Robust primary modulation‐based scatter estimation for cone‐beam CT

Ritschl, Ludwig; Fahrig, Rebecca; Knaup, Michael; Maier, Joscha; Kachelrieß, Marc

doi:10.1118/1.4903261

Cited by 32 publications

(47 citation statements)

References 19 publications

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“…Various strategies have been proposed to estimate the scatter signal in projection images including analytical calculation, Monte Carlo (MC) simulation and beam blocker‐based techniques . Analytical calculation methods employ a kernel to decompose a measured projection into the scatter and primary components.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the geometry and speed of a moving blocker system for cone‐beam computed tomography scatter correction

Chen

Ouyang

Yan³

et al. 2017

Medical Physics

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Purpose X-ray scatter is a significant barrier to image quality improvements in cone-beam computed tomography (CBCT). A moving blocker-based strategy was previously proposed to simultaneously estimate scatter and reconstruct the complete volume within the field of view (FOV) from a single CBCT scan. A blocker consisting of lead stripes is inserted between the x-ray source and the imaging object, and moves back and forth along the rotation axis during gantry rotation. While promising results were obtained in our previous studies, the geometric design and moving speed of the blocker were set empirically. The goal of this work is to optimize the geometry and speed of the moving block system. Methods Performance of the blocker was examined through Monte Carlo (MC) simulation and experimental studies with various geometry designs and moving speeds. All hypothetical designs employed an anthropomorphic pelvic phantom. The scatter estimation accuracy was quantified by using lead stripes ranging from 5 to 100 pixels on the detector plane. An iterative reconstruction based on total variation minimization was used to reconstruct CBCT images from unblocked projection data after scatter correction. The reconstructed image was evaluated under various combinations of lead strip width and interspace (ranging from 10 to 60 pixels) and different moving speed (ranging from 1 to 30 pixels per projection). Results MC simulation showed that the scatter estimation error varied from 0.8% to 5.8%. Phantom experiment showed that CT number error in the reconstructed CBCT images varied from 13 to 35. Highest reconstruction accuracy was achieved when the strip width was 20 pixels and interspace was 60 pixels and the moving speed was 15 pixels per projection. Conclusions Scatter estimation can be achieved in a large range of lead strip width and interspace combinations. The moving speed does not have a very strong effect on reconstruction result if it is above 5 pixels per projection. Geometry design of the blocker affected image reconstruction accuracy more. The optimal geometry of the blocker has a strip width of 20 pixels and an interspace three times the strip width, which means 25% detector is covered by the blocker, while the optimal moving speed is 15 pixels per projection.

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Section: Introductionmentioning

confidence: 99%

Optimization of the geometry and speed of a moving blocker system for cone‐beam computed tomography scatter correction

Chen

Ouyang

Yan³

et al. 2017

Medical Physics

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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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“…The iPMSE scatter correction algorithm of Ritschl et al. is briefly explained in the following paragraph to outline the challenges to transfer the algorithm to a clinical C‐arm CT system . In order to estimate the scatter, the algorithm requires two projection images: a projection of the modulator only, called reference modulator image m , and a projection with the modulator and the object in place, called

{bold-italicc}_{bold-italicm}

in the following.…”

Section: Methodsmentioning

confidence: 99%

Scatter correction using a primary modulator on a clinical angiography C‐arm CT system

et al. 2017

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Purpose: Cone beam computed tomography (CBCT) suffers from a large amount of scatter, resulting in severe scatter artifacts in the reconstructions. Recently, a new scatter correction approach, called improved primary modulator scatter estimation (iPMSE), was introduced. That approach utilizes a primary modulator that is inserted between the X-ray source and the object. This modulation enables estimation of the scatter in the projection domain by optimizing an objective function with respect to the scatter estimate. Up to now the approach has not been implemented on a clinical angiography C-arm CT system. Methods: In our work, the iPMSE method is transferred to a clinical C-arm CBCT. Additional processing steps are added in order to compensate for the C-arm scanner motion and the automatic X-ray tube current modulation. These challenges were overcome by establishing a reference modulator database and a block-matching algorithm. Experiments with phantom and experimental in vivo data were performed to evaluate the method. Results: We show that scatter correction using primary modulation is possible on a clinical C-arm CBCT. Scatter artifacts in the reconstructions are reduced with the newly extended method. Compared to a scan with a narrow collimation, our approach showed superior results with an improvement of the contrast and the contrast-to-noise ratio for the phantom experiments. In vivo data are evaluated by comparing the results with a scan with a narrow collimation and with a constant scatter correction approach. Conclusions: Scatter correction using primary modulation is possible on a clinical CBCT by compensating for the scanner motion and the tube current modulation. Scatter artifacts could be reduced in the reconstructions of phantom scans and in experimental in vivo data.

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Robust primary modulation‐based scatter estimation for cone‐beam CT

Cited by 32 publications

References 19 publications

Optimization of the geometry and speed of a moving blocker system for cone‐beam computed tomography scatter correction

Optimization of the geometry and speed of a moving blocker system for cone‐beam computed tomography scatter correction

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

Scatter correction using a primary modulator on a clinical angiography C‐arm CT system

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