Techniques to improve the accuracy of noise power spectrum measurements in digital x‐ray imaging based on background trends removal

Zhou, Zhongxing; Gao, Feng; Zhao, Huapeng; Zhang, Lixin

doi:10.1118/1.3556566

Cited by 34 publications

(32 citation statements)

References 31 publications

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“…The two‐dimensional (2D) noise power spectrum (NPS) was calculated for each ROI g , using the following equation:

N P S (f_{i}) = \frac{p_{x} p_{y}}{N_{x} N_{y}} | DFT \{g - \bar{boldg}\} {1.2em1.2emtrue|}^{2}

where p x and p y are the pixel sizes in millimeter and N x and N y are the number of pixels in each dimension. Here,

\bar{g}

is the structured, nonstochastic background which was estimated using a 2D first‐order polynomial fit function . The aforementioned method can be extended to a second‐order polynomial fitting, with the function being defined as:

{\bar{g}}_{m, n}^{'} = C_{1} + C_{2} m + C_{3} n + C_{4} m^{2} + C_{5} n^{2} + C_{6} m n

…”

Section: Methodsmentioning

confidence: 99%

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Technical Note: A feasibility study of using the flat panel detector on linac for the kV x‐ray generator test

et al. 2018

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“…The two‐dimensional (2D) noise power spectrum (NPS) was calculated for each ROI g , using the following equation:

N P S (f_{i}) = \frac{p_{x} p_{y}}{N_{x} N_{y}} | DFT \{g - \bar{boldg}\} {1.2em1.2emtrue|}^{2}

where p x and p y are the pixel sizes in millimeter and N x and N y are the number of pixels in each dimension. Here,

\bar{g}

{\bar{g}}_{m, n}^{'} = C_{1} + C_{2} m + C_{3} n + C_{4} m^{2} + C_{5} n^{2} + C_{6} m n

…”

Section: Methodsmentioning

confidence: 99%

“…It is acceptable to use NPS integrating with peaks for the consistency check. More details on calculation of NPS can be found in literatures …”

Section: Methodsmentioning

confidence: 99%

Technical Note: A feasibility study of using the flat panel detector on linac for the kV x‐ray generator test

et al. 2018

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“…For example, this method was utilized to estimate the background signal of digital X‐ray imaging (26) . In brief, for a ROI with m row elements and n column elements, the first‐order polynomial function fitting for the image background can be defined as:

{\bar{g}}_{m, n}^{'} = C_{1} + C_{2} m + C_{3} n

…”

Section: Methodsmentioning

confidence: 99%

“…Similar to the first‐order polynomial fitting, the second‐order cases can also be utilized to estimate

{\bar{g}}^{normal′}

for a ROI, designated as RSS second‐order (26) . The aforementioned method can be extended to a second‐order polynomial fitting, with the function being defined as:

{\bar{g}}_{m, n}^{'} = C_{1} + C_{2} m + C_{3} n + C_{4} m^{2} + C_{5} n^{2} + C_{6} m n

…”

Section: Methodsmentioning

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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“…Therefore, the acquired images are affected by the fixed pattern noise due to the nonuniform gains. 7,26,33,34 Besides these nonuniform gains, the heel effect from the x-ray tube and the back scattering inside the detector enclosure can cause a fixed pattern noise with a low-frequency trend.…”

Section: Introductionmentioning

confidence: 99%

Noise power spectrum of the fixed pattern noise in digital radiography detectors

Kim

Kim²

2016

Medical Physics

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Techniques to improve the accuracy of noise power spectrum measurements in digital x‐ray imaging based on background trends removal

Cited by 34 publications

References 31 publications

Technical Note: A feasibility study of using the flat panel detector on linac for the kV x‐ray generator test

Technical Note: A feasibility study of using the flat panel detector on linac for the kV x‐ray generator test

Practical considerations for noise power spectra estimation for clinical CT scanners

Noise power spectrum of the fixed pattern noise in digital radiography detectors

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