High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models

• Published in: IEEE transactions on biomedical engineering Vol. 71; no. 6; pp. 1969 - 1979
• Main Authors: Zhao, Ruiyang, Peng, Xi, Kelkar, Varun A., Anastasio, Mark A., Lam, Fan
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.06.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Algorithms; Biomedical engineering; Brain - diagnostic imaging; Data models; GAN inversion; Generative adversarial networks; Generative models; high-dimensional MR imaging; high-resolution MRSI; Humans; Image contrast; Image processing; Image Processing, Computer-Assisted - methods; Image reconstruction; Image resolution; Iterative algorithms; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; MR parameter mapping; Optimization; Optimization techniques; Regularization; Spatial data; subspace modeling; Subspaces
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2024.3358223

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.