  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.
