Scattering correction using continuously thickness-adapted kernels

Bhatia, Navnina; Tisseur, David; Buyens, Fanny; Létang, Jean Michel

doi:10.1016/j.ndteint.2015.11.004

Cited by 19 publications

(25 citation statements)

References 11 publications

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“…While scatter suppression approaches try to reduce the amount of scattered X-rays reaching the detector using anti-scatter grids or collimators B Joscha Maier joscha.maier@dkfz.de 1 German Cancer Research Center, Im Neuenheimer Feld 280, 69120 Heidelberg, Germany [26], scatter estimation approaches aim at deriving an estimate of the scatter distribution that is used to correct the acquired projection data [27]. Thereby, the scatter estimate can either be derived using dedicated hardware such as beam blockers or primary modulation grids [3,7,8,20,24,28,38,39] or using software-based approaches that rely on physical or empirical models to predict X-ray scattering [1,2,11,17,18,21,22,30,[32][33][34]36,37]. Among these methods the gold standard is to use a Monte Carlo (MC) photon transport code [27].…”

Section: Introductionmentioning

confidence: 99%

Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network

et al. 2018

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X-ray scatter is a major cause of image quality degradation in dimensional CT. Especially, in case of highly attenuating components scatter-to-primary ratios may easily be higher than 1. The corresponding artifacts which appear as cupping or dark streaks in the CT reconstruction may impair a metrological assessment. Therefore, an appropriate scatter correction is crucial. Thereby, the gold standard is to predict the scatter distribution using a Monte Carlo (MC) code and subtract the corresponding scatter estimate from the measured raw data. MC, however, is too slow to be used routinely. To correct for scatter in real-time, we developed the deep scatter estimation (DSE). It uses a deep convolutional neural network which is trained to reproduce the output of MC simulations using only the acquired projection data as input. Once trained, DSE can be applied in real-time. The present study demonstrates the potential of the proposed approach using simulations and measurements. In both cases the DSE yields highly accurate scatter estimates that differ by < 3% from our MC scatter predictions. Further, DSE clearly outperforms kernel-based scatter estimation techniques and hybrid approaches, as they are in use today.

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Section: Introductionmentioning

confidence: 99%

Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network

et al. 2018

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“…In the SKS scatter correction approach based on [5], scatter signal is modeled as the sum of the scatter contributions from a group of pencil beams passing through the object and the detector. The total scatter signal S(m, n) with m and n as the pixel position on the detector, can then be modeled as:…”

Section: A Scatter Correction Using a Continuous Sks Approachmentioning

confidence: 99%

“…The values for parameters A and B were calculated for these discrete sets of kernels. Thereupon, in order to obtain the continuous kernel, we analytically computed the expression for the kernels parameters A and B in terms of the transmission of the object [5]. For this case, we fitted a curve with respect to the transmission using a classical non linear least square fitting technique for each parameter.…”

Section: B Kernel Generation With Mncp6mentioning

confidence: 99%

“…In a previous work [5], the SKS method has been tested with a 2 and 4 Gaussian-model. Fig.2 shows that with our 9 MeV spectrum the best is obtained with a Lorentzian model.…”

Section: B Kernel Generation With Mncp6mentioning

confidence: 99%

“…However, these methods are not very efficient in higher energy range due to higher SPR. In this article, we have applied scatter correction by continuously thickness adapted Scatter Kernel Superposition (SKS) method [5] on a data produced by 9 MeV X-ray photon beam generated from a linear accelerator.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of a scattering correction method for high energy tomography

