4D computed tomography reconstruction from few-projection data via temporal non-local regularization.

Xun Jia, Yifei Lou, Bin Dong, Zhen Tian, Steve Jiang

Research output: Chapter in Book/Report/Conference proceedingChapter

33 Scopus citations

Abstract

4D computed tomography (4D-CT) is an important modality in medical imaging due to its ability to resolve patient anatomy motion in each respiratory phase. Conventionally 4D-CT is accomplished by performing the reconstruction for each phase independently as in a CT reconstruction problem. We propose a new 4D-CT reconstruction algorithm that explicitly takes into account the temporal regularization in a non-local fashion. By imposing a regularization of a temporal non-local means (TNLM) form, 4D-CT images at all phases can be reconstructed simultaneously based on extremely under-sampled x-ray projections. Our algorithm is validated in one digital NCAT thorax phantom and two real patient cases. It is found that our TNLM algorithm is capable of reconstructing the 4D-CT images with great accuracy. The experiments also show that our approach outperforms standard 4D-CT reconstruction methods with spatial regularization of total variation or tight frames.

Original languageEnglish (US)
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages143-150
Number of pages8
Volume13
EditionPt 1
Publication statusPublished - 2010
Externally publishedYes

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ASJC Scopus subject areas

  • Medicine(all)

Cite this

Jia, X., Lou, Y., Dong, B., Tian, Z., & Jiang, S. (2010). 4D computed tomography reconstruction from few-projection data via temporal non-local regularization. In Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention (Pt 1 ed., Vol. 13, pp. 143-150)