diff --git a/direct/nn/mri_models.py b/direct/nn/mri_models.py
index f7b3a7f8..eb047eb6 100644
--- a/direct/nn/mri_models.py
+++ b/direct/nn/mri_models.py
@@ -136,6 +136,32 @@ def _do_iteration(
             regularizer_dict = {
                 k: torch.tensor([0.0], dtype=data["target"].dtype).to(self.device) for k in regularizer_fns.keys()
             }
+
+            if self.ndim == 3 and "registration_model" in self.models:
+                # Perform registration and compute loss on registered image and displacement field
+                registered_image, displacement_field = self.do_registration(data, output_image)
+
+                # If DL-based model calculate loss
+                if len(list(self.models["registration_model"].parameters())) > 0:
+                    shape = data["reference_image"].shape
+                    loss_dict = self.compute_loss_on_data(
+                        loss_dict,
+                        loss_fns,
+                        data,
+                        output_image=registered_image,
+                        target_image=(
+                            data["reference_image"]
+                            if shape == registered_image.shape
+                            else data["reference_image"].tile((1, registered_image.shape[1], *([1] * len(shape[1:]))))
+                        ),
+                    )
+                    loss_dict = self.compute_loss_on_data(
+                        loss_dict,
+                        loss_fns,
+                        data,
+                        output_displacement_field=displacement_field,
+                        target_displacement_field=data["displacement_field"],
+                    )
             loss_dict = self.compute_loss_on_data(loss_dict, loss_fns, data, output_image, output_kspace)
             regularizer_dict = self.compute_loss_on_data(
                 regularizer_dict, regularizer_fns, data, output_image, output_kspace
@@ -150,7 +176,11 @@ def _do_iteration(
         regularizer_dict = detach_dict(regularizer_dict)
 
         return DoIterationOutput(
-            output_image=output_image,
+            output_image=(
+                (output_image, registered_image)
+                if (self.ndim == 3 and "registration_model" in self.models)
+                else output_image
+            ),
             sensitivity_map=data["sensitivity_map"],
             sampling_mask=data["sampling_mask"],
             data_dict={**loss_dict, **regularizer_dict},