A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy

Xu, Yuan; Bai, Ti; Yan, Hao; Ouyang, Luo; Pompoš, A.; Wang, Jing; Zhou, Linghong; Jiang, Steve B; Jia, Xun

doi:10.1088/0031-9155/60/9/3567

Cited by 98 publications

(108 citation statements)

References 63 publications

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“…While CBCT is an invaluable tool for image guidance, the physical imaging characteristics, namely a large scatter‐to‐primary ratio, lead to image artifacts such as streaking, shading, cupping, and reduced image contrast. All of these factors prevent quantitative CBCT, hindering full utilization of the information provided by frequent imaging . The incorporation of CBCT into the clinic has led to increased interest in adaptive radiation therapy (ART), where dose would be calculated daily based on the patient's true setup on the treatment table.…”

Section: Introductionmentioning

confidence: 99%

Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography

et al. 2019

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Purpose The incorporation of cone‐beam computed tomography (CBCT) has allowed for enhanced image‐guided radiation therapy. While CBCT allows for daily 3D imaging, images suffer from severe artifacts, limiting the clinical potential of CBCT. In this work, a deep learning‐based method for generating high quality corrected CBCT (CCBCT) images is proposed. Methods The proposed method integrates a residual block concept into a cycle‐consistent adversarial network (cycle‐GAN) framework, called res‐cycle GAN, to learn a mapping between CBCT images and paired planning CT images. Compared with a GAN, a cycle‐GAN includes an inverse transformation from CBCT to CT images, which constrains the model by forcing calculation of both a CCBCT and a synthetic CBCT. A fully convolution neural network with residual blocks is used in the generator to enable end‐to‐end CBCT‐to‐CT transformations. The proposed algorithm was evaluated using 24 sets of patient data in the brain and 20 sets of patient data in the pelvis. The mean absolute error (MAE), peak signal‐to‐noise ratio (PSNR), normalized cross‐correlation (NCC) indices, and spatial non‐uniformity (SNU) were used to quantify the correction accuracy of the proposed algorithm. The proposed method is compared to both a conventional scatter correction and another machine learning‐based CBCT correction method. Results Overall, the MAE, PSNR, NCC, and SNU were 13.0 HU, 37.5 dB, 0.99, and 0.05 in the brain, 16.1 HU, 30.7 dB, 0.98, and 0.09 in the pelvis for the proposed method, improvements of 45%, 16%, 1%, and 93% in the brain, and 71%, 38%, 2%, and 65% in the pelvis, over the CBCT image. The proposed method showed superior image quality as compared to the scatter correction method, reducing noise and artifact severity. The proposed method produced images with less noise and artifacts than the comparison machine learning‐based method. Conclusions The authors have developed a novel deep learning‐based method to generate high‐quality corrected CBCT images. The proposed method increases onboard CBCT image quality, making it comparable to that of the planning CT. With further evaluation and clinical implementation, this method could lead to quantitative adaptive radiation therapy.

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Section: Introductionmentioning

confidence: 99%

Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography

et al. 2019

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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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“…These factors may affect the accuracy of the proposed estimation algorithm, especially when CT images are used as the prior information for CBCT estimation (CBCT projections present more scatters than CT). Previous studies investigated multiple scatter correction/reduction techniques and achieved encouraging results [28], [51], [52]. The imager lags can also be corrected by using a measurement-based approach [53].…”

Section: Discussionmentioning

confidence: 99%

A Biomechanical Modeling Guided CBCT Estimation Technique

You

Tehrani

Wang

2017

IEEE Trans. Med. Imaging

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Two-dimensional-to-three-dimensional (2D-3D) deformation has emerged as a new technique to estimate cone-beam computed tomography (CBCT) images. The technique is based on deforming a prior high-quality 3D CT/CBCT image to form a new CBCT image, guided by limited-view 2D projections. The accuracy of this intensity-based technique, however, is often limited in low-contrast image regions with subtle intensity differences. The solved deformation vector fields (DVFs) can also be biomechanically unrealistic. To address these problems, we have developed a biomechanical modeling guided CBCT estimation technique (Bio-CBCT-est) by combining 2D-3D deformation with finite element analysis (FEA)-based biomechanical modeling of anatomical structures. Specifically, Bio-CBCT-est first extracts the 2D-3D deformation-generated displacement vectors at the high-contrast anatomical structure boundaries. The extracted surface deformation fields are subsequently used as the boundary conditions to drive structure-based FEA to correct and fine-tune the overall deformation fields, especially those at low-contrast regions within the structure. The resulting FEA-corrected deformation fields are then fed back into 2D-3D deformation to form an iterative loop, combining the benefits of intensity-based deformation and biomechanical modeling for CBCT estimation. Using eleven lung cancer patient cases, the accuracy of the Bio-CBCT-est technique has been compared to that of the 2D-3D deformation technique and the traditional CBCT reconstruction techniques. The accuracy was evaluated in the image domain, and also in the DVF domain through clinician-tracked lung landmarks.

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A practical cone-beam CT scatter correction method with optimized Monte Carlo simulations for image-guided radiation therapy

Cited by 98 publications

References 63 publications

Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography

Paired cycle‐GAN‐based image correction for quantitative cone‐beam computed tomography

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

A Biomechanical Modeling Guided CBCT Estimation Technique

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