Few-View Projection Reconstruction With an Iterative Reconstruction-Reprojection Algorithm and TV Constraint

Duan, Xinhui; Zhang, Li; Xing, Yuxiang; Chen, Zhiqiang; Cheng, Jianping

doi:10.1109/tns.2008.2009990

Cited by 46 publications

(37 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As a transformation reconstruction method, IRR estimates the missing projections by alternating between back-projection and reprojection. [7] It is mathematically equivalent to the iteration in the Fourier domain known as the GP algorithm. [9,10] and the iteration of the Gerchberg-Papoulis (GP) algorithm was proven to mathematically converge for recovering bandlimited signals.…”

Section: Problem Redefinitionmentioning

confidence: 99%

“…Duan et al developed an iterative reconstruction-reprojection (IRR) algorithm with total variation (TV) constraint for few-view projections. [7] One of the difficulties in the implementation of their algorithm is that IRR iterations lead to the reduction of image resolution and contrast, due to the accumulation of discrete error caused by back-projection and reprojection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Iterative Filtering Reconstruction Algorithm for Gamma Computerized Tomography: Application to Two-Phase Flow Visualization

Xue

Wang

Kuihong

et al. 2012

Instrumentation Science & Technology

View full text Add to dashboard Cite

& Considering the influence of the limited measurement data on the effectiveness of the image reconstruction algorithms in industrial multiphase flow tomography, an iterative filtering reconstruction algorithm is proposed by redefining inverse problem in this article. In order to improve the image quality in iterative reconstruction algorithm, Wiener filter and soft-threshold algorithm are employed as constraints for the reconstructed image. The developed algorithm is applied to gamma-CT based two-phase flow measurements, both simulated and experimental. The results demonstrate that the proposed method cannot only realize the flow regime identification on horizontal pipe, but also improve the iterative reconstruction process, i.e., the reconstructed image quality is enhanced and the iteration number is reduced.

show abstract

Section: Problem Redefinitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Iterative Filtering Reconstruction Algorithm for Gamma Computerized Tomography: Application to Two-Phase Flow Visualization

Xue

Wang

Kuihong

et al. 2012

Instrumentation Science & Technology

View full text Add to dashboard Cite

show abstract

“…This problem has made the tomographic inversion process the most critical part [6]. [10]. As for the system presented here, there is insufficient information to obtain an accurate reconstructed image with these methods.…”

Section: Image Reconstructionmentioning

confidence: 99%

Development of a low-energy gamma CT system: Application to two-phase pipe flow visualization

Xue

Wang

Shan

et al. 2011

2011 IEEE International Conference on Imaging Systems and Techniques

View full text Add to dashboard Cite

This paper describes the application of a lowenergy γ-ray computed tomography (CT) system to twophase flow measurement. A 241 m A (59.5 KeV) γ-ray source is used to penetrate a 82 mm i.d. pipe, an array of 17 detector elements is utilized to measure the projection data, and a rotating mechanism is employed to get 3 to 11 projections with the single source. Because this system offers only 3 to 11 projections, and the detector elements are sparsely arranged, conventional reconstruction algorithms are ineffective, thus improvement based on matrix completion algorithm has been developed to improve the imaging quality. Reconstructed results from measurement data demonstrate the effectiveness of this technique. Dynamic experiment result of a horizontally moving Perspex model are also presented, which clearly show the shape and size of the model.

show abstract

“…However, when the number of projection images for CT reconstruction is reduced to 100 or less [5] while covering the complete 180°or 360°angular range, it refers to as the sparse-view CT. CS-based image reconstruction from sparse views is extensively studied in the field of medical imaging to reduce the potential radiation dose delivered to the patients [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. It is the most popular method of handling the sparse data.…”

Section: Introductionmentioning

confidence: 99%

Sparse-view neutron CT reconstruction of irradiated fuel assembly using total variation minimization with Poisson statistics

Abir

Islam

Wachs

et al. 2015

J Radioanal Nucl Chem

View full text Add to dashboard Cite

We inspect the nuclear fuel assembly by demonstrating the potential use of sparse-view neutron computed tomography. The projection images of the fuel assembly were collected at the Idaho National Laboratory hot fuel examination facility using indirect foil-film transfer technique. The radiographs were digitized using a commercial film digitizer and registered spatially for reconstruction. Digitized data were reconstructed using simultaneous algebraic reconstruction technique (SART) with total variation minimization using a dual approach for numerical solution assuming the projection data are corrupted by Poisson noise. To validate and evaluate the performance of the algorithm, visual inspections, as well as quantitative evaluation studies using a computer simulation data and the experimental data of the fuel assembly were carried out. The proposed method provides better reconstruction for both simulated and experimental case in terms of artifact reduction, higher SNR, and better spatial resolution compared to the reconstruction yielded by filtered back projection and SART reconstruction.

show abstract

Few-View Projection Reconstruction With an Iterative Reconstruction-Reprojection Algorithm and TV Constraint

Cited by 46 publications

References 8 publications

Iterative Filtering Reconstruction Algorithm for Gamma Computerized Tomography: Application to Two-Phase Flow Visualization

Iterative Filtering Reconstruction Algorithm for Gamma Computerized Tomography: Application to Two-Phase Flow Visualization

Development of a low-energy gamma CT system: Application to two-phase pipe flow visualization

Sparse-view neutron CT reconstruction of irradiated fuel assembly using total variation minimization with Poisson statistics

Contact Info

Product

Resources

About