Analysis of the imaging performance in indirect digital mammography detectors by linear systems and signal detection models

Liaparinos, P.; Kalyvas, N.; Kandarakis, I.; Cavouras, D.

doi:10.1016/j.nima.2012.08.014

Cited by 9 publications

(9 citation statements)

References 48 publications

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“…In Table 1 , the results of the ANOVA test are demonstrated. As an initial choice, the large area lesions of 40 × 40 pixels were selected, since the lesions with larger areas are easier to distinguish [ 9 , 31 , 36 ]. It can be seen in Table 1 that, for the Mo/Mo 28 kVp irradiation conditions for the 4.2 cm breast thickness and the 0.1 cm lesion thickness, the adipose tissue is statistically comparable with the breast tissue background ( p > 0.05), while for the blood and the muscle tissue, the signal is significantly different.…”

Section: Resultsmentioning

confidence: 99%

“…The previously described software phantom was assumed to be irradiated by four mammographic X-ray spectra with anode/filter KVp combination as follows [ 31 ]: (a) Mo/Mo 28 kVp, (b) Mo/Mo 32 kVp, (c) W/Rh 28 kVp and (d) W/Rh 32 kVp. The Mo filtration was 0.03 mm and the Rh filtration was 0.06 mm [ 31 ]. The entrance surface air KERMA of the spectra was 5 mGy except in the case of Mo/Mo 32 kVp, which was 3 mGy.…”

Section: Methodsmentioning

confidence: 99%

“…In order to calculate the incident exposure and subsequently the KERMA on the detector surface (the part of the X-ray spectrum that has propagated through the simulated phantom), we have employed an equation that considers that the X-ray spectrum [ 7 , 16 ] is exponentially attenuated in the tissues [ 31 ], as provided below:

where f ( E ) is the X-ray fluence, µ 1 ( E ) is the X-ray attenuation coefficient of breast tissue for energy E , T is the breast thickness and ( µ /ρ) en,air is the mass energy absorption coefficient of air. For the other substances with a thickness t, Equation (1) was adjusted as:

where µ 2 i ( E ) is the attenuation coefficient of tissue i , that is, blood, muscle, adipose, Ca and Ca(50%)-P(50%) tissue.…”

Section: Methodsmentioning

confidence: 99%

“…The image creation procedure involves X-ray absorption in the image detector, creation of secondary quanta (electrons in direct conversion detectors or optical photons in indirect conversion detectors [ 9 , 13 , 17 , 31 , 32 , 33 , 34 , 35 , 36 ]), the spread of the secondary quanta to the output and the interaction in its electronic part. Therefore, the final image is characterized by visual unsharpness (i.e., blur) in object details and noise due to the statistical properties of signal propagation [ 9 , 10 , 11 , 12 , 13 , 14 , 31 ]. The blur can be described in the spatial frequency domain via the MTF .…”

Section: Methodsmentioning

confidence: 99%

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A Novel Method to Model Image Creation Based on Mammographic Sensors Performance Parameters: A Theoretical Study

Kalyvas

Chamogeorgaki

Michail

et al. 2023

Sensors

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Background: Mammographic digital imaging is based on X-ray sensors with solid image quality characteristics. These primarily include (a) a response curve that yields high contrast and image latitude, (b) a frequency response given by the Modulation Transfer Function (MTF), which enables small detail imaging and (c) the Normalize Noise Power Spectrum (NNPS) that shows the extent of the noise effect on image clarity. Methods: In this work, a methodological approach is introduced and described for creating digital phantom images based on the measured image quality properties of the sensor. For this purpose, a mathematical phantom, simulating breast tissue and lesions of blood, adipose, muscle, Ca and Ca(50%)-P(50%) was created by considering the corresponding X-ray attenuation coefficients. The simulated irradiation conditions of the phantom used four mammographic spectra assuming exponential attenuation. Published data regarding noise and blur of a commercial RadEye HR CMOS imaging sensor were used as input data for the resulting images. Results: It was found that the Ca and Ca(50%)-P(50%) lesions were visible in all exposure conditions. In addition, the W/Rh spectrum at 28 kVp provided more detailed images than the corresponding Mo/Mo spectrum. Conclusions: The presented methodology can act complementarily to image quality measurements, leading to initial optimization of the X-ray exposure parameters per clinical condition.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

where µ 2 i ( E ) is the attenuation coefficient of tissue i , that is, blood, muscle, adipose, Ca and Ca(50%)-P(50%) tissue.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Method to Model Image Creation Based on Mammographic Sensors Performance Parameters: A Theoretical Study

