Region-Of-Interest tomography with noisy projection data

2015 ◽  
pp. 397-402
Keyword(s):  
Region Of Interest ◽  
Projection Data ◽  
Noisy 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 GPU-based fast cone beam CT reconstruction from undersampled and noisy projection data via total variation

2010 ◽  
Vol 37 (4) ◽  
pp. 1757-1760 ◽  
Author(s):  
Xun Jia ◽  
Yifei Lou ◽  
Ruijiang Li ◽  
William Y. Song ◽  
Steve B. Jiang
Keyword(s):  
Total Variation ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Reconstruction ◽  
Noisy Projection

scholarly journals A General Local Reconstruction Approach Based on a Truncated Hilbert Transform

2007 ◽  
Vol 2007 ◽  
pp. 1-8 ◽  
Author(s):  
Yangbo Ye ◽  
Hengyong Yu ◽  
Yuchuan Wei ◽  
Ge Wang
Keyword(s):  
Image Reconstruction ◽  
Hilbert Transform ◽  
Region Of Interest ◽  
Cone Beam ◽  
Projection Data ◽  
Partial Knowledge ◽  
Practical Applications ◽  
Beam Geometry ◽  
Volume Of Interest ◽  
Simulation Results

Exact image reconstruction from limited projection data has been a central topic in the computed tomography (CT) field. In this paper, we present a general region-of-interest/volume-of-interest (ROI/VOI) reconstruction approach using a truly truncated Hilbert transform on a line-segment inside a compactly supported object aided by partial knowledge on one or both neighboring intervals of that segment. Our approach and associated new data sufficient condition allows the most flexible ROI/VOI image reconstruction from the minimum account of data in both the fan-beam and cone-beam geometry. We also report primary numerical simulation results to demonstrate the correctness and merits of our finding. Our work has major theoretical potentials and innovative practical applications.

A Research of Electromagnetic Tomography Based on Different Parameters

2013 ◽  
Vol 834-836 ◽  
pp. 926-929
Author(s):  
Jing Sun ◽  
Fun Qun Shao
Keyword(s):  
Electromagnetic Induction ◽  
Imaging Technique ◽  
Region Of Interest ◽  
Industrial Applications ◽  
Projection Data ◽  
Conducting Material ◽  
Electromagnetic Tomography ◽  
Electrically Conducting ◽  
Fundamental Investigation ◽  
Electromagnetic Measurements

Electromagnetic tomography (EMT) is a recent imaging technique. Its purpose is to determine the distribution of electrical conductivity and magnetic permeability in a given region of interest. EMT is based on the principle of electromagnetic induction. This distribution is computed from the measurements of the induced voltages at several coils placed around the region which are produced by the application of different excitation patterns . Usually images are obtained through inversion of the projection data. The paper represents a fundamental investigation of the potential of using electromagnetic measurements for industrial tomography application. This paper concentrates on image reconstruction of either electrically conducting material or magnetically permeable materials. Finally a number of potential industrial applications for the EMT technique are discussed.

Sparse and limited-angle CT image reconstruction using dictionary learning constrained by L1 norm

2019 ◽  
Vol 61 (10) ◽  
pp. 584-590
Author(s):  
Jun-Nian Gou ◽  
Pan-Pan Zhai ◽  
Hai-Ying Dong
Keyword(s):  
Dictionary Learning ◽  
Absolute Error ◽  
Image Feature ◽  
Projection Data ◽  
Reconstruction Technique ◽  
L1 Norm ◽  
Noisy Projection ◽  
The Impact ◽  
Better Than ◽  
Limited Angle

Reconstructed images from computed tomography (CT) using the algebraic reconstruction technique (ART) and simultaneous ART (SART) algorithms often suffer from obvious artefacts when only sparse and limited-angle projection data are available. Using the ability of dictionary learning (DL) in image feature extraction and sparse signal representation, a new iterative reconstruction algorithm, ART-DL-L1, is proposed to overcome the aforementioned limitations. This new algorithm is based on DL and an L1 norm constraint, combined with ART. An alternate iterative solving strategy based on an approach of 'ART first, then adaptive dictionary learning' is suggested and is explicitly described in a flowchart depicting the ART-DL-L1 algorithm. For both a noisy projection of 360° sparse data and limitedangle data of 120°, simulation reconstruction results from the classic Shepp-Logan image obtained using ART-DL-L1 appear to be better than those obtained using SART and total variation (TV) algorithms and also better than the cutting-edge ART-DL-L2 algorithm. Five evaluation metrics corresponding to the root-mean-square error (RMSE), the mean absolute error (MAE), the peak signal-to-noise ratio (PSNR), the residuals and the structural similarity (SSIM) index are adopted to estimate the reconstruction effect. The results suggest that the five metrics obtained using ART-DL-L1 outperform those obtained using the other three algorithms. The impact of using patches of various sizes played by the DL part in ART-DL-L1 is considered in the simulations and the patch size achieving the best reconstructed image quality is identified in this case as 25 (5 × 5). Overall, the proposed ART-DL-L1 algorithm may reduce artefacts and suppress noise from incomplete noisy projection CT imaging to some degree.

scholarly journals Exact Interior Reconstruction from Truncated Limited-Angle Projection Data

2008 ◽  
Vol 2008 ◽  
pp. 1-6 ◽  
Author(s):  
Yangbo Ye ◽  
Hengyong Yu ◽  
Ge Wang
Keyword(s):  
Analytic Continuation ◽  
Prior Knowledge ◽  
Classical Theory ◽  
Region Of Interest ◽  
Projection Data ◽  
Filtered Backprojection ◽  
Interior Region ◽  
Volume Of Interest ◽  
Interior Volume ◽  
Limited Angle

Using filtered backprojection (FBP) and an analytic continuation approach, we prove that exact interior reconstruction is possible and unique from truncated limited-angle projection data, if we assume a prior knowledge on a subregion or subvolume within an object to be reconstructed. Our results show that (i) the interior region-of-interest (ROI) problem and interior volume-of-interest (VOI) problem can be exactly reconstructed from a limited-angle scan of the ROI/VOI and a 180 degree PI-scan of the subregion or subvolume and (ii) the whole object function can be exactly reconstructed from nontruncated projections from a limited-angle scan. These results improve the classical theory of Hamaker et al. (1980).

Sign in / Sign up

Export Citation Format

Share Document