PINER: Prior-informed Implicit Neural Representation Learning for Test-time Adaption in Sparse-view

Authors: Song, Bowen*; Shen, Liyue; Xing, Lei Description: Recently, deep learning has been introduced to solve important medical image reconstruction problems such as sparse-view CT reconstruction. However, the developed deep reconstruction models are generally limited in generalization when applied to unseen testing samples in target domain. Furthermore, privacy concerns may impede the availability of source-domain training data to retrain or adapt the model to the target-domain testing data, which are quite common in real-world medical applications. To address these issues, we introduce a source-free black-box test-time adaptation method for sparse-view CT reconstruction with unknown noise levels based on prior-informed implicit neural representation learning (PINER). By leveraging implicit neural representation learning to generate the image representations at various noise levels, the proposed method is able to construct the adapted input representations at test time based on the inference of black-box model and output analysis. We performed experiments of source-free test-time adaptation for sparse-view CT reconstruction with unknown noise levels on multiple anatomical sites with different black-box deep reconstruction models, where our method outperforms the state-of-the-art algorithms.