Model-Based Simultaneous Multi-Slice (SMS) Reconstruction with Hankel Subspace Learning for Accelerated MR T1 Mapping

Sugil Kim, Hua Wu and Jae-Ho Han ()
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Sugil Kim: Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
Hua Wu: Center for Cognitive and Neurobiological Imaging, Stanford University, Stanford, CA 94304, USA
Jae-Ho Han: Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea

Mathematics, 2023, vol. 11, issue 13, 1-18

Abstract: Herein, we propose a novel model-based simultaneous multi-slice (SMS) reconstruction method by exploiting data-driven parameter modeling for highly accelerated T1 parameter quantification. We assume that the predefined slice-specific null space operator remains invariant along the parameter dimension. We incorporate the parameter dimension into SMS-HSL to exploit Hankel-structured and Casorati matrices. Given this consideration, the SMS signal is reformulated in k-p space as a constrained optimization problem that exploits rank deficiency for the Hankel-structured matrix and a finite-dimensional basis for a subspace containing slowly evolving signals in the parameter direction. The proposed model-based SMS reconstruction method is validated on in vivo data and compared with state-of-the-art methods with slice acceleration factors of 3 and 5, including an in-plane acceleration factor of 2. The experimental results demonstrate that the proposed method performs effective slice unfolding and signal recovery in reconstructed images and T1 maps with high precision as compared to the state-of-the-art methods.

Keywords: magnetic resonance imaging (MRI); simultaneous multi-slice (SMS); parallel imaging; parameter mapping; null space; low rank (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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