The Moner's motion estimation and image reconstruction can be enhanced by integrating additional physical or physiological priors in several ways:
Dynamic Motion Models: Beyond the quasi-static rigid motion model, incorporating dynamic motion models that account for varying motion patterns over time can significantly improve accuracy. For instance, using models that capture respiratory or cardiac motion can help in scenarios where these factors contribute to motion artifacts.
Physiological Constraints: Integrating physiological priors, such as anatomical knowledge about tissue properties and expected motion patterns, can guide the reconstruction process. This can be achieved through the use of anatomical atlases or statistical shape models that inform the expected structure and motion of different tissues.
Multi-Modal Data Fusion: Combining data from different imaging modalities (e.g., MRI with CT or PET) can provide complementary information that enhances motion estimation and image quality. This multi-modal approach can leverage the strengths of each modality to improve overall reconstruction fidelity.
Regularization Techniques: Advanced regularization techniques that incorporate physical models of tissue behavior can help in constraining the solution space during reconstruction. For example, using total variation or wavelet-based regularization can preserve edges and fine details while reducing noise.
Machine Learning Approaches: Employing machine learning techniques to learn motion patterns from large datasets can provide a more flexible and adaptive framework for motion estimation. This can include training models on diverse datasets to capture a wide range of motion scenarios, improving the model's ability to generalize to unseen data.
By incorporating these additional priors, the Moner framework can achieve more accurate motion correction and higher-quality image reconstructions, ultimately enhancing its applicability in clinical settings.