New functionalities of SINDBAD simulation software for spectral X-ray imaging using counting detectors with energy discrimination

Rebuffel, V.; Tabary, Joachim; Hugonnard, Patrick; Popa, Emil M.; Brambilla, A.; Montémont, G.; Verger, L.

doi:10.1109/nssmic.2012.6551583

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Measurements were generated with the software Sindbad-SFFD 7 . The study phantom is a 30 cm diameter cylinder of water with six 5 cm diameter inserts.…”

Section: Methodsmentioning

confidence: 99%

“…Each insert is made of a different material such as cortical bone, a less attenuating bone material named compact bone, aluminum, adipose tissue, Polyethylene and Plexiglas. The source was set to 140kV with 0.1 cm aluminum filtering for an x-ray exposure of 5x10 7 per pixel. The spectral convolution for this simulation was performed using a realistic DRM (cf Fig.…”

Section: Methodsmentioning

confidence: 99%

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Spectral CT reconstruction with an explicit photon-counting detector model: a one-step approach

Rodesch

Rebuffel

Fournier

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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To cite this version:Pierre-Antoine Rodesch, V. Rebuffel, C. Fournier, Florence Forbes, L. Verger. Spectral CT reconstruction with an explicit photon-counting detector model: a " one-step " approach. SPIE Medical Imaging, Feb 2018 ABSTRACTRecent developments in energy-discriminating Photon-Counting Detector (PCD) enable new horizons for spectral CT. With PCDs, new reconstruction methods take advantage of the spectral information measured through energy measurement bins. However PCDs have serious spectral distortion issues due to charge-sharing, fluorescence escape, pileup effect… Spectral CT with PCDs can be decomposed into two problems: a noisy geometric inversion problem (as in standard CT) and an additional PCD spectral degradation problem. The aim of the present study is to introduce a reconstruction method which solves both problems simultaneously: a "one-step" approach. An explicit linear detector model is used and characterized by a Detector Response Matrix (DRM). The algorithm reconstructs two basis material maps from energy-window transmission data. The results prove that the simultaneous inversion of both problems is well performed for simulation data. For comparison, we also perform a standard "two-step" approach: an advanced polynomial decomposition of measured sinograms combined with a filtered-back projection reconstruction. The results demonstrate the potential uses of this method for medical imaging or for non-destructive control in industry.

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“…Measurements were generated with the software Sindbad-SFFD 7 . The study phantom is a 30 cm diameter cylinder of water with six 5 cm diameter inserts.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Spectral CT reconstruction with an explicit photon-counting detector model: a one-step approach

Rodesch

Rebuffel

Fournier

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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“…A spectral mammography acquisition chain was simulated for the need of this study including a spectrum generation, an attenuating object described in the section above, a detector response function and a noise model. An incident spectrum was simulated using the Sindbad software (Rebuffel et al 2012) with a 1 keV resolution for the output spectrum. A tungsten anode was used with 49 kVp high voltage value that is above the iodine K-edge at 33.2 keV, a Be 0.8 mm inherent and Al 1.2 mm additional filtrations were also included (mean energy of 29.5 keV, HVL = 1.8 mm Al).…”

Section: Spectral Measurement Simulationmentioning

confidence: 99%

“…The DRM is obtained by simulating interactions of photons and electrons with a semiconductor material and charge transport to the anode with a Monte-Carlo code (Montémont et al 2007). The accuracy of this modelling had been demonstrated experimentally (Rebuffel et al 2012). The spectral response was calculated for a CdTe detector, presented in figure 3, with a thickness of 650 and a 60 μm pixel pitch.…”

