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@@ -26,6 +26,7 @@ Biobank-scale imaging provides a unique opportunity to characterise structural a
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  ## Model Details
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  During this research, the original [DiffAE](https://diff-ae.github.io/) model was adapted and extended for 3D to create the 3D DiffAE model, and was trained on the CINE Cardiac Long-axis 4-chamber view MRIs from UK Biobank dataset using 5 different seeds. This model can be used to infer latent representations from similar cardiac MRIs, or can also be used as pretrained models and then fine-tuned on other datasets or tasks.
 
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  ### Model Description
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@@ -33,6 +34,8 @@ During this research, the original [DiffAE](https://diff-ae.github.io/) model wa
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  - **Task:** Obtaining latent representation from 3D input volumes
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  - **Training dataset:** [CINE Cardiac Long-axis 4-chamber view MRIs from UK Biobank](https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=20208)
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  - **Training seed:** 1994
 
 
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  ### Model Sources
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  ## Model Details
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  During this research, the original [DiffAE](https://diff-ae.github.io/) model was adapted and extended for 3D to create the 3D DiffAE model, and was trained on the CINE Cardiac Long-axis 4-chamber view MRIs from UK Biobank dataset using 5 different seeds. This model can be used to infer latent representations from similar cardiac MRIs, or can also be used as pretrained models and then fine-tuned on other datasets or tasks.
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+ This model can also be used to generate synthetic cardiac MRIs similar to the training set.
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  ### Model Description
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  - **Task:** Obtaining latent representation from 3D input volumes
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  - **Training dataset:** [CINE Cardiac Long-axis 4-chamber view MRIs from UK Biobank](https://biobank.ctsu.ox.ac.uk/crystal/field.cgi?id=20208)
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  - **Training seed:** 1994
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+ - **Input:** 3D MRI (2D over time), intensity normalised (min-max, followed by z-score with 0.5 mean and std)
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+ - **Output:** 128 latent factors. Can also be used for generating synthetic MRIs.
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  ### Model Sources
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