diff --git a/GANDLF/cli/generate_metrics.py b/GANDLF/cli/generate_metrics.py
index aa47e5f69..eaef3f77f 100644
--- a/GANDLF/cli/generate_metrics.py
+++ b/GANDLF/cli/generate_metrics.py
@@ -253,18 +253,20 @@ def __percentile_clip(input_tensor, reference_tensor=None, p_min=0.5, p_max=99.5
             ] = structural_similarity_index(gt_image_infill, output_infill, mask).item()
 
             # ncc metrics
-            overall_stats_dict[current_subject_id]["ncc_mean"] = ncc_mean(
-                gt_image_infill, output_infill
-            )
-            overall_stats_dict[current_subject_id]["ncc_std"] = ncc_std(
-                gt_image_infill, output_infill
-            )
-            overall_stats_dict[current_subject_id]["ncc_max"] = ncc_max(
-                gt_image_infill, output_infill
-            )
-            overall_stats_dict[current_subject_id]["ncc_min"] = ncc_min(
-                gt_image_infill, output_infill
-            )
+            compute_ncc = parameters.get("compute_ncc", True)
+            if compute_ncc:
+                overall_stats_dict[current_subject_id]["ncc_mean"] = ncc_mean(
+                    gt_image_infill, output_infill
+                )
+                overall_stats_dict[current_subject_id]["ncc_std"] = ncc_std(
+                    gt_image_infill, output_infill
+                )
+                overall_stats_dict[current_subject_id]["ncc_max"] = ncc_max(
+                    gt_image_infill, output_infill
+                )
+                overall_stats_dict[current_subject_id]["ncc_min"] = ncc_min(
+                    gt_image_infill, output_infill
+                )
 
             # only voxels that are to be inferred (-> flat array)
             # these are required for mse, psnr, etc.