DeepInterior: new pathway to address the interior tomographic reconstruction problem in CT via direct backprojecting divergent beam projection data

2021 ◽  
Author(s):  
Chengzhu Zhang ◽  
Yinsheng Li ◽  
Ke Li ◽  
Guang-Hong Chen
Keyword(s):  
Tomographic Reconstruction ◽  
Projection Data ◽  
Reconstruction Problem ◽  
Divergent Beam ◽  
Beam Projection

scholarly journals Shearlet-based regularized reconstruction in region-of-interest computed tomography

2018 ◽  
Vol 13 (4) ◽  
pp. 34
Author(s):  
T.A. Bubba ◽  
D. Labate ◽  
G. Zanghirati ◽  
S. Bonettini
Keyword(s):  
Optimization Problem ◽  
Ad Hoc ◽  
Iterative Scheme ◽  
Tomographic Reconstruction ◽  
Region Of Interest ◽  
Projection Data ◽  
Reconstruction Problem ◽  
Convex Optimization Problem ◽  
Scanning Time ◽  
Novel Approach

Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruction problem has unique solution. To address this problem, bothad hocanalytic formulas and iterative numerical schemes have been proposed in the literature. In this paper, we introduce a novel approach for ROI tomographic reconstruction, formulated as a convex optimization problem with a regularized term based on shearlets. Our numerical implementation consists of an iterative scheme based on the scaled gradient projection method and it is tested in the context of fan-beam CT. Our results show that our approach is essentially insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.

scholarly journals Dense U-Net for Limited Angle Tomography of Sound Pressure Fields

2021 ◽  
Vol 11 (10) ◽  
pp. 4570
Author(s):  
Oliver Rothkamm ◽  
Johannes Gürtler ◽  
Jürgen Czarske ◽  
Robert Kuschmierz
Keyword(s):  
Measurement Errors ◽  
Sound Pressure ◽  
Tomographic Reconstruction ◽  
Synthetic Data ◽  
Projection Data ◽  
Quantitative Measurements ◽  
Pressure Fields ◽  
The Neural Network ◽  
Bias Flow ◽  
3D Information

Tomographic reconstruction allows for the recovery of 3D information from 2D projection data. This commonly requires a full angular scan of the specimen. Angular restrictions that exist, especially in technical processes, result in reconstruction artifacts and unknown systematic measurement errors. We investigate the use of neural networks for extrapolating the missing projection data from holographic sound pressure measurements. A bias flow liner was studied for active sound dampening in aviation. We employed a dense U-Net trained on synthetic data and compared reconstructions of simulated and measured data with and without extrapolation. In both cases, the neural network based approach decreases the mean and maximum measurement deviations by a factor of two. These findings can enable quantitative measurements in other applications suffering from limited angular access as well.

Recent Development of Cone-Beam X-Ray Microtomography at Sunyab

1997 ◽  
Vol 3 (S2) ◽  
pp. 1125-1126
Author(s):  
S.J. Pan ◽  
A. Shih ◽  
W.S. Liou ◽  
M.S. Park ◽  
G. Wang ◽  
...  
Keyword(s):  
Dynamic Range ◽  
Imaging System ◽  
Tomographic Reconstruction ◽  
Ccd Camera ◽  
Test Sample ◽  
Glass Beads ◽  
Cone Beam ◽  
Projection Data ◽  
X Ray ◽  
Cooled Ccd

An experimental X-ray cone-beam microtomographic imaging system utilizing a generalized Feldkamp reconstruction algorithm has been developed in our laboratory. This microtomographic imaging system consists of a conventional dental X-ray source (Aztech 65, Boulder, CO), a sample position and rotation stage, an X-ray scintillation phosphor screen, and a high resolution slow scan cooled CCD camera (Kodak KAF 1400). A generalized Feldkamp cone-beam algorithm was used to perform tomographic reconstruction from cone-beam projection data. This algorithm was developed for various hardware configuration to perform reconstruction of spherical, rod-shaped and plate-like specimen.A test sample consists of 8 glass beads (approx. 800μm in diameter) dispersed in an epoxy-filled #0 gelatin capsule. One hundred X-ray projection images were captured equal angularly (at 3.6 degree spacing) by the cooled CCD camera at a of 1317×967 (17×17mm2) pixels with 12-bit dynamic range. Figure 1 shows a 3D isosurface rendering of the test sample. The eight glass beads and trapped air bubbles (arrows) in the epoxy resin (e) are clearly visible.

scholarly journals Data correlation based noise level estimation for cone beam projection data

2017 ◽  
Vol 25 (6) ◽  
pp. 907-926 ◽  
Author(s):  
Ti Bai ◽  
Hao Yan ◽  
Luo Ouyang ◽  
David Staub ◽  
Jing Wang ◽  
...  
Keyword(s):  
Noise Level ◽  
Cone Beam ◽  
Projection Data ◽  
Data Correlation ◽  
Noise Level Estimation ◽  
Beam Projection

scholarly journals Removing multiple outliers and single-crystal artefacts from X-ray diffraction computed tomography data

2015 ◽  
Vol 48 (6) ◽  
pp. 1943-1955 ◽  
Author(s):  
Antonios Vamvakeros ◽  
Simon D. M. Jacques ◽  
Marco Di Michiel ◽  
Vesna Middelkoop ◽  
Christopher K. Egan ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Single Crystal ◽  
Tomographic Reconstruction ◽  
Projection Data ◽  
Data Sets ◽  
Two Dimensional ◽  
X Ray Diffraction ◽  
X Ray ◽  
Diffraction Patterns ◽  
Tomography Data

This paper reports a simple but effective filtering approach to deal with single-crystal artefacts in X-ray diffraction computed tomography (XRD-CT). In XRD-CT, large crystallites can produce spots on top of the powder diffraction rings, which, after azimuthal integration and tomographic reconstruction, lead to line/streak artefacts in the tomograms. In the simple approach presented here, the polar transform is taken of collected two-dimensional diffraction patterns followed by directional median/mean filtering prior to integration. Reconstruction of one-dimensional diffraction projection data sets treated in such a way leads to a very significant improvement in reconstructed image quality for systems that exhibit powder spottiness arising from large crystallites. This approach is not computationally heavy which is an important consideration with big data sets such as is the case with XRD-CT. The method should have application to two-dimensional X-ray diffraction data in general where such spottiness arises.

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