diff --git a/dap/algos/forcesend.py b/dap/algos/forcesend.py
index d98f0e8..cc60598 100644
--- a/dap/algos/forcesend.py
+++ b/dap/algos/forcesend.py
@@ -4,41 +4,43 @@ import numpy as np
 def calc_force_send(results, data, pixel_mask_pf, image, n_aggregated_images, data_summed):
     force_send_visualisation = False
 
-    if data.dtype != np.uint16:
-        apply_threshold = results.get("apply_threshold", False)
-        apply_aggregation = results.get("apply_aggregation", False)
+    if data.dtype == np.uint16:
+        return data, force_send_visualisation, n_aggregated_images, data_summed
 
-        if not apply_aggregation:
-            data_summed = None
-            n_aggregated_images = 1
+    apply_threshold = results.get("apply_threshold", False)
+    apply_aggregation = results.get("apply_aggregation", False)
 
-        if apply_threshold or apply_aggregation:
-            if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
-                threshold_min = float(results["threshold_min"])
-                threshold_max = float(results["threshold_max"])
-                data[data < threshold_min] = 0.0
-                if threshold_max > threshold_min:
-                    data[data > threshold_max] = 0.0
+    if not apply_aggregation:
+        data_summed = None
+        n_aggregated_images = 1
 
-            if apply_aggregation and "aggregation_max" in results:
-                if data_summed is not None:
-                    data += data_summed
-                    n_aggregated_images += 1
-                data_summed = data.copy()
-                data_summed[data == -np.nan] = -np.nan #TODO: this does nothing
-                results["aggregated_images"] = n_aggregated_images
-                results["worker"] = 1 #TODO: keep this for backwards compatibility?
+    if apply_threshold or apply_aggregation:
+        if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
+            threshold_min = float(results["threshold_min"])
+            threshold_max = float(results["threshold_max"])
+            data[data < threshold_min] = 0.0
+            if threshold_max > threshold_min:
+                data[data > threshold_max] = 0.0
 
-                if n_aggregated_images >= results["aggregation_max"]:
-                    force_send_visualisation = True
-                    data_summed = None
-                    n_aggregated_images = 1
+        if apply_aggregation and "aggregation_max" in results:
+            if data_summed is not None:
+                data += data_summed
+                n_aggregated_images += 1
+            data_summed = data.copy()
+            data_summed[data == -np.nan] = -np.nan #TODO: this does nothing
+            results["aggregated_images"] = n_aggregated_images
+            results["worker"] = 1 #TODO: keep this for backwards compatibility?
 
-            if pixel_mask_pf is not None:
-                data[~pixel_mask_pf] = np.nan
+            if n_aggregated_images >= results["aggregation_max"]:
+                force_send_visualisation = True
+                data_summed = None
+                n_aggregated_images = 1
 
-        else:
-            data = image
+        if pixel_mask_pf is not None:
+            data[~pixel_mask_pf] = np.nan
+
+    else:
+        data = image
 
     return data, force_send_visualisation, n_aggregated_images, data_summed