Kalyvas

Chamogeorgaki

Michail

et al. 2023

Sensors

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show abstract

“…Monte Carlo) or the use of Linear Cascaded Systems Theory (LCST) where the stages of image formation from X-ray absorption to pixel creation can be represented by statistical processes. LCST theory has been utilized in the past to determine general mathematical formulas which describe noise and signal transfer in the detector [3]- [9]. In addition, LCST processes have been successfully employed to investigate the image quality metrics of CR systems [10].…”

Section: Introductionmentioning

confidence: 99%

Effect of Reader Software in Image Quality Metrics of X-Ray Computed Radiography Systems

Galanopoulou¹,

Katsigiannis²,

Bakas³

et al. 2022

RAP Conference Proceedings

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X-rays are used in medical imaging to acquire information from inside the human body. The quality of the information is affected by the tube voltage responsible for X-ray penetration and contrast as well as the tube load which affects image noise. Another important part is the X-ray detector. It consists either of a scintillator component coupled to semiconductor (indirect detection) or only of a semiconductor part that directly converts the X-rays to electron-hole pairs which impinge onto an electronic circuit (direct detection). An intermediate solution is the use of a Computed Radiography cassette (CR) which has a scintillator with introduced defaults. These defaults act as traps for the radiation excited electrons and prohibit the spontaneous optical photon generation. The cassette is then excited by a LASER beam provoking the de-excitation of the trapped information carriers. The optical photons generated are collected by a photocathode digitized and presented as an image. The image is further manipulated in an automated manner depending upon the examination. The purpose of this work is to examine the effect of the automated software manipulation to the image quality metrics. A theoretical model based in the linear cascade system theory was utilized. The model has considered the incident X-rays, their absorption in the CR, the generation and trap of electrons, the optical photon generation emission and capture at the photocathode. The model predicted the electrons per incident X-ray as well as the pre-sampled Modulation Transfer Function (MTF) which defines the spatial resolution of the system. The data needed for the model were obtained from literature. The calculation of optical photon transport was done by an analytical solution of Boltzmann diffusion equation. In order to find the effect of the software a PTW edge phantom was irradiated by a BMI GMM X-ray generator and imaged by a FujiFilm ST-VI cassette and a Capsule-X scanner. The images were shown in 'chest', 'patella' and 'PDR' mode to simulate a high latitude, a high contrast and a generic imaging window respectively. The MTF was estimated by Fourier transforming a differentiated edge profile. The contrast was obtained by irradiating the Artinis CDRAD low contrast PMMA phantom and 3Dprinted PLA phantom, both for 'breast' imaging conditions. The data were processed through ImageJ and Octave free software. The best MTF agreement was found for patella imaging conditions. It was found that the image contrast was affected by the phantom material. The PMMA phantom showed better agreement with the experimental results. Since image quality parameters are phantom material based, each new phantom should have a reference image.

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Visualization of blurring process due to analog components in a digital radiography system using a simple method

Maruyama

2020

Phys Eng Sci Med

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Analysis of the imaging performance in indirect digital mammography detectors by linear systems and signal detection models

Cited by 9 publications

References 48 publications

A Novel Method to Model Image Creation Based on Mammographic Sensors Performance Parameters: A Theoretical Study

A Novel Method to Model Image Creation Based on Mammographic Sensors Performance Parameters: A Theoretical Study

Effect of Reader Software in Image Quality Metrics of X-Ray Computed Radiography Systems

Visualization of blurring process due to analog components in a digital radiography system using a simple method

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