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High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models
Journal article   Open access   Peer reviewed

High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models

Ruiyang Zhao, Xi Peng, Varun A Kelkar, Mark A Anastasio and Fan Lam
IEEE transactions on biomedical engineering, Vol.71(6), pp.1969-1979
06/2024
DOI: 10.1109/TBME.2024.3358223
PMCID: PMC11105985
PMID: 38265912
url
https://doi.org/10.1109/TBME.2024.3358223View
Published (Version of record) Open Access

Abstract

OBJECTIVE To develop a new method that integrates subspace and generative image models for high-dimensional MR image reconstruction.METHODS We proposed a formulation that synergizes a low-dimensional subspace model of high-dimensional images, an adaptive generative image prior serving as spatial constraints on the sequence of "contrast-weighted" images or spatial coefficients of the subspace model, and a conventional sparsity regularization. A special pretraining plus subject-specific network adaptation strategy was proposed to construct an accurate generative-network-based representation for images with varying contrasts. An iterative algorithm was introduced to jointly update the subspace coefficients and the multi-resolution latent space of the generative image model that leveraged an recently proposed intermediate layer optimization technique for network inversion.RESULTS We evaluated the utility of the proposed method for two high-dimensional imaging applications: accelerated MR parameter mapping and high-resolution MR spectroscopic imaging. Improved performance over state-of-the-art subspace-based methods was demonstrated in both cases.CONCLUSION The proposed method provided a new way to address high-dimensional MR image reconstruction problems by incorporating an adaptive generative model as a data-driven spatial prior for constraining subspace reconstruction.SIGNIFICANCE Our work demonstrated the potential of integrating data-driven and adaptive generative priors with canonical low-dimensional modeling for high-dimensional imaging problems.

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