An iterative approach to the beam hardening correction in cone beam CT

Hsieh, Jiang; Molthen, Robert C.; Dawson, Christopher A.; Johnson, Roger H.

doi:10.1118/1.598853

Cited by 243 publications

(163 citation statements)

References 7 publications

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“…Scatter is a very important artifact causing factor in CBCT. The scatter‐to‐primary ratio (SPR) is about 0.01 for single‐ray CT and 0.05–0.15 for fan‐beam and spiral CT, and may be as large as 0.4–2.0 in CBCT 10, 11, 12, 13, 29, 30, 31. Typical scatter artifacts show as shading or streaks, which would result in reduced contrast resolution and increased noise in CBCT imaging.…”

Section: Discussionmentioning

confidence: 99%

“…During daily treatment, cone beam CT (CBCT) mounted on the gantry of linear accelerator is used to detect target position relative to the planned radiation beams to improve the accuracy of treatment delivery through geometric corrections. Target detectability of CBCT is a very important image quality metric to achieve a high level of patient positioning and treatment accuracy 10, 11, 12, 13. In spite of the increasing use of CBCT to verify and correct patient setup, the contrast resolution of CBCT in delineating soft tissue structures is lower than that of MDCT 14, 15.…”

Section: Introductionmentioning

confidence: 99%

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Optimizing the target detectability of cone beam CT performed in image‐guided radiation therapy for patients of different body sizes

Yang

Ruan

et al. 2018

J Applied Clin Med Phys

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PurposeThe target detectability of cone beam computed tomography (CBCT) performed in image‐guided radiation therapy (IGRT) was investigated to achieve sufficient image quality for patient positioning over a course of treatment session while maintaining radiation exposure from CBCT imaging as low as reasonably achievable (ALARA).MethodsBody CBCT scans operated in half‐fan mode were acquired with three different protocols: CBCTlowD, CBCTmidD, and CBCThighD, which resulted in weighted CT dose index (CTDIw) of 0.36, 1.43, and 2.78 cGy, respectively. An electron density phantom that is 18 cm in diameter was covered by four layers of 2.5‐cm‐thick bolus to simulate patients of different body sizes. Multivariate analysis was used to examine the impact of body size, radiation exposure, and tissue type on the target detectability of CBCT imaging, quantified as contrast‐to‐noise ratio (CNR).ResultsCBCTmidD allows sufficient target detection of adipose, breast, muscle, liver in a background of water for normal‐weight adults with cross‐sectional diameter less than 28 cm, while CBCThighD is suitable for adult patients with larger body sizes or body mass index over 25 kg/m2. Once the cross‐sectional diameter of adult patients is larger than 35 cm, the CTDIw of CBCT scans should be higher than 2.78 cGy to achieve required CNR. As for pediatric and adolescent patients with cross‐sectional diameter less than 25 cm, CBCTlowD is able to produce images with sufficient target detection.ConclusionThe target detectability of soft tissues in default CBCT scans may not be sufficient for overweight or obese adults. Contrary, pediatric and adolescent patients would receive unnecessarily high radiation exposure from default CBCT scans. Therefore, the selection of acquisition parameters for CBCT scans optimized according to patient body size was proposed to ensure sufficient image quality for daily patient positioning in radiation therapy while achieving the ALARA principle.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimizing the target detectability of cone beam CT performed in image‐guided radiation therapy for patients of different body sizes

Yang

Ruan

et al. 2018

J Applied Clin Med Phys

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“…This bone projection provides an estimate of the amount of nonlinear beam hardening distortion, which is then corrected for [21,37,38]. In our case, we use an approach similar to the one described in [37], according to the algorithm summarized in Fig. 11.…”

