To extend X-Diffusion for generating dynamic MRI sequences, we can incorporate recurrent neural networks (RNNs) or temporal convolutional networks (TCNs) into the model architecture. By feeding the model sequential MRI slices over time, we can train it to understand the temporal evolution of structures within the MRI volumes. This approach would enable X-Diffusion to capture dynamic changes in tissues or organs over time, providing a more comprehensive understanding of the patient's condition.
Additionally, we can introduce attention mechanisms to focus on relevant regions in each time step, allowing the model to adaptively select and combine information from different time points. By incorporating these temporal components, X-Diffusion can generate not only static 3D volumes but also dynamic sequences that reflect changes over time in the MRI data.