Scatter correction for a clinical cone‐beam CT system using an optimized stationary beam blocker in a single scan

Liang, Xiaokun; Jiang, Yangkang; Zhao, Wei; Zhang, Zhicheng; Luo, Chen; Xiong, Jing; Yu, Shaode; Yang, Xiaoming; Sun, Jihong; Zhou, Quanfa; Niu, Tianye; Xie, Yaoqin

doi:10.1002/mp.13568

Cited by 17 publications

(11 citation statements)

References 54 publications

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“…Of course, the proposed method not only can apply in the breast setup, but also any other organ such as spine, brain, liver and so on. Third, image artifacts such as the scatter, ring, metal artifact will hamper the accuracy of the setup [33][34][35][36][37][38][39][40][41][42][43][44].…”

Section: Limitations and Future Workmentioning

confidence: 99%

Breast Cancer Patient Auto-Setup Using Residual Neural Network for CT-Guided Therapy

Liu

et al. 2020

IEEE Access

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Patient setup will influence the treatment of the breast cancer in radiation therapy. Improving the accuracy of the tumor target localization is vital for the cancer treatment. In this study, we focus on the breast patient setup and develop an accurate tumor localization method based on the deep learning in radiation therapy. The proposed method used a double residual neural network model to achieve the high precision and efficiency patient tumor localization. In the network training, the model attempt to localize the breast and then detect the landmarks inside the localized region. After the model training, we used an iterative filter scheme for calculating a transformation to the daily CT. Therefore, the gray value distribution can match well with the training image. The final landmark positions were obtained after the iteration. The translation errors in the daily CT were determined using the detected landmarks. We used the digital CT phantom images and the real patient CT images to evaluate the proposed method. Then result of the breast patient setup was shown to be clinically acceptable. The mean and standard deviation setup errors were 0.64 ± 1.40 mm, 0.15 ± 1.28 mm, -0.46 ± 1.17 mm in the anterior-posterior, left-right, and superior-inferior, respectively. In conclusion, we proposed an accurate patient setup method, which shown a very promising alternative for marker-free breast auto-setup.

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Section: Limitations and Future Workmentioning

confidence: 99%

Breast Cancer Patient Auto-Setup Using Residual Neural Network for CT-Guided Therapy

Liu

et al. 2020

IEEE Access

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“…Due to the gantry rotation wobble, its clinical application is limited. The measurement-based method requires inserted blocker (usually using lead) into the X-ray source and scanned object (1)(2)(3)6,(21)(22)(23)(24)(25). In this way, the detector forms the shadow region that only contains the scatter signal, but such a methodology is difficult to operate by changing the hardware setting of the existing system.…”

Section: Original Articlementioning

confidence: 99%

Shading correction for volumetric CT using deep convolutional neural network and adaptive filter

Liang

Zhang

et al. 2019

Quant. Imaging Med. Surg.

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Background: Shading artifact may lead to CT number inaccuracy, image contrast loss and spatial nonuniformity (SNU), which is considered as one of the fundamental limitations for volumetric CT (VCT) application. To correct the shading artifact, a novel approach is proposed using deep learning and an adaptive filter (AF).Methods: Firstly, we apply the deep convolutional neural network (DCNN) to train a human tissue segmentation model. The trained model is implemented to segment the tissue. According to the general knowledge that CT number of the same human tissue is approximately the same, a template image without shading artifact can be generated using segmentation and then each tissue is filled with the corresponding CT number of a specific tissue. By subtracting the template image from the uncorrected image, the residual image with image detail and shading artifact are generated. The shading artifact is mainly low-frequency signals while the image details are mainly high-frequency signals. Therefore, we proposed an adaptive filter to separate the shading artifact and image details accurately. Finally, the estimated shading artifacts are deleted from the raw image to generate the corrected image.Results: On the Catphan©504 study, the error of CT number in the corrected image's region of interest (ROI) is reduced from 109 to 11 HU, and the image contrast is increased by a factor of 1.46 on average. On the patient pelvis study, the error of CT number in selected ROI is reduced from 198 to 10 HU. The SNU calculated from the ROIs decreases from 24% to 9% after correction. Conclusions:The proposed shading correction method using DCNN and AF may find a useful application in future clinical practice.

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“…Even with moving beam blockers, partial data loss cannot be avoided, because beam‐stops obstruct a part of the projection during a CBCT acquisition. To overcome this issue, a stationary beam‐stop approach was developed, 6,7 which exploits data redundancy by increasing the scan angular range from 180 degrees plus fan angle to 360 degrees. This way, strategically placed beam‐stop shadows will project onto redundant data regions, which are not needed during reconstruction.…”

Section: Introductionmentioning

confidence: 99%

A unified scatter rejection and correction method for cone beam computed tomography

Altunbas

Park

et al. 2021

Medical Physics

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Purpose Scattered radiation is a major cause of image quality degradation in flat panel detector‐based cone beam CT (CBCT). While recently introduced 2D antiscatter grids reject the majority of scatter fluence, the small percentage of scatter fluence still transmitted to the detector remains a major challenge for implementation of quantitative imaging techniques such as dual energy imaging in CBCT. Additionally, this residual scatter is also a major source of grid‐induced artifacts, which impedes implementation of 2D grids in CBCT. We therefore present a new method to achieve both robust scatter rejection and residual scatter correction using a 2D antiscatter grid; in doing so, we expand the role of 2D grids from mere scatter rejection devices to scatter measurement devices. Method In our method, the radiopaque septa of the 2D grid emulate a micro array of beam‐stops placed on the detector which introduce spatially periodic septal shadows. By selecting sufficiently thin grid septa, the primary intensity can be reduced while preserving the uniformity of scatter intensity. This enables us to correlate the modulated pixel signal intensity in septal shadows with local scatter intensity. Our method then exploits this correlation to measure and remove residual scatter intensity from projections. No assumptions are made about the object being imaged. We refer to this as Grid‐based Scatter Sampling (GSS). In this work, we evaluate the principle of signal modulation with grid septa, the accuracy of scatter estimates, and the effect of the GSS method on image quality using simulations and measurements. We also implement the GSS method experimentally using a 2D grid prototype. Results Our results demonstrate that the GSS method increased CT number accuracy and reduced image artifacts associated with scatter. With 2D grid and residual scatter correction, HU nonuniformity was reduced from 65 HU to 30 HU in pelvis sized phantoms, and HU variations due to change in phantom size were reduced from 59 HU to 20 HU, when compared to use of only a 2D grid. With residual scatter correction via GSS method, grid‐induced ring artifacts were suppressed, leading to a 41% reduction in noise. The shape of the modulation transfer function (MTF) was preserved before and after suppression of ring artifacts. Conclusions Our grid‐based scatter sampling method enables utilization of a 2D grid as a scatter measurement and correction device. This method significantly improves quantitative accuracy in CBCT, further reducing the image quality gap between CBCT and multi‐detector CT. By correcting residual scatter with the proposed method, grid‐induced line artifacts in projections and associated ring artifacts in CBCT images were also suppressed with no compromise of spatial resolution.

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Scatter correction for a clinical cone‐beam CT system using an optimized stationary beam blocker in a single scan

Cited by 17 publications

References 54 publications

Breast Cancer Patient Auto-Setup Using Residual Neural Network for CT-Guided Therapy

Breast Cancer Patient Auto-Setup Using Residual Neural Network for CT-Guided Therapy

Shading correction for volumetric CT using deep convolutional neural network and adaptive filter

A unified scatter rejection and correction method for cone beam computed tomography

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