Compressed sensing based cone-beam computed tomography reconstruction with a first-order method

Kihwan Choi, Jing Wang, Lei Zhu, Tae Suk Suh, Stephen Boyd, Lei Xing

Research output: Contribution to journalArticle

176 Scopus citations

Abstract

Purpose: This article considers the problem of reconstructing cone-beam computed tomography (CBCT) images from a set of undersampled and potentially noisy projection measurements. Methods: The authors cast the reconstruction as a compressed sensing problem based on l1 norm minimization constrained by statistically weighted least-squares of CBCT projection data. For accurate modeling, the noise characteristics of the CBCT projection data are used to determine the relative importance of each projection measurement. To solve the compressed sensing problem, the authors employ a method minimizing total-variation norm, satisfying a prespecified level of measurement consistency using a first-order method developed by Nesterov. Results: The method converges fast to the optimal solution without excessive memory requirement, thanks to the method of iterative forward and back-projections. The performance of the proposed algorithm is demonstrated through a series of digital and experimental phantom studies. It is found a that high quality CBCT image can be reconstructed from undersampled and potentially noisy projection data by using the proposed method. Both sparse sampling and decreasing x-ray tube current (i.e., noisy projection data) lead to the reduction of radiation dose in CBCT imaging. Conclusions: It is demonstrated that compressed sensing outperforms the traditional algorithm when dealing with sparse, and potentially noisy, CBCT projection views.

Original languageEnglish (US)
Pages (from-to)5113-5125
Number of pages13
JournalMedical Physics
Volume37
Issue number9
DOIs
Publication statusPublished - Sep 2010

    Fingerprint

Keywords

  • compressed sensing
  • cone-beam computed tomography
  • Nesterov's first order method
  • weighted least-squares

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging
  • Medicine(all)

Cite this