Joint reconstruction of multi-contrast images: compressive sensing reconstruction using both joint and individual regularization functions

Kopanoglu, Emre ORCID: https://orcid.org/0000-0001-8982-4441, Güngör, Alper, Kilic, Toygan, Saritas, Emine Ulku, Çukur, Tolga and Guven, H. Emre 2017. Joint reconstruction of multi-contrast images: compressive sensing reconstruction using both joint and individual regularization functions. Presented at: ISMRM 25th Annual Meeting & Exhibition SMRT 26th Annual Meeting, Honolulu, HI, USA, 22-27 April 2017. Proceedings of the 25th ISMRM 25th Annual Meeting & Exhibition. p. 3875.

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Abstract

In many clinical settings, multi-contrast images of a patient are acquired to maximize complementary information. With the underlying anatomy being the same, the mutual information in multi-contrast data can be exploited to improve image reconstruction, especially in accelerated acquisition schemes such as Compressive Sensing (CS). This study proposes a CS-reconstruction algorithm that uses four regularization functions; joint L1-sparsity and TV-regularization terms to exploit the mutual information, and individual L1-sparsity and TV-regularization terms to recover unique features in each image. The proposed method is shown to be robust against leakage-of-features across contrasts, and is demonstrated using simulations and in-vivo experiments.

Item Type:	Conference or Workshop Item (Poster)
Status:	Published
Schools:	Psychology Cardiff University Brain Research Imaging Centre (CUBRIC)
Date of First Compliant Deposit:	24 February 2021
Date of Acceptance:	22 June 2017
Last Modified:	09 Nov 2022 10:37
URI:	https://orca.cardiff.ac.uk/id/eprint/140074

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