TH-E-330A-05: Reducing Metal Artifacts in Cone-Beam CT by Tracking and Eliminating Metal Shadows in Raw Projection Data

2006 ◽  
Vol 33 (6Part23) ◽  
pp. 2288-2288 ◽  
Author(s):  
Y Zhang ◽  
L Zhang ◽  
RX Zhu ◽  
M Chambers ◽  
L Dong
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Data ◽  
Metal Artifacts

Reducing metal artifacts in cone-beam CT images by preprocessing projection data

2007 ◽  
Vol 67 (3) ◽  
pp. 924-932 ◽  
Author(s):  
Yongbin Zhang ◽  
Lifei Zhang ◽  
X. Ronald Zhu ◽  
Andrew K. Lee ◽  
Mark Chambers ◽  
...  
Keyword(s):  
Cone Beam Ct ◽  
Ct Images ◽  
Cone Beam ◽  
Projection Data ◽  
Metal Artifacts

A new weighting scheme for arc based circle cone-beam CT reconstruction

2021 ◽  
pp. 1-19
Author(s):  
Wei Wang ◽  
Xiang-Gen Xia ◽  
Chuanjiang He ◽  
Zemin Ren ◽  
Jian Lu
Keyword(s):  
Characteristic Function ◽  
Weighting Function ◽  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Characteristic Functions ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Scanning Angle ◽  
Beam Geometry

In this paper, we present an arc based fan-beam computed tomography (CT) reconstruction algorithm by applying Katsevich’s helical CT image reconstruction formula to 2D fan-beam CT scanning data. Specifically, we propose a new weighting function to deal with the redundant data. Our weighting function ϖ ( x _ , λ ) is an average of two characteristic functions, where each characteristic function indicates whether the projection data of the scanning angle contributes to the intensity of the pixel x _ . In fact, for every pixel x _ , our method uses the projection data of two scanning angle intervals to reconstruct its intensity, where one interval contains the starting angle and another contains the end angle. Each interval corresponds to a characteristic function. By extending the fan-beam algorithm to the circle cone-beam geometry, we also obtain a new circle cone-beam CT reconstruction algorithm. To verify the effectiveness of our method, the simulated experiments are performed for 2D fan-beam geometry with straight line detectors and 3D circle cone-beam geometry with flat-plan detectors, where the simulated sinograms are generated by the open-source software “ASTRA toolbox.” We compare our method with the other existing algorithms. Our experimental results show that our new method yields the lowest root-mean-square-error (RMSE) and the highest structural-similarity (SSIM) for both reconstructed 2D and 3D fan-beam CT images.

scholarly journals Positioning errors of metal localization devices with motion artifacts on kV and MV cone beam CT

BJR|Open ◽  
2019 ◽  
Vol 1 (1) ◽  
pp. 20190013
Author(s):  
Teh Lin ◽  
Chang-Ming Charlie Ma
Keyword(s):  
Radiation Treatment ◽  
Cone Beam Ct ◽  
Motion Artifacts ◽  
Cone Beam ◽  
Breathing Cycle ◽  
Metal Artifacts ◽  
Motion Direction ◽  
Image Guided Radiation Therapy ◽  
Positioning Errors ◽  
Image Volume

Objective: To investigate motion artifacts on kV CBCT and MV CBCT images with metal localization devices for image-guided radiation therapy. Methods: The 8 μ pelvis CBCT template for the Siemens Artiste MVision and Pelvis template for the Varian IX on-board Exact Arms kV were used to acquire CBCT images in this study. Images from both CBCT modalities were compared in CNRs, metal landmark absolute positions, and image volume distortion on three different planes of view. The images were taken on a breathing-simulated thoracic phantom in which several typical metal localization devices were implanted, including clips and wires for breast patients, gold seeds for prostate patients, and BBs as skin markers. To magnify the artifacts, a 4 cm diameter metal ball was also implanted into the thoracic phantom to mimic the metal artifacts. Results: For MV CBCT, the CNR at a 4 sec breathing cycle with 1 cm breathing amplitude was 5.0, 3.4 and 4.6 for clips, gold seeds and BBs, respectively while it was 1.5, 2.0 and 1.6 for the kV CBCT. On the images, the kV CBCT showed symmetric streaking artifacts both in the transverse and longitudinal directions relative to the motion direction. The kV CBCT images predicted 89 % of the expected volume, while the MV CBCT images predicted 95 % of the expected volume. The simulated soft tissue observed in the MVCT could not be detected in the kV CBCT. Conclusion: The MV CBCT images showed better volume prediction, less streaking effects and better CNRs of a moving metal target, i.e. clips, BBs, gold seeds and metal balls than on the kV CBCT images. The MV CBCT was more advantageous compared to the kV CBCT with less motion artifacts for metal localization devices. Advances in knowledge: This study would benefit clinicians to prescribe MV CBCT as localization modality for radiation treatment with moving target when metal markers are implanted.

