• Check the image dimensions, voxel spacing, and preprocessing steps (e.g., normalization) used in the original dataset.

• Analyze the data format and how it's being loaded in the model's codebase.

• Review augmentation techniques used during training.

Ensure the image dimensions match those used in the original training dataset. If your dataset has different sizes, you might need to resize or crop them. For volumetric data, 3D resampling to match the voxel resolution can be necessary.If the original dataset used intensity normalization (e.g., scaling pixel values between 0 and 1), you’ll need to apply the same preprocessing to your dataset.

Convert your dataset into the format the model expects, or modify the data loader in the codebase to handle your dataset’s format. If your task requires ground truth labels (e.g., segmentation maps or landmarks for registration), ensure your dataset has corresponding labels and they are formatted correctly.

FBCT and CBCT images likely have different intensity distributions compared to the MRI images the model was initially trained on. You may need to retrain the model from scratch or use domain adaptation techniques (e.g., transfer learning). Modality-specific preprocessing might be necessary. For example, MRI images often have non-linear intensity variations that may not exist in FBCT/CBCT, so consider using preprocessing pipelines suited to your modalities. If the model is expecting a specific number of channels (e.g., grayscale or multi-modal input), ensure your data matches these requirements.