Performance of a new star‐bar phantom designed for MTF calculations in x‐ray imaging systems

González‐López, Antonio; Campos-Morcillo, Pedro-Antonio; Vera-Sánchez, Juan Antonio; Ruiz-Morales, Carmen

doi:10.1002/mp.14426

Cited by 6 publications

(7 citation statements)

References 25 publications

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“…The MTF was calculated from the star–bar images 20 by averaging the different MTFs obtained from the different images. In this way, uncertainties in the NEQ calculations were reduced.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to NEQ calculations, a simulated node detection task was carried out to compare the star‐based results. Images of a 5‐mm diameter disk with the same attenuation properties as those of the star–bar phantom 20 for the beam qualities studied in this work were generated. Then, a detectability index

d_{nod}^{2}

was calculated by numerically integrating

d_{italicnod}^{2} = \int \frac{| {\overset{f}{true}}_{s} ‐ {\overset{f}{true}}_{italicas} {false|}^{2} M T F^{2}}{italicNPS},

where

{\overset{false~}{f}}_{s}

and

{\overset{false~}{f}}_{as}

are the Fourier transforms of the image

f_{s}

containing the node and the background

f_{as}

, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to NEQ calculations, a simulated node detection task was carried out to compare the star-based results. Images of a 5-mm diameter disk with the same attenuation properties as those of the star-bar phantom 20 for the beam qualities studied in this work were generated. Then, a detectability index d 2 nod was calculated by numerically integrating…”

Section: B Images and Proceduresmentioning

confidence: 99%

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Detectability assessment of an x‐ray imaging system using the nodes in a wavelet packet decomposition of a star–bar object

González‐López

2021

Medical Physics

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Purpose Using linear transformations of the acquired data can expand the study of detectability in an imaging system. From one image, an appropriate transformation will produce a set of signals with different contrast and different frequency contents. In this work this strategy is explored to present a task‐based test for the detectability of an x‐ray imaging system. Methods Images of a new star–bar phantom are acquired with different entrance air KERMA and with different beam qualities. Then, after a wavelet packet is applied to both input and output of the system, statistical decision theory is applied to determine detectability of the different images or nodes resulting from the transformation. An ideal Bayesian observer (IBO) is applied to the data in the spatial domain to perform ROC analysis and to determine a detectability index for each of the nodes. In addition, image quality is characterized in terms of noise equivalent quanta (NEQ) and a 5 mm nodule detection task is performed. Results AUC maps resulting from the analysis show the area under the ROC curve over the whole 2D frequency space for the different doses and beam qualities. Also, AUC curves, obtained by radially averaging the AUC maps, allow comparing detectability of the different techniques as a function of the frequency in a single figure. The results obtained show differences between images acquired with different doses for each of the beam qualities analyzed. Classifying image quality by means of detectability indexes agrees with that of the AUC curves and the nodule detection task but differs from the NEQ for the low air KERMA images. Conclusions Combining a star–bar as test object, a wavelet packet as linear transformation and ROC analysis provides an appropriate task‐based test for detectability performance of an imaging system. The test presented in this work produces maps and curves quantifying system detectability as a function of the frequency characterizing the signal to detect and allows calculating detectability differences between different acquisition techniques.

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“…The MTF was calculated from the star–bar images 20 by averaging the different MTFs obtained from the different images. In this way, uncertainties in the NEQ calculations were reduced.…”

Section: Methodsmentioning

confidence: 99%

d_{nod}^{2}

was calculated by numerically integrating

d_{italicnod}^{2} = \int \frac{| {\overset{f}{true}}_{s} ‐ {\overset{f}{true}}_{italicas} {false|}^{2} M T F^{2}}{italicNPS},

where

{\overset{false~}{f}}_{s}

and

{\overset{false~}{f}}_{as}

are the Fourier transforms of the image

f_{s}

containing the node and the background

f_{as}

, respectively.…”

Section: Methodsmentioning

confidence: 99%

Section: B Images and Proceduresmentioning

confidence: 99%

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Detectability assessment of an x‐ray imaging system using the nodes in a wavelet packet decomposition of a star–bar object

González‐López

2021

Medical Physics

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“…We propose a novel test object for estimating the MTF function in the transversal cross section and along the Z-axis of the cylindrical field of view. The test object combines the concepts of a star-bar phantom [31] and a Defrise phantom [34].…”

Section: D Test Objectmentioning

confidence: 99%

Image reconstruction using Z-axis spatio-temporal sampling in 3D electrical capacitance tomography

Wanta

Smolik

Kryszyn

et al. 2022

Meas. Sci. Technol.

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A 3D visualization raises interest in electrical capacitance tomography (ECT) due to the progress in the development of capacitance measurement circuits and data acquisition systems. This paper proposes image reconstruction using spatio-temporal sampling in the Z-axis for 3D ECT to improve spatial image resolution. In a typical application of ECT, multi-phase flow imaging is performed through 2D data acquisition and two-dimensional reconstruction of image frames over time. In the presented method, a volume of interest is reconstructed from several time samples obtained using a 3D sensor. The procedure of Jacobian matrix extension for the spatio-temporal data was described. The experiments were carried out using the EVT4 system and a 32 electrodes sensor. The reconstruction results of a volume of interest from a single 3D measurement and spatio-temporal sampling were compared. A phantom for 3D imaging to assess spatial image resolution using a modulation transfer function was proposed.

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“…[ 9 10 11 14 15 16 17 ] Furthermore, some studies are being conducted for developing new measurement methods to obtain accurate MTFs for imaging systems. [ 18 19 20 ]…”

Section: Introductionmentioning

confidence: 99%

Assessment of Uncertainty Depending on Various Conditions in Modulation Transfer Function Calculation Using the Edge Method

Maruyama

2021

Journal of Medical Physics

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In medical X-ray imaging, to perform optimal operations, it is essential for the user to understand whether a required image quality level which depends on a diagnostic task can be achieved with the imaging system used. This study focuses on the effects of noise on the modulation transfer function (MTF) using the edge method, the most widely used to evaluate the task dependence property. The purpose is to verify the uncertainty of the MTF value at each spatial frequency and examine the conditions under which the accuracy is ensured. By using a Monte Carlo simulation, edge images with various contrast-to-noise ratio (CNR) are acquired. MTFs are then calculated with different edge spread function (ESF) lengths. The uncertainties for each spatial frequency are estimated based on the independent MTF calculations obtained from the five edge data. The uncertainty of the MTF is inversely proportional to the CNR. In the frequency range up to the Nyquist frequency, the uncertainty in five calculations is <0.01 when the CNR is more than 60. In addition, it is observed that the uncertainty increases as the ESF length increases. This relationship depends on the frequency range, but it is proportional to the 0.3–0.5 power of the ESF length. The results in which the uncertainty is most likely to be large in the MTF calculation are clearly shown. Therefore, it is expected to provide an important barometer and useful insights for a proper image quality measurement.

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Performance of a new star‐bar phantom designed for MTF calculations in x‐ray imaging systems

Cited by 6 publications

References 25 publications

Detectability assessment of an x‐ray imaging system using the nodes in a wavelet packet decomposition of a star–bar object

Detectability assessment of an x‐ray imaging system using the nodes in a wavelet packet decomposition of a star–bar object

Image reconstruction using Z-axis spatio-temporal sampling in 3D electrical capacitance tomography

Assessment of Uncertainty Depending on Various Conditions in Modulation Transfer Function Calculation Using the Edge Method

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