High-Dimensional MR Reconstruction Integrating Subspace and Adaptive Generative Models

作     者：Zhao, Ruiyang Peng, Xi Kelkar, Varun A. Anastasio, Mark A. Lam, Fan 

作者机构：The Department of Electrical and Computer Engineering Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign IL61801 United States The Department of Radiology Mayo Clinic RochesterMN55902 United States The Department of Bioengineering Beckman Institute for Advanced Science and Technology University of Illinois Urbana-Champaign IL61801 United States 

出 版 物：《arXiv》 (arXiv)

年 卷 期：2023年

核心收录：

主　　题：Image reconstruction 

摘      要：We present a novel method that integrates subspace modeling with an adaptive generative image prior for high-dimensional MR image reconstruction. The subspace model imposes an explicit low-dimensional representation of the high-dimensional images, while the generative image prior serves as a spatial constraint on the contrast-weighted images or the spatial coefficients of the subspace model. A formulation was introduced to synergize these two components with complimentary regularization such as joint sparsity. A special pretraining plus subject-specific network adaptation strategy was proposed to construct an accurate generative-model-based representation for images with varying contrasts, validated by experimental data. An iterative algorithm was introduced to jointly update the subspace coefficients and the multi-resolution latent space of the generative image model that leveraged a recently developed intermediate layer optimization technique for network inversion. We evaluated the utility of the proposed method in two high-dimensional imaging applications: accelerated MR parameter mapping and high-resolution MRSI. Improved performance over state-of-the-art subspace-based methods was demonstrated in both cases. Our work demonstrated the potential of integrating data-driven and adaptive generative models with low-dimensional representation for high-dimensional imaging problems. © 2023, CC BY.