Volume-of-interest cone-beam CT using a 2.35 MV beam generated with a carbon target

2012 ◽  
Vol 39 (7Part1) ◽  
pp. 4209-4218 ◽  
Author(s):  
James L. Robar ◽  
David Parsons ◽  
Avery Berman ◽  
Alex MacDonald
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Carbon Target ◽  
Volume Of Interest

Automated volume of interest delineation and rendering of cone beam CT images in interventional cardiology

2012 ◽  
Author(s):  
Cristian Lorenz ◽  
Dirk Schäfer ◽  
Peter Eshuis ◽  
John Carroll ◽  
Michael Grass
Keyword(s):  
Cone Beam Ct ◽  
Interventional Cardiology ◽  
Ct Images ◽  
Cone Beam ◽  
Volume Of Interest

Volume-of-interest reconstruction from severely truncated data in dental cone-beam CT

2015 ◽  
Author(s):  
Zheng Zhang ◽  
Budi Kusnoto ◽  
Xiao Han ◽  
E. Y. Sidky ◽  
Xiaochuan Pan
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Truncated Data ◽  
Volume Of Interest

SU-GG-I-30: Magnitude of Scattered Radiation and Dose Efficiency in Volume-Of-Interest (VOI) Cone Beam CT

2008 ◽  
Vol 35 (6Part3) ◽  
pp. 2649-2649
Author(s):  
C Lai ◽  
L Chen ◽  
T Han ◽  
X Liu ◽  
Y Shen ◽  
...  
Keyword(s):  
Scattered Radiation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Volume Of Interest ◽  
Dose Efficiency

Dose saving and scatter reduction in volume-of-interest (VOI) cone beam CT: work in progress

2008 ◽  
Author(s):  
Chao-Jen Lai ◽  
Chris C. Shaw ◽  
Lingyun Chen ◽  
Xinming Liu ◽  
Tao Han ◽  
...  
Keyword(s):  
Cone Beam Ct ◽  
Cone Beam ◽  
Work In Progress ◽  
Volume Of Interest ◽  
Dose Saving ◽  
Scatter Reduction

Dosimetric properties and commissioning of cone-beam CT image beam line with a carbon target

2013 ◽  
Vol 189 (7) ◽  
pp. 566-572 ◽  
Author(s):  
Y. Dzierma ◽  
F.G. Nuesken ◽  
N.P. Licht ◽  
C. Ruebe
Keyword(s):  
Cone Beam Ct ◽  
Beam Line ◽  
Cone Beam ◽  
Ct Image ◽  
Carbon Target

scholarly journals Exact Interior Reconstruction with Cone-Beam CT

2007 ◽  
Vol 2007 ◽  
pp. 1-5 ◽  
Author(s):  
Yangbo Ye ◽  
Hengyong Yu ◽  
Ge Wang
Keyword(s):  
Singular Value Decomposition ◽  
Convex Set ◽  
Cone Beam Ct ◽  
Singular Value ◽  
Cone Beam ◽  
Filtered Backprojection ◽  
Volume Of Interest ◽  
Interior Problem ◽  
Value Decomposition ◽  
Svd Method

Using the backprojection filtration (BPF) and filtered backprojection (FBP) approaches, respectively, we prove that with cone-beam CT the interior problem can be exactly solved by analytic continuation. The prior knowledge we assume is that a volume of interest (VOI) in an object to be reconstructed is known in a subregion of the VOI. Our derivations are based on the so-called generalized PI-segment (chord). The available projection onto convex set (POCS) algorithm and singular value decomposition (SVD) method can be applied to perform the exact interior reconstruction. These results have many implications in the CT field and can be extended to other tomographic modalities, such as SPECT/PET, MRI.

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.

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