Convex optimization for 3D parallel MRI reconstruction

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

doi:10.1145/3015166.3015181

Convex optimization for 3D parallel MRI reconstruction

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

Electrical and Computer Systems Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-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 language	English
Title of host publication	Proceedings of the 8th International Conference on Signal Processing Systems, ICSPS 2016
Subtitle of host publication	Auckland, New Zealand; 21-24 November 2016
Place of Publication	New Yrok NY USA
Publisher	Association for Computing Machinery (ACM)
Pages	41-45
Number of pages	5
ISBN (Electronic)	9781450347907
DOIs	https://doi.org/10.1145/3015166.3015181
Publication status	Published - 21 Nov 2016
Event	International Conference on Signal Processing Systems 2016 - Auckland, New Zealand Duration: 21 Nov 2016 → 24 Nov 2016 Conference number: 8th https://dl.acm.org/doi/proceedings/10.1145/3015166

Conference

Conference	International Conference on Signal Processing Systems 2016
Abbreviated title	ICSPS 2016
Country/Territory	New Zealand
City	Auckland
Period	21/11/16 → 24/11/16
Internet address	https://dl.acm.org/doi/proceedings/10.1145/3015166

Keywords

Convex optimization
Parallel magnetic resonance imaging
Volume imaging

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title = "Convex optimization for 3D parallel MRI reconstruction",

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.",

keywords = "Convex optimization, Parallel magnetic resonance imaging, Volume imaging",

author = "Kai Zhu and Cishen Zhang and Jingxin Zhang and Baqee, {Ifat Al} and Al-Hasani, {Sulaiman A.}",

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Zhu, K, Zhang, C, Zhang, J, Baqee, IA & Al-Hasani, SA 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. Association for Computing Machinery (ACM), New Yrok NY USA, pp. 41-45, International Conference on Signal Processing Systems 2016, Auckland, New Zealand, 21/11/16. https://doi.org/10.1145/3015166.3015181

Convex optimization for 3D parallel MRI reconstruction. / Zhu, Kai; Zhang, Cishen; Zhang, Jingxin et al.
Proceedings of the 8th International Conference on Signal Processing Systems, ICSPS 2016: Auckland, New Zealand; 21-24 November 2016. New Yrok NY USA: Association for Computing Machinery (ACM), 2016. p. 41-45.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

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T1 - Convex optimization for 3D parallel MRI reconstruction

AU - Zhu, Kai

AU - Zhang, Cishen

AU - Zhang, Jingxin

AU - Baqee, Ifat Al

AU - Al-Hasani, Sulaiman A.

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AB - 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.

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KW - Volume imaging

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Convex optimization for 3D parallel MRI reconstruction

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Keywords

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