Untrained neural networks pioneered by Deep Image Prior (DIP) have recently
enabled MRI reconstruction without requiring fully-sampled measurements for
training. Their success is widely attributed to the implicit regularization
induced by suitable network architectures. However, the lack of understanding
of such architectural priors results in superfluous design choices and
sub-optimal outcomes. This work aims to simplify the architectural design
decisions for DIP-MRI to facilitate its practical deployment. We observe that
certain architectural components are more prone to causing overfitting
regardless of the number of parameters, incurring severe reconstruction
artifacts by hindering accurate extrapolation on the un-acquired measurements.
We interpret this phenomenon from a frequency perspective and find that the
architectural characteristics favoring low frequencies, i.e., deep and narrow
with unlearnt upsampling, can lead to enhanced generalization and hence better
reconstruction. Building on this insight, we propose two architecture-agnostic
remedies: one to constrain the frequency range of the white-noise input and the
other to penalize the Lipschitz constants of the network. We demonstrate that
even with just one extra line of code on the input, the performance gap between
the ill-designed models and the high-performing ones can be closed. These
results signify that for the first time, architectural biases on untrained MRI
reconstruction can be mitigated without architectural modifications.

该研究对未经训练的神经网络在磁共振成像（MRI）重建领域的应用进行了讨论，通过理解网络架构的频率特征，提出了两种与架构无关的改进方法，从而使得不合理设计的模型在性能上能够接近高性能模型。