Fully unsupervised deep generative modeling (FU-DGM) is promising for compressively sampled MRI (CS-MRI) when training data or compute are limited. Classical FU-DGMs such as DIP and INR rely on architectural priors, but the ill-conditioned inverse problem often demands many iterations and easily overfits measurement noise. We propose CogGen, a cognitive-load-informed FU-DGM that casts CS-MRI as staged inversion and regulates task-side "cognitive load" by progressively scheduling intrinsic difficulty and extraneous interference. CogGen replaces uniform data fitting with an easy-to-hard k-space weighting/selection strategy: early iterations emphasize low-frequency, high-SNR, structure-dominant samples, while higher-frequency or noise-dominated measurements are introduced later. We realize this schedule through self-paced curriculum learning (SPCL) with complementary criteria: a student mode that reflects what the model can currently learn and a teacher mode that indicates what it should follow, supporting both soft weighting and hard selection. Experiments and analyses show that CogGen-DIP and CogGen-INR improve reconstruction fidelity and convergence behavior compared with strong unsupervised baselines and competitive supervised pipelines.

翻译：全无监督深度生成建模（FU-DGM）在训练数据或计算资源有限时，对压缩感知磁共振成像（CS-MRI）具有广阔前景。经典的FU-DGM方法（如DIP和INR）依赖于架构先验，但病态逆问题通常需要大量迭代且易对测量噪声过拟合。我们提出CogGen，一种基于认知负荷的FU-DGM方法，将CS-MRI重构构建为分阶段逆问题求解过程，并通过渐进式调度内在难度与外部干扰来调控任务侧的“认知负荷”。CogGen采用由易到难的k空间加权/选择策略替代均匀数据拟合：早期迭代强调低频、高信噪比、结构主导的样本，而高频或噪声主导的测量值则在后期引入。我们通过具有互补准则的自步课程学习（SPCL）实现该调度：反映模型当前学习能力的“学生模式”与指示其应遵循目标的“教师模式”，同时支持软加权与硬选择。实验与分析表明，相较于强无监督基线方法与有竞争力监督流程，CogGen-DIP和CogGen-INR在重建保真度与收敛行为方面均有提升。