AA-VoxelMorph: A Weakly-supervised Learning Model for PET-MRI Registration via Adaptive Attention

Motivation: Registered PET-MRI is better than single modality in diagnoses, and traditional algorithms are time-consuming and perform poorly in cross-modal registration. Goal(s): Improve registration efficiency and reduce registration time by improving traditional deep learning networks. Approach: We propose a weakly-supervised PET-MRI registration network based on a hybrid adaptive attention mechanism. Masks extracted from the fine-tuned large model is uesd to constrain the network. Results: We validate the proposed method on liver PET-MRI images. The experimental results show that the proposed method achieves a higher DICE value and shorter registration time than the other state-of-the-art registration algorithms. Impact: Our proposed new network can help doctors to complete the registration between PET and MRI and diagnose a disease in a short period of time.