Motion-compensated cone beam computed tomography using a conjugate gradient least-squares algorithm and electrical impedance tomography imaging motion data

T. Pengpen, M. Soleimani

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
150 Downloads (Pure)

Abstract

Cone beam computed tomography (CBCT) is an imaging modality that has been used in image-guided radiation therapy (IGRT). For applications such as lung radiation therapy, CBCT images are greatly affected by the motion artefacts. This is mainly due to low temporal resolution of CBCT. Recently, a dual modality of electrical impedance tomography (EIT) and CBCT has been proposed, in which the high temporal resolution EIT imaging system provides motion data to a motion-compensated algebraic reconstruction technique (ART)-based CBCT reconstruction software. High computational time associated with ART and indeed other variations of ART make it less practical for real applications. This paper develops a motion-compensated conjugate gradient least-squares (CGLS) algorithm for CBCT. A motion-compensated CGLS offers several advantages over ART-based methods, including possibilities for explicit regularization, rapid convergence and parallel computations. This paper for the first time demonstrates motion-compensated CBCT reconstruction using CGLS and reconstruction results are shown in limited data CBCT considering only a quarter of the full dataset. The proposed algorithm is tested using simulated motion data in generic motion-compensated CBCT as well as measured EIT data in dual EIT–CBCT imaging.
Original languageEnglish
Article number20140390
JournalPhilosophical Transactions of the Royal Society A - Mathematical Physical and Engineering Sciences
Volume373
Issue number2043
Early online date4 May 2015
DOIs
Publication statusPublished - Jun 2015

Keywords

  • Cone beam computed tomography
  • Conjugate gradient least squares
  • Dual modality imaging
  • Electrical impedance tomography

Fingerprint Dive into the research topics of 'Motion-compensated cone beam computed tomography using a conjugate gradient least-squares algorithm and electrical impedance tomography imaging motion data'. Together they form a unique fingerprint.

Cite this