Section: Spectral Measurement Simulationmentioning

confidence: 99%

Breast density and iodine quantification in spectral mammography

Pavia

Brambilla

Rebuffel

et al. 2017

Biomed. Phys. Eng. Express

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Breast density is known for being a parameter used in breast cancer risk models and might be measured during mammography exams. Automated methods based on grayscale values already exist, but they only allow a density estimation over the whole breast. Material decomposition approaches make it possible to define the breast density at each pixel based on a dual energy exposure. Furthermore, the use of contrast enhanced digital mammography exams is increasing and helps physicians to highlight blood vessels with higher permeability that may be related to malignant lesions. Nevertheless, this technique also requires two x-ray exposures. In this paper, we propose to take benefit of energy sensitive x-ray detectors that give access to several energy measurements in a single shot acquisition to both quantify breast density and iodine during a single mammography exam. To this purpose, a spectral mammography acquisition chain has been simulated and different material decomposition methods have been proposed and investigated using a simulated phantom under clinically achievable settings with an average absorbed dose by the mammary gland of 0.93 mGy (mimicking a 45 mm thick breast made of 50% fibroglandular tissue) using a 49 kVp with a tungsten anode, an inherent filtration of 0.8 mmBe and an additional filtration of 1.2 mmAl. Breast density is commonly defined as the ratio between glandular tissue and the sum of adipose and glandular tissues thicknesses. Standard polynomial material decomposition methods have been extended to make them compliant with 3-energy measurements in order to allow a three-material decomposition: adipose and glandular tissues as well as iodine concentration. Those methods allow to estimate iodine concentration with an average accuracy inferior to 0.15 mg ml −1 and breast density with a precision inferior to 8% (root-mean-square errors). Nevertheless, these methods are difficult to extend to more energy channels due to the least-square model inversion. To achieve better results, a maximum log-likelihood approach has been introduced to take into consideration more spectral information while improving accuracy and precision on measurements. Moreover, the use of the proposed method permits to accurately detect the absence of iodine and significantly improves the estimation of breast density compared to polynomial approaches. We also highlighted that there is a strong interest to increase the number of energy thresholds from 3 to 6, a less significant gain is still noticeable with more energy bins. RECEIVED

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“…where D is the detector response matrix (DRM) (Rebuffel et al 2012), μ q ( p) is the linear attenuation coefficient function of a given material q ∈ {1, …, Q} traversed along the ray path between the source and the pixel, l q ( p) is the length of the respective material. The symbol corresponds to a Hadamard product.…”

Section: Primary and Scattered Radiation In Multi-energy X-ray Imagingmentioning

confidence: 99%

A novel scatter separation method for multi-energy x-ray imaging

Sossin¹,

Rebuffel²,

Tabary³

et al. 2016

Phys. Med. Biol.

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X-ray imaging coupled with recently emerged energy-resolved photon counting detectors provides the ability to differentiate material components and to estimate their respective thicknesses. However, such techniques require highly accurate images. The presence of scattered radiation leads to a loss of spatial contrast and, more importantly, a bias in radiographic material imaging and artefacts in computed tomography (CT). The aim of the present study was to introduce and evaluate a partial attenuation spectral scatter separation approach (PASSSA) adapted for multi-energy imaging. This evaluation was carried out with the aid of numerical simulations provided by an internal simulation tool, Sindbad-SFFD. A simplified numerical thorax phantom placed in a CT geometry was used. The attenuation images and CT slices obtained from corrected data showed a remarkable increase in local contrast and internal structure detectability when compared to uncorrected images. Scatter induced bias was also substantially decreased. In terms of quantitative performance, the developed approach proved to be quite accurate as well. The average normalized root-mean-square error between the uncorrected projections and the reference primary projections was around 23%. The application of PASSSA reduced this error to around 5%. Finally, in terms of voxel value accuracy, an increase by a factor >10 was observed for most inspected volumes-of-interest, when comparing the corrected and uncorrected total volumes.

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New functionalities of SINDBAD simulation software for spectral X-ray imaging using counting detectors with energy discrimination

Cited by 6 publications

References 10 publications

Spectral CT reconstruction with an explicit photon-counting detector model: a one-step approach

Spectral CT reconstruction with an explicit photon-counting detector model: a one-step approach

Breast density and iodine quantification in spectral mammography

A novel scatter separation method for multi-energy x-ray imaging

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