One potential limitation of X-Diffusion in handling complex tissue interfaces is the generation of artifacts at these interfaces, leading to inaccuracies in the synthesized MRI volumes. To address this limitation, several improvements can be implemented:
Fine-tuning on diverse datasets: Training X-Diffusion on a more diverse set of MRI datasets that include a wide range of tissue interfaces can help the model learn to handle complex structures more effectively.
Data augmentation: Augmenting the training data with variations in tissue interfaces, noise levels, and artifacts can help the model generalize better to complex scenarios and reduce the generation of artifacts.
Adaptive resolution: Implementing a mechanism that dynamically adjusts the resolution of the generated MRI volumes based on the complexity of the tissue interfaces can help improve the model's performance in handling intricate structures.
Ensemble methods: Utilizing ensemble methods by combining multiple X-Diffusion models trained with different hyperparameters or architectures can help mitigate limitations and improve overall performance in handling complex tissue interfaces.