Fully unsupervised deep generative modeling (FU-DGM) offers significant potential for compressively sampled magnetic resonance imaging (CS-MRI) reconstruction. Representative FU-DGM formulations, such as deep image prior (DIP) and implicit neural representation (INR), employ architectural bias to induce a low-dimensional manifold in the image space that aligns with the forward observation. However, as the underlying inverse system is highly ill-posed, prolonged iterative fitting in FU-DGM typically leads to poor efficiency and noise amplification. In this paper, guided by the cognitive principle of easy-to-hard learning, we propose CogGen, an FU-DGM framework that reformulates CS-MRI reconstruction as a staged inversion problem. Specifically, CogGen implements an self-paced curriculum learning (SPCL)-driven progressive scheduling strategy through an MRI-aware dual-threshold weighting criterion, which adaptively regulates k-space measurement participation. The data-consistency residual thresholding evaluates the fitting reliability of the current generator, while the k-space radius thresholding controls stage-wise measurement exposure, thereby avoiding uniform fitting throughout optimization. Theoretically, our analysis shows that, when early stages favor easy-to-fit measurements, CogGen yields a reduced local sufficient-iteration bound and a smaller cumulative noise-amplification bound, explaining the improved convergence behavior and reconstruction fidelity of CogGen within a finite iteration budget. Numerical experiments demonstrate that both CogGen instantiations, CogGen-DIP and CogGen-INR, achieve superior performance over prevailing CS-MRI reconstruction techniques, including unsupervised and supervised pipelines.

翻译：全无监督深度生成建模（FU-DGM）在压缩采样磁共振成像（CS-MRI）重建中展现出巨大潜力。代表性的FU-DGM方法，如深度图像先验（DIP）和隐式神经表示（INR），利用架构偏置在图像空间中诱导出与前向观测一致的低维流形。然而，由于底层的逆系统高度病态，FU-DGM中的长时间迭代拟合通常会导致效率低下和噪声放大。本文受从易到难学习的认知原则启发，提出CogGen——一种将CS-MRI重建重新表述为分阶段逆问题的FU-DGM框架。具体而言，CogGen通过一种MRI感知的双阈值加权准则，实现自定步调课程学习（SPCL）驱动的渐进调度策略，该准则自适应地调节k空间测量参与度。数据一致性残差阈值评估当前生成器的拟合可靠性，而k空间半径阈值控制阶段性的测量暴露，从而避免在整个优化过程中进行均匀拟合。理论上，我们的分析表明，当早期阶段偏向于易于拟合的测量时，CogGen能降低局部充分迭代界并减小累积噪声放大界，这解释了CogGen在有限迭代预算下改进的收敛行为与重建保真度。数值实验表明，CogGen的两种实例化——CogGen-DIP和CogGen-INR——在性能上优于当前主流的CS-MRI重建技术，包括无监督和监督管道。