Optimization of a calibration phantom for quantitative radiography

Martinez, Cristobal; Molina, Claudia de; Desco, Manuel; Abella, Mónica

doi:10.1002/mp.14638

Cited by 5 publications

(5 citation statements)

References 28 publications

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“…DE images obtained with this method is similar to the logsubtraction algorithm in terms of contrast and noise characteristics (Sabol et al 2001). A linear decomposition assumes a monoenergetic beam (Li et al 2012), therefore, non-linear decomposition approaches were proposed to account for beam hardening through a calibration process (Brody et al 1981, Cardinal and Fenster 1990, Lee et al 2020, Martinez et al 2021. In particular Martinez et al (2021) described an automatic calibration for material decompression method, avoiding the need for a radiology technician with expert knowledge.…”

Section: Discussionmentioning

confidence: 99%

“…A linear decomposition assumes a monoenergetic beam (Li et al 2012), therefore, non-linear decomposition approaches were proposed to account for beam hardening through a calibration process (Brody et al 1981, Cardinal and Fenster 1990, Lee et al 2020, Martinez et al 2021. In particular Martinez et al (2021) described an automatic calibration for material decompression method, avoiding the need for a radiology technician with expert knowledge. Similarly, the calibration process for the log-subtraction DE algorithm applied in this study and previous studies (Darvish-Molla and Sattarivand 2019, Haytmyradov et al 2020) corrects for beam hardening effects.…”

Section: Discussionmentioning

confidence: 99%

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Adaptive dual-energy algorithm based on pre-calibrated weighting factors for chest radiography

Romadanov¹,

Abeywardhana²,

Sattarivand³

2022

Phys. Med. Biol.

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Objective: To develop a dual-energy (DE) algorithm with spatially varying weighting factors for material selection and noise suppression. Approach: Calibration step-phantoms, with overlapping slabs of solid water and bone with different thicknesses, were used to obtain the pre-calibrated material selection and noise reduction weighting factors. The Material selection weighting factors were calculated by finding a zero of contrast-to-noise-ratio (CNR) between regions with two overlapping materials and regions of only target material, while noise suppression weighting factors were determined by maximizing signal-to-noise ratio for overlapping regions. The pre-calibrated weighting factors were fitted with low and high energy radiograph of two Rando phantoms to create maps of material selection and noise suppression weighting factors, which used with duel energy (DE) algorithm and anti-correlated noise reduction (ACNR) algorithm to generate DE images. Three different implementations, including two different sizes of Rando phantoms and two different orientations (oblique and Anterior-Posterior), were investigated. Soft-tissue and bone only images of Rando phantoms were obtained with five combination of DE algorithms and CNR, contrast, and noise values of selected regions of interest (ROIs) were compared to evaluate the performance of the novel method: simple log subtraction (SLS), SLS with uniform ACNR, adaptive DE (aDE), aDE with uniform ACNR, and aDE & adaptive ACNR (aACNR). Main results: Compared to SLS, the aDE algorithm demonstrated improved image quality in all three orientations. CNR increased with better contrast for both soft-tissue and bone images. Implementation of aACNR algorithm resulted in further reduction of image noise and improvements in CNR at the cost of contrast. However, aACNR algorithm showed better contrast comparing to ACNR method. Significance: A novel DE algorithm was proposed, which showed improved material selection and noise suppression as compared to the conventional DE techniques and can be easily implemented in a clinical environment for real-time DE image generation.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Adaptive dual-energy algorithm based on pre-calibrated weighting factors for chest radiography

Romadanov¹,

Abeywardhana²,

Sattarivand³

2022

Phys. Med. Biol.

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“…A realistic calibration phantom can be made up of PMMA as an equivalent of soft-tissue and aluminum 6082 (AL6082) for bone, as proposed in Martinez et al (2020). While PMMA is a good substitute for soft tissue in terms of beam hardening, AL6082 does not match bone very well in this regard.…”

Section: Tissue Equivalent Materialsmentioning

confidence: 99%

Simple beam hardening correction method (2DCalBH) based on 2D linearization

Martinez¹,

Fessler²,

Desco³

et al. 2022

Phys. Med. Biol.

