AdaML: Adaptive meta-learning for generalizable sparse-view CT reconstruction

Sparse-view CT reduces radiation exposure but renders image reconstruction severely ill-posed. Although deep learning has substantially improved reconstruction quality, most existing models are trained within specific anatomical domains and often degrade under cross-anatomy distribution shifts. To improve reconstruction generalization under limited target-domain supervision, we investigate a meta-learning framework for sparse-view CT reconstruction. However, directly applying Model-Agnostic Meta-Learning (MAML) to this task is challenging, because CT reconstruction is a high-dimensional inverse problem in which aggressive inner-loop updates can easily induce meta-overfitting and unstable optimization. To address this issue, we first introduce Stable Meta-Learning (StaML), which constructs anatomy-wise tasks and adopts conservative inner-loop updates to learn a more transferable initialization. Building on this, we further propose Adaptive Meta-Learning (AdaML), which dynamically adjusts per-step learning rates during meta-testing to better balance convergence capability and stability. Extensive experiments across multiple CT datasets and anatomical regions, under different k-shot settings, view numbers, and noise levels, demonstrate that AdaML consistently improves reconstruction quality over representative meta-learning and non-meta-learning baselines with favorable convergence. These results suggest that combining stable meta-initialization with adaptive test-time optimization is a promising direction for cross-anatomy sparse-view CT reconstruction. The source code is publicly available at https://github.com/Stth-er/AdaML.