction by up to 70% without affecting image quality they deserve attention; therefore properties of noise present in iteratively reconstructed data should be examined and compared to the images reconstructed by conventionally used filtered back projection. Instead of evaluating noise in imaged phantoms or small homogeneous regions of interest in real patient data, a methodology for assessing the noise in full extent of real patient data and in diverse tissues is presented in this paper. The methodology is based on segmentation of basic tissues, subtraction of images reconstructed by different algorithms and computation of standard deviation and radial onedimensional noise power spectra. Tissue segmentation naturally introduces errors into estimation of noise power spectra; therefore, magnitude of segmentation error is examined and is considered to be acceptable for estimation of noise power spectra in soft tissue and bones. As a result of this study it can be concluded that iDose 4 hybrid iterative reconstruction algorithm effectively reduces noise in multidetector X-ray computed tomography (MDCT) data. The MDCT noise has naturally different characteristics in diverse tissues; thus it is object dependent and phantom studies are therefore unable to reflect its whole complexity.
Noise Analysis in Digital Radiography
1986
Noise plays a dominant role in the detection of low-contrast objects in any imaging system. Until the advent of the computed tomography (CT) scanner and more recently digital radiography, noise in an imaging system (typically a screen-film combination) was considered to have two major components: quantum mottle, the statistical fluctuations in the number of detected photons, and structure noise, due to phosphor screen inhomogeneities and film grain. The development of electronic X-ray detectors and in particular digital video subtraction systems has added much complexity to noise analysis. Due to the many new noise sources present, noise analysis now requires new evaluation criteria and techniques. The rapid development of digital subtraction angiography in the past few years has required the use of new and improved imaging components. (1-12) These improvements have been focused heavily upon noise reduction particularly in the TV system. (13) The signal-to-noise ratio (SNR) of the TV camera required significant improvements before digital video angiography with intravenous (IV) injections could be implemented. The noise in the imaging chain which ends up in the output of the system, the final image, deteriorates the quality of the final image in both a subjective and a quantitative sense. This noise may lead to an undetected lesion in a visual interpretation reading or may create errors in quantitative results, such as percent stenosis or regional blood flow.
This research presented an appropriate approach for the robust estimation of noise statistic in dental panoramic x-rays images. To achieve maximum image quality after denoising, a new, low order, local adaptive Gaussian Scale Mixture model is presented, which accomplishes nonlinearities from scattering. State of art methods use multi scale filtering of images to reduce the irrelevant part of information, based on generic estimation of noise. The usual assumption of a distribution of Gaussian and Poisson statistics only lead to overestimation of the noise variance in regions of low intensity (small photon counts), but to underestimation in regions of high intensity and therefore to non-optional results. The analysis approach is tested on 50 samples from a database of 50 panoramic X-rays images and the results are cross validated by medical experts.