Parallel Computation of Fan Beam Back-Projection Reconstruction Algorithm in Computed Tomography

1991 ◽  
pp. 113-127
Author(s):  
Wen-Tai Lin ◽  
Chung-Yih Ho ◽  
Chi-Yuan Chin
Keyword(s):  
Computed Tomography ◽  
Parallel Computation ◽  
Reconstruction Algorithm ◽  
Back Projection ◽  
Projection Reconstruction

A reconstruction algorithm for coherent scatter computed tomography based on filtered back-projection

2003 ◽  
Vol 30 (9) ◽  
pp. 2465-2474 ◽  
Author(s):  
U. van Stevendaal ◽  
J.-P. Schlomka ◽  
A. Harding ◽  
M. Grass
Keyword(s):  
Computed Tomography ◽  
Reconstruction Algorithm ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Coherent Scatter

Simulation Analysis of the Sensitivity in Magnetic Induction Tomography Based on Perturbation Theory

2013 ◽  
Vol 749 ◽  
pp. 371-376
Author(s):  
Yang Xuan ◽  
Xu Wang ◽  
Cheng An Liu ◽  
Dan Yang
Keyword(s):  
Perturbation Theory ◽  
Salt Solution ◽  
Magnetic Induction ◽  
Imaging Modality ◽  
Reconstruction Algorithm ◽  
Target Object ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Projection Reconstruction ◽  
Magnetic Induction Tomography

Magnetic induction tomography (MIT) is a noninvasive and contactless imaging modality which aims at the reconstruction of the electrical conductivity in objects from alternating magnetic fields. Filtered back projection reconstruction algorithm is widely used in biomedical imaging field, and tried to use in MIT. Finite element analysis model has been established based on Scharfetter coil-coil model and perturbation theory, then simulated coaxial coil system by ANSYS software, the perturbation aroused by a target object moving on vertical coil axis. The sensitivity of a target object moves in vacuum and a salt solution were calculated respectively, the characteristics of the perturbation sensitivity in a salt solution were analyzed. The conditions of filtered back projection reconstruction algorithm in MIT were discussed.

A novel denoising method based on Radon transform and filtered back-projection reconstruction algorithm

2012 ◽  
Vol 50 (4) ◽  
pp. 593-598 ◽  
Author(s):  
Hong-yan Qu ◽  
Feng Xu ◽  
Xiao-fang Hu ◽  
Luo-bin Wang ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Radon Transform ◽  
Reconstruction Algorithm ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Projection Reconstruction ◽  
Denoising Method

scholarly journals Evaluation and Clinical Application of a Commercially Available Iterative Reconstruction Algorithm for CBCT-Based IGRT

2019 ◽  
Vol 18 ◽  
pp. 153303381882305 ◽  
Author(s):  
Weihua Mao ◽  
Chang Liu ◽  
Stephen J. Gardner ◽  
Farzan Siddiqui ◽  
Karen C. Snyder ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Quality Assurance ◽  
Image Quality ◽  
Cone Beam Computed Tomography ◽  
Reconstruction Algorithm ◽  
Cone Beam ◽  
Back Projection ◽  
Filtered Back Projection ◽  
Tomography Reconstruction ◽  
Computed Tomography Reconstruction

Purpose: We have quantitatively evaluated the image quality of a new commercially available iterative cone-beam computed tomography reconstruction algorithm over standard cone-beam computed tomography image reconstruction results. Methods: This iterative cone-beam computed tomography reconstruction pipeline uses a finite element solver (AcurosCTS)-based scatter correction and a statistical (iterative) reconstruction in addition to a standard kernel-based correction followed by filtered back-projection-based Feldkamp-Davis-Kress cone-beam computed tomography reconstruction. Standard full-fan half-rotation Head, half-fan full-rotation Head, and standard Pelvis cone-beam computed tomography protocols have been investigated to scan a quality assurance phantom via the following image quality metrics: uniformity, HU constancy, spatial resolution, low contrast detection, noise level, and contrast-to-noise ratio. An anthropomorphic head phantom was scanned for verification of noise reduction. Clinical patient image data sets for 5 head/neck patients and 5 prostate patients were qualitatively evaluated. Results: Quality assurance phantom study results showed that relative to filtered back-projection-based cone-beam computed tomography, noise was reduced from 28.8 ± 0.3 HU to a range between 18.3 ± 0.2 and 5.9 ± 0.2 HU for Full-Fan Head scans, from 14.4 ± 0.2 HU to a range between 12.8 ± 0.3 and 5.2 ± 0.3 HU for Half-Fan Head scans, and from 6.2 ± 0.1 HU to a range between 3.8 ± 0.1 and 2.0 ± 0.2 HU for Pelvis scans, with the iterative cone-beam computed tomography algorithm. Spatial resolution was marginally improved while results for uniformity and HU constancy were similar. For the head phantom study, noise was reduced from 43.6 HU to a range between 24.8 and 13.0 HU for a Full-Fan Head and from 35.1 HU to a range between 22.9 and 14.0 HU for a Half-Fan Head scan. The patient data study showed that artifacts due to photon starvation and streak artifacts were all reduced, and image noise in specified target regions were reduced to 62% ± 15% for 10 patients. Conclusion: Noise and contrast-to-noise ratio image quality characteristics were significantly improved using the iterative cone-beam computed tomography reconstruction algorithm relative to the filtered back-projection-based cone-beam computed tomography method. These improvements will enhance the accuracy of cone-beam computed tomography-based image-guided applications.

Sign in / Sign up

Export Citation Format

Share Document