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 multiresolution 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.

翻译：本文提出一种新颖方法，将子空间建模与自适应生成图像先验相结合，用于高维磁共振图像重建。子空间模型对高维图像施加显式低维表示，而生成图像先验则对“对比度加权”图像或子空间模型的空间系数施加空间约束。引入一种公式化方法，通过联合稀疏性等互补正则化策略协同这两个组件。针对对比度变化的图像，提出一种特殊的预训练加受试者特异性网络适应策略，构建基于生成模型的精确表示，并经实验数据验证。引入一种迭代算法，联合更新子空间系数与生成图像模型的多分辨率潜空间，该算法利用近期发展的中间层优化技术实现网络逆映射。通过加速MR参数映射和高分辨率MRSI两项高维成像应用，评估了所提方法的实用价值。与现有最优子空间方法相比，两种应用场景均展现出更优性能。本研究证明了将数据驱动的自适应生成模型与低维表示相结合，在高维成像问题中的潜力。