Section: Beam Hardening Correctionmentioning

confidence: 99%

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Abella

Vaquero

Sisniega

et al. 2012

Computer Methods and Programs in Biomedicine

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Cone-beam t r a c tMost small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp et al. (FDK). Besides the implementation of the reconstruction algorithm itself, in order to design a real system it is necessary to take into account numerous issues so as to obtain the best quality images from the acquired data. This work presents a comprehensive, novel software architecture for small-animal CT scanners based on cone-beam geometry with circular scanning trajectory. The proposed architecture covers all the steps from the system calibration to the volume reconstruction and conversion into Hounsfield units. It includes an efficient implementation of an FDK-based reconstruction algorithm that takes advantage of system symmetries and allows for parallel reconstruction using a multiprocessor computer. Strategies for calibration and artifact correction are discussed to justify the strategies adopted. New procedures for multi-bed misalignment, beam-hardening, and Housfield units calibration are proposed. Experiments with phantoms and real data showed the suitability of the proposed software architecture for an X-ray small animal CT based on cone-beam geometry. IntroductionMany small animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory [1][2][3][4]. This configuration presents advantages over other alternatives used in clinical and preclinical applications: reduction of acquisition time, large axial field of view (FOV) without geometrical distortions, and optimization of radiated dose [5]. proposed by Feldkamp et al. (FDK) [6] are still widely used for solving the 3D reconstruction task because of their straightforward implementation and computational efficiency [4]. Almost every aspect of the reconstruction process has been studied: there is literature on algorithm variations for different trajectories [7,8], optimizations using graphic processing units (GPUs) [9][10][11][12][13][14][15], strategies to reduce cone beam artifacts [16,17], study of consistency conditions [18], optimization of the back-projection step [19], etc. However, in a real practical system, the implementation of a reconstruction algorithm core such as FDK is just an initial step of the process, and there 1

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“…Note that, the user-defined parameter ε is interpreted as the variance of the difference between the predicted and the raw projections. After effective data correction for scatter and beam-hardening effects, [11][12][13][14][15][16][17][18] most of the projection errors in CT scans are from Poisson statistics of the incident photons, except for very lowdose imaging cases. [19][20][21] ε can therefore be readily estimated from the measured projections.…”

Section: Introductionmentioning

confidence: 99%

Accelerated barrier optimization compressed sensing (ABOCS) reconstruction for cone-beam CT: Phantom studies

2012

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Purpose: Recent advances in compressed sensing (CS) enable accurate CT image reconstruction from highly undersampled and noisy projection measurements, due to the sparsifiable feature of most CT images using total variation (TV). These novel reconstruction methods have demonstrated advantages in clinical applications where radiation dose reduction is critical, such as onboard cone-beam CT (CBCT) imaging in radiation therapy. The image reconstruction using CS is formulated as either a constrained problem to minimize the TV objective within a small and fixed data fidelity error, or an unconstrained problem to minimize the data fidelity error with TV regularization. However, the conventional solutions to the above two formulations are either computationally inefficient or involved with inconsistent regularization parameter tuning, which significantly limit the clinical use of CS-based iterative reconstruction. In this paper, we propose an optimization algorithm for CS reconstruction which overcomes the above two drawbacks. Methods:The data fidelity tolerance of CS reconstruction can be well estimated based on the measured data, as most of the projection errors are from Poisson noise after effective data correction for scatter and beam-hardening effects. We therefore adopt the TV optimization framework with a data fidelity constraint. To accelerate the convergence, we first convert such a constrained optimization using a logarithmic barrier method into a form similar to that of the conventional TV regularization based reconstruction but with an automatically adjusted penalty weight. The problem is then solved efficiently by gradient projection with an adaptive Barzilai-Borwein step-size selection scheme. The proposed algorithm is referred to as accelerated barrier optimization for CS (ABOCS), and evaluated using both digital and physical phantom studies. Results: ABOCS directly estimates the data fidelity tolerance from the raw projection data. Therefore, as demonstrated in both digital Shepp-Logan and physical head phantom studies, consistent reconstruction performances are achieved using the same algorithm parameters on scans with different noise levels and/or on different objects. On the contrary, the penalty weight in a TV regularization based method needs to be fine-tuned in a large range (up to seven times) to maintain the reconstructed image quality. The improvement of ABOCS on computational efficiency is demonstrated in the comparisons with adaptive-steepest-descent-projection-onto-convex-sets (ASD-POCS), an existing CS reconstruction algorithm also using constrained optimization. ASD-POCS alternatively minimizes the TV objective using adaptive steepest descent (ASD) and the data fidelity error using projection onto convex sets (POCS). For similar image qualities of the Shepp-Logan phantom, ABOCS requires less computation time than ASD-POCS in MATLAB by more than one order of magnitude. Conclusions: We propose ABOCS for CBCT reconstruction. As compared to other published CSbased algorithms, our method has at...

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An iterative approach to the beam hardening correction in cone beam CT

Cited by 243 publications

References 7 publications

Optimizing the target detectability of cone beam CT performed in image‐guided radiation therapy for patients of different body sizes

Optimizing the target detectability of cone beam CT performed in image‐guided radiation therapy for patients of different body sizes

Software architecture for multi-bed FDK-based reconstruction in X-ray CT scanners

Accelerated barrier optimization compressed sensing (ABOCS) reconstruction for cone-beam CT: Phantom studies

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