diff --git a/tests/operators/test_fourier_op.py b/tests/operators/test_fourier_op.py
index 28fd03969..23479a0b6 100644
--- a/tests/operators/test_fourier_op.py
+++ b/tests/operators/test_fourier_op.py
@@ -11,6 +11,18 @@
 from tests.conftest import COMMON_MR_TRAJECTORIES, create_traj
 
 
+class NufftTrajektory(KTrajectory):
+    """Always returns non-grid trajectory type."""
+
+    def _traj_types(
+        self,
+        tolerance: float,
+    ) -> tuple[tuple[TrajType, TrajType, TrajType], tuple[TrajType, TrajType, TrajType]]:
+        true_types = super()._traj_types(tolerance)
+        modified = tuple([tuple([t & (~TrajType.ONGRID) for t in ts]) for ts in true_types])
+        return modified
+
+
 def create_data(im_shape, k_shape, nkx, nky, nkz, type_kx, type_ky, type_kz):
     random_generator = RandomGenerator(seed=0)
 
@@ -39,13 +51,10 @@ def test_fourier_op_fwd_adj_property(
     )
     fourier_op = FourierOp(recon_matrix=recon_matrix, encoding_matrix=encoding_matrix, traj=trajectory)
 
-    # apply forward operator
-    (kdata,) = fourier_op(img)
-
     # test adjoint property; i.e. <Fu,v> == <u, F^Hv> for all u,v
     random_generator = RandomGenerator(seed=0)
-    u = random_generator.complex64_tensor(size=img.shape)
-    v = random_generator.complex64_tensor(size=kdata.shape)
+    u = random_generator.complex64_tensor(size=im_shape)
+    v = random_generator.complex64_tensor(size=k_shape)
     dotproduct_adjointness_test(fourier_op, u, v)
 
 
@@ -64,19 +73,21 @@ def test_fourier_op_norm(im_shape, k_shape, nkx, nky, nkz, type_kx, type_ky, typ
         int(trajectory.kx.max() - trajectory.kx.min() + 1),
     )
     fourier_op = FourierOp(recon_matrix=recon_matrix, encoding_matrix=encoding_matrix, traj=trajectory)
-    # only do few iterations to speed up the test
-    norm = fourier_op.operator_norm(img, dim=None, max_iterations=20)
-    torch.testing.assert_close(norm.squeeze(), torch.tensor(1.0), atol=0.1, rtol=0.0)
+    (initial_value,) = fourier_op.adjoint(*fourier_op(img))
+    norm = fourier_op.operator_norm(initial_value, dim=None, max_iterations=4).squeeze()
+    torch.testing.assert_close(norm, torch.tensor(1.0), atol=0.1, rtol=0.0)
 
 
 @COMMON_MR_TRAJECTORIES
-def test_fourier_op_fft_nufft(im_shape, k_shape, nkx, nky, nkz, type_kx, type_ky, type_kz, type_k0, type_k1, type_k2):
+def test_fourier_op_fft_nufft_forward(
+    im_shape, k_shape, nkx, nky, nkz, type_kx, type_ky, type_kz, type_k0, type_k1, type_k2
+):
     """Test Nufft vs FFT for Fourier operator."""
+    if not any(t == 'uniform' for t in [type_kx, type_ky, type_kz]):
+        return  # only test for uniform trajectories
 
-    # generate random images and k-space trajectories
     img, trajectory = create_data(im_shape, k_shape, nkx, nky, nkz, type_kx, type_ky, type_kz)
 
-    # create operator
     recon_matrix = SpatialDimension(im_shape[-3], im_shape[-2], im_shape[-1])
     encoding_matrix = SpatialDimension(
         int(trajectory.kz.max() - trajectory.kz.min() + 1),
@@ -85,35 +96,45 @@ def test_fourier_op_fft_nufft(im_shape, k_shape, nkx, nky, nkz, type_kx, type_ky
     )
     fourier_op = FourierOp(recon_matrix=recon_matrix, encoding_matrix=encoding_matrix, traj=trajectory)
 
-    class NufftTrajektory(KTrajectory):
-        """Always returns non-grid trajectory type."""
-
-        def _traj_types(
-            self,
-            tolerance: float,
-        ) -> tuple[tuple[TrajType, TrajType, TrajType], tuple[TrajType, TrajType, TrajType]]:
-            true_types = super()._traj_types(tolerance)
-            modified = tuple([tuple([t & (~TrajType.ONGRID) for t in ts]) for ts in true_types])
-            return modified
-
     nufft_fourier_op = FourierOp(
         recon_matrix=recon_matrix,
         encoding_matrix=encoding_matrix,
         traj=NufftTrajektory(trajectory.kz, trajectory.ky, trajectory.kx),
+        nufft_oversampling=8.0,
     )
 
     (result_normal,) = fourier_op(img)
     (result_nufft,) = nufft_fourier_op(img)
-    torch.testing.assert_close(result_normal, result_nufft, atol=1e-5, rtol=1e-4)
+    torch.testing.assert_close(result_normal, result_nufft, atol=1e-4, rtol=5e-3)
+
+
+@COMMON_MR_TRAJECTORIES
+def test_fourier_op_fft_nufft_adjoint(
+    im_shape, k_shape, nkx, nky, nkz, type_kx, type_ky, type_kz, type_k0, type_k1, type_k2
+):
+    """Test AdjointNufft vs IFFT for Fourier operator."""
+    if not any(t == 'uniform' for t in [type_kx, type_ky, type_kz]):
+        return  # only test for uniform trajectories
+    img, trajectory = create_data(im_shape, k_shape, nkx, nky, nkz, type_kx, type_ky, type_kz)
+    recon_matrix = SpatialDimension(im_shape[-3], im_shape[-2], im_shape[-1])
+    encoding_matrix = SpatialDimension(
+        int(trajectory.kz.max() - trajectory.kz.min() + 1),
+        int(trajectory.ky.max() - trajectory.ky.min() + 1),
+        int(trajectory.kx.max() - trajectory.kx.min() + 1),
+    )
+    fourier_op = FourierOp(recon_matrix=recon_matrix, encoding_matrix=encoding_matrix, traj=trajectory)
+
+    nufft_fourier_op = FourierOp(
+        recon_matrix=recon_matrix,
+        encoding_matrix=encoding_matrix,
+        traj=NufftTrajektory(trajectory.kz, trajectory.ky, trajectory.kx),
+        nufft_oversampling=8.0,
+    )
 
     k = RandomGenerator(0).complex64_tensor(size=k_shape)
     (result_normal,) = fourier_op.H(k)
     (result_nufft,) = nufft_fourier_op.H(k)
-    torch.testing.assert_close(result_normal, result_nufft, atol=1e-5, rtol=1e-4)
-
-    (result_normal,) = fourier_op(img)
-    (result_nufft,) = nufft_fourier_op(img)
-    torch.testing.assert_close(result_normal, result_nufft, atol=1e-5, rtol=1e-4)
+    torch.testing.assert_close(result_normal, result_nufft, atol=3e-4, rtol=5e-3)
 
 
 @COMMON_MR_TRAJECTORIES