scholarly journals Improving soft tissue imaging with volume-of-interest cone-beam CT

2021 ◽  
Author(s):  
Christopher Huynh Huynh
Keyword(s):  
Soft Tissue ◽  
Cone Beam Ct ◽  
Field Size ◽  
Tissue Imaging ◽  
Cone Beam ◽  
Projection Data ◽  
Full Field ◽  
Volume Of Interest ◽  
Photon Fluence ◽  
Intermediate Image

Current cone-beam CT systems acquire full field-of-view projections in which x-ray scatter degrades the contrast of soft-tissue in the reconstructed images. The objective of this work was to simulate volume-of-interest (VOI) imaging, which reduces scatter and dose to the patient through beam collimation, to investigate the improvements in soft-tissue visibility on the Gamma Knife Icon. The results indicated that as field size decreased, contrast and noise increased, leading to only modest improvements in the contrast-to-noise ratio when using the same initial photon fluence. A reconstruction framework called the interior virtual method was adapted to suppress truncation-induced artifacts and noise in the VOI image. In this framework the projection data were extrapolated using a cosine function, an intermediate image was reconstructed analytically, and virtual projections of the intermediate image were created for iterative reconstruction. The framework supports high quality VOI reconstruction and can allow clinicians to optimize dose for soft-tissue visualization.

scholarly journals Improving soft tissue imaging with volume-of-interest cone-beam CT

2021 ◽  
Author(s):  
Christopher Huynh Huynh
Keyword(s):  
Soft Tissue ◽  
Cone Beam Ct ◽  
Field Size ◽  
Tissue Imaging ◽  
Cone Beam ◽  
Projection Data ◽  
Full Field ◽  
Volume Of Interest ◽  
Photon Fluence ◽  
Intermediate Image

Current cone-beam CT systems acquire full field-of-view projections in which x-ray scatter degrades the contrast of soft-tissue in the reconstructed images. The objective of this work was to simulate volume-of-interest (VOI) imaging, which reduces scatter and dose to the patient through beam collimation, to investigate the improvements in soft-tissue visibility on the Gamma Knife Icon. The results indicated that as field size decreased, contrast and noise increased, leading to only modest improvements in the contrast-to-noise ratio when using the same initial photon fluence. A reconstruction framework called the interior virtual method was adapted to suppress truncation-induced artifacts and noise in the VOI image. In this framework the projection data were extrapolated using a cosine function, an intermediate image was reconstructed analytically, and virtual projections of the intermediate image were created for iterative reconstruction. The framework supports high quality VOI reconstruction and can allow clinicians to optimize dose for soft-tissue visualization.

Achieving high-resolution soft-tissue imaging with cone-beam CT: a two-pronged approach for modulation of x-ray fluence and detector gain

2005 ◽  
Author(s):  
S. A. Graham ◽  
J. H. Siewerdsen ◽  
D. J. Moseley ◽  
H. Keller ◽  
N. A. Shkumat ◽  
...  
Keyword(s):  
High Resolution ◽  
Soft Tissue ◽  
Cone Beam Ct ◽  
Tissue Imaging ◽  
Cone Beam ◽  
X Ray

scholarly journals High-performance soft-tissue imaging in extremity cone-beam CT

2014 ◽  
Author(s):  
W. Zbijewski ◽  
A. Sisniega ◽  
J. W. Stayman ◽  
A. Muhit ◽  
G. Thawait ◽  
...  
Keyword(s):  
Soft Tissue ◽  
High Performance ◽  
Cone Beam Ct ◽  
Tissue Imaging ◽  
Cone Beam

scholarly journals Soft-tissue imaging in low-dose, C-arm cone-beam CT using statistical image reconstruction

2013 ◽  
Author(s):  
Adam S. Wang ◽  
Sebastian Schafer ◽  
J. W. Stayman ◽  
Yoshi Otake ◽  
Marc S. Sussman ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Image Reconstruction ◽  
Low Dose ◽  
Cone Beam Ct ◽  
Tissue Imaging ◽  
Cone Beam ◽  
Statistical Image Reconstruction

scholarly journals Soft-tissue imaging with C-arm cone-beam CT using statistical reconstruction

2014 ◽  
Vol 59 (4) ◽  
pp. 1005-1026 ◽  
Author(s):  
Adam S Wang ◽  
J Webster Stayman ◽  
Yoshito Otake ◽  
Gerhard Kleinszig ◽  
Sebastian Vogt ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Cone Beam Ct ◽  
Tissue Imaging ◽  
Cone Beam ◽  
Statistical Reconstruction

Soft-tissue contrast resolution within the head of human cadaver by means of flat-detector-based cone-beam CT

2004 ◽  
Author(s):  
Jens Wiegert ◽  
Matthias Bertram ◽  
Dirk Schaefer ◽  
Norbert Conrads ◽  
Niels Noordhoek ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Human Cadaver ◽  
Flat Detector ◽  
Contrast Resolution ◽  
Soft Tissue Contrast ◽  
Tissue Contrast

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.

Soft tissue thickness in Brazilian adults of different skeletal classes and facial types: A cone beam CT – Study

2020 ◽  
Vol 47 ◽  
pp. 101743
Author(s):  
Amanda Farias Gomes ◽  
Debora Duarte Moreira ◽  
Mariana Fabbro Zanon ◽  
Francisco Carlos Groppo ◽  
Francisco Haiter-Neto ◽  
...  
Keyword(s):  
Soft Tissue ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Tissue Thickness ◽  
Soft Tissue Thickness ◽  
Facial Types

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.

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