Convex optimization for 3D parallel MRI reconstruction

Kai Zhu, Cishen Zhang, Jingxin Zhang, Ifat Al Baqee, Sulaiman A. Al-Hasani

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

The 3dimensional (3D) volume imaging in magnetic resonanceimaging (MRI) enables encoding in frequency, phaseand partition directions in k-space. To increase scan speed,subsampling of multiple-coil k-space data is applied. The imagereconstruction problem from subsampled 3D k-space datais non-convex due to the coupling between image and sensitivityprofile of coils. In this paper, it is shown that the magnitudeof 3D image is constrained in a convex hull and thus canbe solved by a two-step convex optimization procedure. Comparedwith other methods, this method directly processes 3Dk-space data without using sensitivity profile of coils. Phantomand in vivo data are used to test the proposed method,resulting in improved reconstructed image with lower meansquared error.

Original languageEnglish
Title of host publicationProceedings of the 8th International Conference on Signal Processing Systems, ICSPS 2016
Subtitle of host publicationAuckland, New Zealand; 21-24 November 2016
Place of PublicationNew Yrok NY USA
PublisherAssociation for Computing Machinery (ACM)
Pages41-45
Number of pages5
ISBN (Electronic)9781450347907
DOIs
Publication statusPublished - 21 Nov 2016
EventInternational Conference on Signal Processing Systems 2016 - Auckland, New Zealand
Duration: 21 Nov 201624 Nov 2016
Conference number: 8th
https://dl.acm.org/doi/proceedings/10.1145/3015166

Conference

ConferenceInternational Conference on Signal Processing Systems 2016
Abbreviated titleICSPS 2016
CountryNew Zealand
CityAuckland
Period21/11/1624/11/16
Internet address

Keywords

  • Convex optimization
  • Parallel magnetic resonance imaging
  • Volume imaging

Cite this

Zhu, K., Zhang, C., Zhang, J., Baqee, I. A., & Al-Hasani, S. A. (2016). Convex optimization for 3D parallel MRI reconstruction. In Proceedings of the 8th International Conference on Signal Processing Systems, ICSPS 2016: Auckland, New Zealand; 21-24 November 2016 (pp. 41-45). Association for Computing Machinery (ACM). https://doi.org/10.1145/3015166.3015181