et al. 2018

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Abstract-One of the main drawbacks of Cone BeamComputed Tomography (CBCT) is the contribution of the scattered photons due to the object and the detector. Scattered photons are deflected from their original path after their interaction with the object. This additional contribution of the scattered photons results in increased measured intensities, since the scattered intensity simply adds to the transmitted intensity. This effect is seen as an overestimation in the measured intensity thus corresponding to an underestimation of absorption. This results in artifacts like cupping, shading, streaks etc. on the reconstructed images. Moreover, the scattered radiation provides a bias for the quantitative tomography reconstruction (for example atomic number and volumic mass measurement with dual-energy technique). The effect can be significant and difficult in the range of MeV energy using large objects due to higher Scatter to Primary Ratio (SPR). Additionally, the incident high energy photons which are scattered by the Compton effect are more forward directed and hence more likely to reach the detector. Moreover, for MeV energy range, the contribution of the photons produced by pair production and Bremsstrahlung process also becomes important. We propose an evaluation of a scattering correction technique based on the method named Scatter Kernel Superposition (SKS). The algorithm uses a continuously thicknessadapted kernels method. The analytical parameterizations of the scatter kernels are derived in terms of material thickness, to form continuously thickness-adapted kernel maps in order to correct the projections. This approach has proved to be efficient in producing better sampling of the kernels with respect to the object thickness. This technique offers applicability over a wide range of imaging conditions and gives users an additional advantage. Moreover, since no extra hardware is required by this approach, it forms a major advantage especially in those cases where experimental complexities must be avoided. This approach has been previously tested successfully in the energy range of 100 keV -6 MeV. In this paper, the kernels are simulated using MCNP in order to take into account both photons and electronic processes in scattering radiation contribution. We present scatter correction results on a large object scanned with a 9 MeV linear accelerator.

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A correlated sampling‐based Monte Carlo simulation for fast CBCT iterative scatter correction

Qin¹,

Lin²,

Li³

et al. 2022

Medical Physics

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Background: In recent years, cone-beam computed tomography (CBCT) has played an important role in medical imaging. However, the applications of CBCT are limited due to the severe scatter contamination. Conventional Monte Carlo (MC) simulation can provide accurate scatter estimation for scatter correction, but the expensive computational cost has always been the bottleneck of MC method in clinical application. Purpose: In this work, an MC simulation method combined with a variance reduction technique called correlated sampling is proposed for fast iterative scatter correction. Methods: Correlated sampling exploits correlation between similar simulation systems to reduce the variance of interest quantities. Specifically, conventional MC simulation is first performed on the scatter-contaminated CBCT to generate the initial scatter signal. In the subsequent correction iterations, scatter estimation is then updated by applying correlated MC sampling to the latest corrected CBCT images by reusing the random number sequences of the task-related photons in conventional MC. Afterward, the corrected projections obtained by subtracting the scatter estimation from raw projections are utilized for FDK reconstruction. These steps are repeated until an adequate scatter correction is obtained. The performance of the proposed framework is evaluated by the accuracy of the scatter estimation, the quality of corrected CBCT images and efficiency. Results: Overall, the difference in mean absolute percentage error between scatter estimation with and without correlated sampling is 0.25% for full-fan case and 0.34% for half -fan case, respectively. In simulation studies, scatter artifacts are substantially eliminated, where the mean absolute error value is reduced from 15 to 2 HU in full-fan case and from 53 to 13 HU in half -fan case. Scatterto-primary ratio is reduced to 0.02 for full-fan and 0.04 for half -fan, respectively. In phantom study, the contrast-to-noise ratio (CNR) is increased by a factor of 1.63, and the contrast is increased by a factor of 1.77. As for clinical studies, the CNR is improved by 11% and 14% for half -fan and full-fan, respectively. The contrast after correction is increased by 19% for half -fan and 44% for fullfan. Furthermore, root mean square error is also effectively reduced, especially from 78 to 4 HU for full-fan. Experimental results demonstrate that the figure of merit is improved between 23 and 43 folds when using correlated sampling. The proposed method takes less than 25 s for the whole iterative scatter correction process. Conclusions:The proposed correlated sampling-based MC simulation method can achieve fast and accurate scatter correction for CBCT, making it suitable for real-time clinical use.

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Scattering correction using continuously thickness-adapted kernels

Cited by 19 publications

References 11 publications

Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network

Deep Scatter Estimation (DSE): Accurate Real-Time Scatter Estimation for X-Ray CT Using a Deep Convolutional Neural Network

Evaluation of a scattering correction method for high energy tomography

A correlated sampling‐based Monte Carlo simulation for fast CBCT iterative scatter correction

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