Scatter Correction Method by Temporal Primary Modulation in X-Ray CT

Schörner, Karsten; Goldammer, Matthias; Stierstorfer, Karl; Stephan, Jürgen; Böni, P.

doi:10.1109/tns.2012.2218127

Cited by 27 publications

(29 citation statements)

References 20 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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“…Despite its theoretical elegance and success as demonstrated in a few publications by different research groups, [6][7][8][9][10]26,27,31 however, several limitations should be noted on FTPM. First, FTPM requires projections to be downsampled for signal processing.…”

Section: A2 Limitations Of the Ftpm Methodsmentioning

confidence: 99%

“…Different research groups have demonstrated the success of this method. [26][27][28] The current signal processing algorithm of primary modulation was developed using the Fourier transform theory, which enables fast computation via filtering techniques. The derivation of the Fourier transformbased primary modulation method (FTPM), however, is established on an assumption of primary signal modulation with uniform modulation frequency and magnitude, which becomes inaccurate in the presence of physical nonidealities, including focal spot wobble, spatially varying x-ray incident angle, and nonuniform beam-hardening effects on the imaged object.…”

Section: Introductionmentioning

confidence: 99%

Local filtration based scatter correction for cone‐beam CT using primary modulation

Zhu

2016

Medical Physics

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Purpose: Excessive scatter contamination fundamentally limits the image quality of cone-beam CT (CBCT), hindering its quantitative use in clinical applications. The author has previously proposed an effective scatter correction method for CBCT using primary modulation. A Fourier transform-based algorithm (FTPM) was implemented to estimate scatter from modulated projections, with a few limitations including the assumption of uniform modulation frequency and magnitude that becomes less accurate in the presence of beam-hardening and other nonideal effects. This paper aims to overcome the above drawbacks by developing a new algorithm for the primary modulation method with improved accuracy and reliability. Methods: Incident x-ray intensities for each detector pixel with and without the interception of the modulator blocker are estimated from a modulated flat-field image. A new signal relationship is then developed to obtain a first scatter estimate from a modulated projection using a spatially varying modulation distribution. The method empirically adjusts the effective modulation magnitude for each projection ray to account for the beam-hardening effects. Estimated scatter signals with high expected errors are discarded in the generation of the final scatter distribution. The author proposes a technique of local filtration to accelerate major portions of the signal processing, and the new algorithm is referred to as local filtration based primary modulation (LFPM). Results: The study on the Catphan® 600 phantom shows that LFPM effectively removes scatterinduced cupping artifacts on CBCT images and reduces the CT image error from 222 to 15 HU. In addition, the image contrast on eight contrast rods of the phantom is enhanced by a factor of 2 on average. On an anthropomorphic head phantom, LFPM reduces the CT image error from 153 to 18 HU and eliminates the streak artifacts observed on the result of FTPM with substantially improved image uniformity. On the Rando® phantom, LFPM reduces the CT image error from 278 to 4 HU around the object center. Conclusions: As compared with the previously developed FTPM algorithm, LFPM enhances the imaging performance by using a more flexible data processing framework that does not require projection data downsampling or uniform modulation frequency and magnitude. It also becomes possible to discard suspicious scatter estimate values prior to the generation of a final scatter distribution and to model the beam-hardening effects on modulation for improved scatter estimation accuracy. The presented research further exploits the potential of the primary modulation method on scatter correction and facilitates its clinical adoption in CBCT imaging. C 2016 American Association of Physicists in Medicine. [http://dx

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“…11 This method uses temporal motion of a modulator pattern to mitigate the sampling and high-frequency related limitations of the Fourier-based modulator approach. The additional complexity required of providing accurate modulator motion may make this approach less attractive for rotating systems such as C-arms or gantry-based devices.…”

Section: Introductionmentioning

confidence: 99%

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

Ritschl¹,

Fahrig

Knaup

et al. 2015

Medical Physics

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Purpose: Scattered radiation is one of the major problems facing image quality in flat detector conebeam computed tomography (CBCT). Previously, a new scatter estimation and correction method using primary beam modulation has been proposed. The original image processing technique used a frequency-domain-based analysis, which proved to be sensitive to the accuracy of the modulator pattern both spatially and in amplitude as well as to the frequency of the modulation pattern. In addition, it cannot account for penumbra effects that occur, for example, due to the finite focal spot size and the scatter estimate can be degraded by high-frequency components of the primary image. Methods: In this paper, the authors present a new way to estimate the scatter using primary modulation. It is less sensitive to modulator nonidealities and most importantly can handle arbitrary modulator shapes and changes in modulator attenuation. The main idea is that the scatter estimation can be expressed as an optimization problem, which yields a separation of the scatter and the primary image. The method is evaluated using simulated and experimental CBCT data. The scattering properties of the modulator itself are analyzed using a Monte Carlo simulation.

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Scatter Correction Method by Temporal Primary Modulation in X-Ray CT

Cited by 27 publications

References 20 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

Local filtration based scatter correction for cone‐beam CT using primary modulation

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

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