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Objective: The polychromatic nature of the X-ray spectrum in CT leads to two types of artifacts in the reconstructed image: cupping in homogeneous areas and dark bands between dense parts, such as bones. This fact, together with the energy dependence of the mass attenuation coefficients of the tissues, results in erroneous values in the reconstructed image. Many post-processing correction schemes previously proposed require either knowledge of the X-ray spectrum or the heuristic selection of some parameters that have been shown to be suboptimal for correcting different slices in heterogeneous studies. In this study, we propose and validate a method to correct the beam hardening artifacts that avoids such restrictions and restores the quantitative character of the image. Approach: Our approach extends the idea of the water-linearization method. It uses a simple calibration phantom to characterize the attenuation for different soft tissue and bone combinations of the X-ray source polychromatic beam. The correction is based on the bone thickness traversed, obtained from a preliminary reconstruction. We evaluate the proposed method with simulations and real data using a phantom composed of PMMA and aluminum 6082 as materials equivalent to water and bone. Main results: Evaluation in simulated data showed a correction of the artifacts and a recovery of monochromatic values similar to that of post-processing techniques used for comparison, while it outperformed them on real data. Significance: The proposed method corrects beam hardening artifacts and restores monochromatic attenuation values with no need of spectrum knowledge or heuristic parameter tuning, based on the previous acquisition of a very simple calibration phantom.

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“…(4) (5) where μj imply the linear-attenuation coefficient of the specific material, wf is the weighting factor for suppression of the non-interest tissue in DE image, and wf for bone suppression is defined as follows: (6) The doses delivered to the patient by LE and HE are 0.01 to 0.09 mGy, respectively, and the total doses are 0.1 mGy. Therefore, the dose allocation (ξ, values between 0 and 1) is defined as: (7)…”

Section: Dual-energy X-ray Imagingmentioning

confidence: 99%

“…Jeon et al [5] investigated the image quality of virtual monochromatic images synthesized from DE computed tomography, and found that the DE scan mode with the 100/140 kV protocol achieved a better maximum contrastto-noise ratio (CNR), compared to the 80/140 kV protocol for various materials, except for adipose and brain. We noted that a basis material-decomposition using a calibration phantom had been applied for DE radiography [6].…”

Section: Introductionmentioning

confidence: 99%

Study on Dual-Energy Signal and Noise of Double-Exposure X-Ray Imaging for High Conspicuity

Song

Kim

2021

J Radiat Prot Res

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Background: Dual-energy X-ray images (DEI) can distinguish or improve materials of interest in a two-dimensional radiographic image, by combining two images obtained from separate low and high energies. The concepts of DEI performance describing the performance of doubleexposure DEI systems in the Fourier domain been previously introduced, however, the performance of double-exposure DEI itself in terms of various parameters, has not been reported. Materials and Methods:To investigate the DEI performance, signal-difference-to-noise ratio, modulation transfer function, noise power spectrum, and noise equivalent quanta were used. Low-and high-energy were 60 and 130 kVp with 0.01-0.09 mGy, respectively. The energyseparation filter material and its thicknesses were tin (Sn) and 0.0-1.0 mm, respectively. Noisereduction (NR) filtering used the Gaussian-filter NR, median-filter NR, and anti-correlated NR.Results and Discussion: DEI performance was affected by Sn-filter thickness, weighting factor, and dose allocation. All NR filtering successfully reduced noise, when compared with the dual-energy (DE) images without any NR filtering. Conclusion:The results indicated the significance of investigating, and evaluating suitable DEI performance, for DE images in chest radiography applications. Additionally, all the NR filtering methods were effective at reducing noise in the resultant DE images.

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Optimization of a calibration phantom for quantitative radiography

Cited by 5 publications

References 28 publications

Adaptive dual-energy algorithm based on pre-calibrated weighting factors for chest radiography

Adaptive dual-energy algorithm based on pre-calibrated weighting factors for chest radiography

Simple beam hardening correction method (2DCalBH) based on 2D linearization

Study on Dual-Energy Signal and Noise of Double-Exposure X-Ray Imaging for High Conspicuity

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