Methodology for Estimation of Tissue Noise Power Spectra in Iteratively Reconstructed MDCT Data (original) (raw)
Related papers
Comparison of Several Methods for Automated Noise Measurements in Computed Tomography
International Journal of Scientific Research in Science and Technology, 2022
Purpose: To compare the methods of automated noise measurement at the polyester resin (PESR) phantom images and clinical abdominal images. Method: The PESR phantom was scanned with a Siemens SOMATOM Emotion 6 CT scanner for various tube voltages, i.e., 80, 110, and 130 kV. Noises from images of the PESR phantom and 27 clinical abdominal scans were automatically measured. The methods used for automatic measurements were methods proposed by Christianson et al (2015), Malkus et al (2017), and Anam et al (2019), respectively. Results: Three methods of automatic noise measurements can distinguish the noise of the three tube voltages. The measured noises from three methods decrease with increasing tube voltage. It can also be seen that the highest noise in PESR phantom images is Christianson et al (2015) method, and the smallest noise is Malkus et al (2017) method. The highest noise in clinical abdominal images is Malkus et al (2017) method, and the smallest noise is Anam et al (2019) method. Conclusion: The algorithms to automatically measure noises proposed by Christianson et al (2015), Malkus et al (2017), and Anam et al (2019) have been compared. Although the three methods can distinguish noise for different exposure factors, the magnitude of the noise from the three methods can vary. Until now there is no standard for automatic noise determination.
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.
Applied Sciences, 2021
Given the inherent characteristics of nonlinearity and nonstationarity of iterative reconstruction algorithms in computed tomography (CT) imaging, this study aimed to perform, for the first time, a voxel-based characterization of noise properties in CT imaging with the ASiR-V and ASiR algorithms as compared with conventional filtered back projection (FBP). Multiple repeated scans of the Catphan-504 phantom were carried out. CT images were reconstructed using FBP and ASiR/ASiR-V with different blending levels of reconstruction (20%, 40%, 60%, 80%, 100%). Noise maps and their nonuniformity index (NUI) were obtained according to the approach proposed by the report of AAPM TG-233. For the homogeneous CTP486 module, ASiR-V/ASiR allowed a noise reduction of up to 63.7%/52.9% relative to FBP. While the noise reduction values of ASiR-V-/ASiR-reconstructed images ranged up to 33.8%/39.9% and 31.2%/35.5% for air and Teflon contrast objects, respectively, these values were approximately 60%/50...
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).
International Journal of Engineering Research and Technology (IJERT), 2015
https://www.ijert.org/the-method-of-using-slices-to-estimate-the-noise-power-spectrum-of-a-medical-x-ray-imaging-system https://www.ijert.org/research/the-method-of-using-slices-to-estimate-the-noise-power-spectrum-of-a-medical-x-ray-imaging-system-IJERTV4IS020788.pdf 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
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....
A mathematical algorithm for quantification of CT image noise
Echocardiography, 2016
Quantification of computed tomography (CT) noise helps in determination of radiation dosage requirements for adequate image quality. Clinical methods used include calculation of the standard deviation (SD) of a selected region of interest (ROI). In industry, wavelet decomposition has been used for image compression while removing highfrequency noise. We evaluated a cohort of 74 consecutive patients referred for coronary artery calcium scoring and quantitated noise within a 16×16 ROI in the ascending aorta using the traditional SD method and also using a two-dimensional dyadic wavelet decomposition method. Clinically, noise has been shown to be proportional to patient weight and also body mass index (BMI), which is a derived value from height and weight. Noise for both methods was plotted against patient parameters of height, weight, waist circumference and calculated BMI. A regression line was calculated and coefficient of determination (CoD) calculated for each. The CoD was better for height, weight, and waist circumference using the wavelet method as compared to the traditional SD method. The wavelet method of quantification of image noise may be an improved method as compared to the SD method. This method could help further refine an imaging system's determination of radiation dosage requirements to obtain a satisfactory quality image.
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).
“Art” In X-Ray Tomography: Image Noise Reduction
ECMS 2007 Proceedings edited by: I. Zelinka, Z. Oplatkova, A. Orsoni, 2007
Reduction of the object dose by reducing X-ray exposure has the inevitable consequence of increasing statistical noise in the projections. A set of projections with a 10% noise, collected during the test experiment at the Institute of Crystallography RAS, were used to reconstruct a water phantom. Two different reconstruction approaches (Algebraic Reconstruction Technique (ART) and Filtered Back Projections (FBP)) were implemented. The reconstructed images also had about 10 % noise in both cases. Median filtering within each ART iterative step and averaging over images, updated and preserved during the final iteration made it possible to lower the image noise to 3%. For ART calculations, the RegART software package developed by the authors was used.
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