DQE(<i>f</i>) of four generations of computed radiography acquisition devices

Dobbins, James T.; Ergun, David L.; Rutz, Lois; Hinshaw, D. A.; Blume, Hartwig R.; Clark, Dwayne C.

doi:10.1118/1.597627

Cited by 292 publications

(249 citation statements)

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“…The two dimensional normalized noise power spectra were obtained by the standard Fourier transform method. 18 To be consistent with the calculation of NNPS of the microangiographic system, a radial averaging was performed to obtain the one-dimensional noise power spectrum from the corresponding two-dimensional NNPS. To obtain the zero frequency NNPS, we used a fourth-order-polynomial fit over the NNPS data excluding the first four data points because of their unusual behavior due to some low frequency artifacts.…”

Section: Noise Power Spectrummentioning

confidence: 99%

Generalized performance evaluation of x-ray image intensifier compared with a microangiographic system

Yadava

Kyprianou

Rudin

et al. 2005

Medical Imaging 2005: Physics of Medical Imaging

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Standard objective parameters such as MTF, NPS, NEQ and DQE do not reflect complete system performance, because they do not account for geometric unsharpness due to finite focal spot size and scatter due to the patient. The inclusion of these factors led to the generalization of the objective quantities, termed GMTF, GNNPS, GNEQ and GDQE defined at the object plane. In this study, a commercial x-ray image intensifier (II) is evaluated under this generalized approach and compared with a high-resolution, ROI microangiographic system previously developed and evaluated by our group. The study was performed using clinically relevant spectra and simulated conditions for neurovascular angiography specific for each system. A head-equivalent phantom was used, and images were acquired from 60 to 100 kVp. A source to image distance of 100 cm (75 cm for the microangiographic system) and a focal spot of 0.6 mm were used. Effects of varying the irradiation field-size, the air-gaps, and the magnifications (1.1 to 1.3) were compared. A detailed comparison of all of the generalized parameters is presented for the two systems. The detector MTF for the microangiographic system is in general better than that for the II system. For the total x-ray imaging system, the GMTF and GDQE for the II are better at low spatial frequencies, whereas the microangiographic system performs substantially better at higher spatial frequencies. This generalized approach can be used to more realistically evaluate and compare total system performance leading to improved system designs tailored to the imaging task.

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Section: Noise Power Spectrummentioning

confidence: 99%

Generalized performance evaluation of x-ray image intensifier compared with a microangiographic system

Yadava

Kyprianou

Rudin

et al. 2005

Medical Imaging 2005: Physics of Medical Imaging

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“…Using these assumptions the attenuation of the blood vessel is: (12) where θ(x) is the unit step function. The vessel profile in the z direction is defined as: (13) Since the GNEQ is measured at the object plane magnification m does not modify the object. The frequency distribution of the object is obtained by (14) where n x (n y ) is the number of data in the x(y) direction which for our detector are 1024.…”

Section: Object Detectabilitymentioning

confidence: 99%

Study of the generalized MTF and DQE for a new microangiographic system

et al. 2004

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We study the properties of a new microangiographic system, consisting of a Region of Interest (ROI) microangiographic detector, x-ray source, and patient. The study was performed under conditions intended for clinical procedures such as neurological diagnostic angiograms as well as treatments of intracranial aneurysms, and vessel-stenoses. The study was performed in two steps; first a uniform head equivalent phantom was used as a "filter". This allowed us to study the properties of the detector alone, under clinically relevant x-ray spectra. We report the detector MTF, NPS, NEQ, and DQE for beam energies ranging from 60-100kVp and for different detector entrance exposures. For the second step, the phantom was placed adjacent to the detector, allowing scatter to enter the detector and new measurements were obtained for the same beam energies and detector entrance exposures. Different radiation field sizes were studied, and the effects of different scatter amounts were investigated. The spatial distribution of scatter was studied using the edge-spread method and a generalized system MTF was obtained by combining the scatter MTF weighted by the scatter fraction with the detector MTF and focal spot unsharpness due to magnification. The NPS combined with the generalized MTF gave the generalized system NEQ and DQE. The generalized NEQ and the ideal object detectability were used to calculate the Dose Area Product to the patient for 75% object detection probability. This was used as a system optimization method.

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“…The noise power spectrum (NPS), as a more thorough noise descriptor than pixel standard deviation, describes both the magnitude and spatial frequency characteristics of image noise, which plays a critical role in analyzing and optimizing imaging system performance 1 , 2 , 3 , 4 , 5 . It is most often integrated with other metrics to assess image quality for specific tasks, 6 , 7 , 8 , 9 and has been commonly utilized in the development, characterization, optimization, and comparison of many new imaging technologies such as computed radiography, (10) digital mammography, 11 , 12 storage phosphors for dental X‐ray, (13) and other devices in a preclinical 14 , 15 and clinical 16 , 17 environment.…”

Section: Introductionmentioning

confidence: 99%

Practical considerations for noise power spectra estimation for clinical CT scanners

Dolly

Chen

Anastasio

et al. 2016

J Applied Clin Med Phys

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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 iDose4 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. An analysis of the effects of these varied calculation parameters on the magnitude and shape of the NPS was conducted. The local NPS varied depending on calculation parameters, particularly at low spatial frequencies below ∼0.15 mm−1. For the simulation study, NPS calculation error decreased exponentially as ROI number increased. For the Catphan study the NPS magnitude varied as a function of ROI location, which was better observed when using smaller ROI sizes. The image subtraction method for background removal was the most effective at reducing low‐frequency background noise, and produced similar results no matter which ROI size or window function was used. The PCA background removal method with a Hann window function produced the closest match to image subtraction, with an average percent difference of 17.5%. Image noise should be analyzed locally by calculating the NPS for small ROI sizes. A minimum ROI size is recommended based on the chosen radial bin size and image pixel dimensions. As the ROI size decreases, the NPS becomes more dependent on the choice of background removal method and window function. The image subtraction method is most accurate, but other methods can achieve similar accuracy if certain window functions are applied. All dependencies should be analyzed and taken into account when considering the interpretation of the NPS for task‐based image quality assessment.PACS number(s): 87.57.C‐, 87.57.Q‐

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DQE(f) of four generations of computed radiography acquisition devices

Cited by 292 publications

References 0 publications

Generalized performance evaluation of x-ray image intensifier compared with a microangiographic system

Generalized performance evaluation of x-ray image intensifier compared with a microangiographic system

Study of the generalized MTF and DQE for a new microangiographic system

Practical considerations for noise power spectra estimation for clinical CT scanners

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