Adapting the 2D Moner framework to 3D MRI motion correction presents several challenges and considerations:
Increased Computational Complexity: The transition from 2D to 3D significantly increases the computational burden. The model must handle a larger volume of data, which requires more memory and processing power. Efficient algorithms and hardware acceleration (e.g., using GPUs) will be necessary to manage this complexity.
Motion Model Generalization: The quasi-static motion model currently used in Moner is based on rigid motion assumptions. In 3D, the model must account for more complex motion dynamics, including non-rigid deformations and varying motion patterns across different anatomical regions. Developing a robust 3D motion model that can accurately capture these dynamics is crucial.
Data Acquisition and Sampling Patterns: 3D MRI often involves different sampling strategies (e.g., 3D radial, Cartesian, or spiral). The Moner framework will need to be flexible enough to adapt to these various patterns while ensuring that the reconstruction process remains accurate and efficient.
Integration of Priors: The incorporation of additional physical or physiological priors becomes more complex in 3D. The model must effectively leverage these priors to improve reconstruction quality while maintaining computational efficiency.
Validation and Testing: Extensive validation on diverse 3D MRI datasets is essential to ensure that the adapted Moner framework performs well across different scenarios. This includes testing on datasets with varying motion artifacts and anatomical structures to assess the model's robustness and generalizability.