scholarly journals GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Xing Zhao ◽  
Jing-jing Hu ◽  
Peng Zhang
Keyword(s):  
Image Reconstruction ◽  
Cone Beam Ct ◽  
Large Data ◽  
Circular Cone ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Image ◽  
Reconstruction Process ◽  
Data Volume ◽  
Ct Image Reconstruction

Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation.

scholarly journals GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation

2010 ◽  
Vol 37 (4) ◽  
pp. 1757-1760 ◽  
Author(s):  
Xun Jia ◽  
Yifei Lou ◽  
Ruijiang Li ◽  
William Y. Song ◽  
Steve B. Jiang
Keyword(s):  
Total Variation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Noisy Projection

scholarly journals Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network

2019 ◽  
Vol 46 (11) ◽  
pp. 5027-5035 ◽  
Author(s):  
Jordi Minnema ◽  
Maureen Eijnatten ◽  
Allard A. Hendriksen ◽  
Niels Liberton ◽  
Daniël M. Pelt ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Cone Beam Ct ◽  
Ct Scans ◽  
Cone Beam ◽  
Metal Artifacts

scholarly journals A learning-based method for online adjustment of C-arm Cone-beam CT source trajectories for artifact avoidance

2020 ◽  
Vol 15 (11) ◽  
pp. 1787-1796
Author(s):  
Mareike Thies ◽  
Jan-Nico Zäch ◽  
Cong Gao ◽  
Russell Taylor ◽  
Nassir Navab ◽  
...  
Keyword(s):  
Neural Network ◽  
Cone Beam Ct ◽  
Simulated Data ◽  
Tomographic Imaging ◽  
Cone Beam ◽  
Screw Placement ◽  
Patient Specific ◽  
Metal Artifacts ◽  
High Quality ◽  
Reconstruction Quality

Abstract Purpose During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-arm cone-beam CT (CBCT) provides intraoperative 3D tomographic imaging which would allow for immediate verification and, if needed, revision. However, the reconstruction quality attainable with commercial CBCT devices is insufficient, predominantly due to severe metal artifacts in the presence of pedicle screws. These artifacts arise from a mismatch between the true physics of image formation and an idealized model thereof assumed during reconstruction. Prospectively acquiring views onto anatomy that are least affected by this mismatch can, therefore, improve reconstruction quality. Methods We propose to adjust the C-arm CBCT source trajectory during the scan to optimize reconstruction quality with respect to a certain task, i.e., verification of screw placement. Adjustments are performed on-the-fly using a convolutional neural network that regresses a quality index over all possible next views given the current X-ray image. Adjusting the CBCT trajectory to acquire the recommended views results in non-circular source orbits that avoid poor images, and thus, data inconsistencies. Results We demonstrate that convolutional neural networks trained on realistically simulated data are capable of predicting quality metrics that enable scene-specific adjustments of the CBCT source trajectory. Using both realistically simulated data as well as real CBCT acquisitions of a semianthropomorphic phantom, we show that tomographic reconstructions of the resulting scene-specific CBCT acquisitions exhibit improved image quality particularly in terms of metal artifacts. Conclusion The proposed method is a step toward online patient-specific C-arm CBCT source trajectories that enable high-quality tomographic imaging in the operating room. Since the optimization objective is implicitly encoded in a neural network trained on large amounts of well-annotated projection images, the proposed approach overcomes the need for 3D information at run-time.

The Improvement of Katsevich Reconstruction Algorithm Based on Cone-Beam CT

2012 ◽  
Vol 239-240 ◽  
pp. 1148-1151
Author(s):  
Li Fang Wang
Keyword(s):  
Image Quality ◽  
Polynomial Interpolation ◽  
Cone Beam Ct ◽  
Reconstruction Algorithm ◽  
Experimental Results ◽  
Cone Beam ◽  
Projection Data ◽  
Interpolation Function ◽  
Reconstruction Process ◽  
Derivatives Of

The Katsevich reconstruction algorithm based on cone-beam must compute the derivative of projection data in the reconstruction process, but projection data are discrete and haven’t derivative. So the derivatives of the polynomial interpolation function are as approximation of the derivative of projection data. To verify the effectiveness of this method, 3D Shepp-Logan model is reconstructed by the method and the average gradient is used to measure the clarity of image. The experimental results show that this method enables image clearer and improves image quality

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