diff --git a/tests/preprocessing/test_norm_stats.py b/tests/preprocessing/test_norm_stats.py
new file mode 100644
index 000000000..fe51bf59a
--- /dev/null
+++ b/tests/preprocessing/test_norm_stats.py
@@ -0,0 +1,20 @@
+import numpy as np
+from viscy.utils.mp_utils import get_val_stats
+
+
+def test_get_val_stats():
+    sample_values = np.arange(0, 101)
+    stats = get_val_stats(sample_values)
+    expected_stats = {
+        "mean": 50.0,
+        "std": np.std(sample_values),
+        "median": 50.0,
+        "iqr": 50.0,
+        "p1": 1.0,
+        "p5": 5.0,
+        "p95": 95.0,
+        "p99": 99.0,
+        "p99_p1": 98.0,
+        "p95_p5": 90.0,
+    }
+    assert stats == expected_stats
diff --git a/viscy/utils/meta_utils.py b/viscy/utils/meta_utils.py
index f948fb849..2bd79081d 100644
--- a/viscy/utils/meta_utils.py
+++ b/viscy/utils/meta_utils.py
@@ -31,7 +31,15 @@ def write_meta_field(position: ngff.Position, metadata, field_name, subfield_nam
     """
     if field_name in position.zattrs:
         if subfield_name in position.zattrs[field_name]:
-            position.zattrs[field_name][subfield_name].update(metadata)
+            # Need to create a new dict and reassign to trigger zarr write
+            updated_subfield = {
+                **position.zattrs[field_name][subfield_name],
+                **metadata,
+            }
+            position.zattrs[field_name] = {
+                **position.zattrs[field_name],
+                subfield_name: updated_subfield,
+            }
         else:
             D1 = position.zattrs[field_name]
             field_metadata = {
diff --git a/viscy/utils/mp_utils.py b/viscy/utils/mp_utils.py
index 4db77e4de..6b39795f8 100644
--- a/viscy/utils/mp_utils.py
+++ b/viscy/utils/mp_utils.py
@@ -2,7 +2,6 @@
 
 import iohub.ngff as ngff
 import numpy as np
-import scipy.stats
 
 import viscy.utils.image_utils as image_utils
 import viscy.utils.masks as mask_utils
@@ -260,11 +259,21 @@ def get_val_stats(sample_values):
                                         indices
     :return dict meta_row: Dict with intensity data for image
     """
-
+    percentiles = [1, 5, 25, 50, 75, 95, 99]
+    percentile_values = {
+        k: float(v)
+        for k, v in zip(percentiles, np.nanpercentile(sample_values, percentiles))
+    }
     meta_row = {
         "mean": float(np.nanmean(sample_values)),
         "std": float(np.nanstd(sample_values)),
-        "median": float(np.nanmedian(sample_values)),
-        "iqr": float(scipy.stats.iqr(sample_values)),
+        "median": percentile_values[50],
+        "iqr": percentile_values[75] - percentile_values[25],
+        "p5": percentile_values[5],
+        "p95": percentile_values[95],
+        "p95_p5": percentile_values[95] - percentile_values[5],
+        "p1": percentile_values[1],
+        "p99": percentile_values[99],
+        "p99_p1": percentile_values[99] - percentile_values[1],
     }
     return meta_row