Keywords: Analysis/Processing, PET/MR, Multi-model Registration, Dual Attention Mechanism
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.
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