Evaluation of Sparse-view Reconstruction from Flat-panel-detector Cone-beam CT

By Junguo Bian, Jeffrey H. Siewerdsen, Xiao Han, Emil Y. Sidky, Jerry L Prince, Charles A. Pelizzari, Xiaochuan Pan. Physics in Medicine and Biology, 2010

398 citations recorded by the source corpus.

Flat-panel-detector x-ray cone-beam computed tomography (CBCT) is used in a rapidly increasing host of imaging applications, including image-guided surgery and radiotherapy. The purpose of the work is to investigate and evaluate image reconstruction from data collected at projection views significantly fewer than what is used in current CBCT imaging. Specifically, we carried out imaging experiments using a bench-top CBCT system that was designed to mimic imaging conditions in image-guided surgery and radiotherapy; we applied an image reconstruction algorithm based on constrained total-variation (TV)-minimization to data acquired with sparsely sampled view-angles and conducted extensive evaluation of algorithm performance. Results of the evaluation studies demonstrate that, depending upon scanning conditions and imaging tasks, algorithms based on constrained TV-minimization can reconstruct images of potential utility from a small fraction of the data used in typical, current CBCT applications. A practical implication of the study is that the optimization of algorithm design and implementation can be exploited for considerably reducing imaging effort and radiation dose in CBCT.

Subfields: Biomedical Engineering, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/be7671fd95348f8f11f94b6eb2d06bddc9caf2d5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biomedical Engineering

1. Xiaokun Liang
2. Zhicheng Zhang
3. Xu Dong
4. Yaoqin Xie
5. Guohua Cao
6. Xiaochuan Pan
7. Emil Y. Sidky
8. Ge Wang

In Radiology, Nuclear Medicine and Imaging

1. Xiaokun Liang
2. Zhicheng Zhang
3. Xu Dong
4. Yaoqin Xie
5. Guohua Cao
6. Xin Jin
7. Liang Li
8. Zhiqiang Chen

Citations in the corpus

• A Sparse-View CT Reconstruction Method Based on Combination of DenseNet and Deconvolution (2018, background): “However, it will lead to the problem of insufficient projection data, to the extent that sparse-view CT will suffer from severe streaking artifacts [14].”
• A limited-angle CT reconstruction method based on anisotropic TV minimization (2013, methodology): “Exemplary algorithms are POCS with TV minimization (POCS + TV) (Sidky et al 2006, 2009, Bian et al 2010), PICCS (Chen et al 2008, 2009, Tang et al 2009), simultaneous algebraic reconstruction technique (SART) with TV (SART + TV) (Yu and Wang 2009), soft-thresholding algorithms (Yu and Wang 2010, Xu…”
• Convex optimization problem prototyping for image reconstruction in computed tomography with the Chambolle-Pock algorithm (2011, methodology): “Optimization problems based on the Total Variation (TV) semi-norm Optimization problems with the TV semi-norm have received much attention for CT image reconstruction lately because of their potential to provide high quality images from sparse view sampling [8], [30], [22], [9], [31], [32], [33].”
• DART: A Practical Reconstruction Algorithm for Discrete Tomography (2011, background): “In such cases, sparsity of the gradient image can be exploited by total variation minimization [8], [10], [28].”