diff --git a/skimage/transform/tests/test_radon_transform.py b/skimage/transform/tests/test_radon_transform.py
index 2e1a016f..e6faa78f 100644
--- a/skimage/transform/tests/test_radon_transform.py
+++ b/skimage/transform/tests/test_radon_transform.py
@@ -3,6 +3,7 @@ from __future__ import division
 
 import numpy as np
 from numpy.testing import *
+import itertools
 from skimage.transform import *
 
 
@@ -130,6 +131,45 @@ def test_radon_circle():
     print(relative_error.max(), relative_error.mean())
     assert np.all(relative_error < 3e-3)
 
+def test_radon_iradon_circle():
+    shape = (61, 79)
+    # Synthetic random data, zero outside reconstruction circle
+    image = np.random.rand(*shape)
+    interpolations = ('nearest', 'linear')
+    output_sizes = (None, min(shape), max(shape), 97)
+
+    for interpolation, output_size in itertools.product(interpolations,
+                                                        output_sizes):
+        print('interpolation =', interpolation)
+        print('output_size =', output_size)
+        c0, c1 = np.ogrid[0:shape[0], 0:shape[1]]
+        r = np.sqrt((c0 - shape[0] // 2)**2 + (c1 - shape[1] // 2)**2)
+        radius = min(shape) // 2
+        image[r >= radius] = 0.
+        # Forward and inverse radon on synthetic data
+        sinogram_rectangle = radon(image, circle=False)
+        reconstruction_rectangle = iradon(sinogram_rectangle,
+                                          output_size=output_size,
+                                          interpolation=interpolation,
+                                          circle=False)
+        sinogram_circle = radon(image, circle=True)
+        reconstruction_circle = iradon(sinogram_circle,
+                                       output_size=output_size,
+                                       interpolation=interpolation,
+                                       circle=True)
+        # Crop rectangular reconstruction to match circle=True reconstruction
+        width = reconstruction_circle.shape[0]
+        excess = int(np.ceil((reconstruction_rectangle.shape[0] - width) / 2))
+        s = np.s_[excess:width + excess, excess:width + excess]
+        reconstruction_rectangle = reconstruction_rectangle[s]
+        # Find the reconstruction circle, set reconstruction to zero outside
+        c0, c1 = np.ogrid[0:width, 0:width]
+        r = np.sqrt((c0 - width // 2)**2 + (c1 - width // 2)**2)
+        reconstruction_rectangle[r >= radius] = 0.
+        print(reconstruction_circle.shape)
+        print(reconstruction_rectangle.shape)
+        np.allclose(reconstruction_rectangle, reconstruction_circle)
+
 
 if __name__ == "__main__":
     run_module_suite()