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 ◽  
...  
2012 ◽  
Author(s):  
Cristian Lorenz ◽  
Dirk Schäfer ◽  
Peter Eshuis ◽  
John Carroll ◽  
Michael Grass

2015 ◽  
Author(s):  
Zheng Zhang ◽  
Budi Kusnoto ◽  
Xiao Han ◽  
E. Y. Sidky ◽  
Xiaochuan Pan

2012 ◽  
Vol 39 (7Part1) ◽  
pp. 4209-4218 ◽  
Author(s):  
James L. Robar ◽  
David Parsons ◽  
Avery Berman ◽  
Alex MacDonald

2008 ◽  
Author(s):  
Chao-Jen Lai ◽  
Chris C. Shaw ◽  
Lingyun Chen ◽  
Xinming Liu ◽  
Tao Han ◽  
...  

2007 ◽  
Vol 2007 ◽  
pp. 1-5 ◽  
Author(s):  
Yangbo Ye ◽  
Hengyong Yu ◽  
Ge Wang

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.


2021 ◽  
Author(s):  
Christopher Huynh Huynh

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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