diff --git a/src/mrpro/operators/FastFourierOp.py b/src/mrpro/operators/FastFourierOp.py
index 92447e46..bfd1dd8d 100644
--- a/src/mrpro/operators/FastFourierOp.py
+++ b/src/mrpro/operators/FastFourierOp.py
@@ -153,6 +153,7 @@ def adjoint(self, y: torch.Tensor) -> tuple[torch.Tensor,]:
     def __repr__(self) -> str:
         """Representation method for FastFourierOperator."""
         k2k1k0 = ['k2', 'k1', 'k0']
-        dimension_210 = [k2k1k0[i] for i in self._dim if i in range(-3, 0)]
-        out = f'Dimension(s) along which FFT is applied: {list(dimension_210)!s}'
+        dimension_210 = tuple(k2k1k0[i] for i in self._dim if i in range(-3, 0))
+        dimension_210_str = ', '.join(dimension_210)
+        out = f'Dimension(s) along which FFT is applied: ({dimension_210_str})'
         return out
diff --git a/src/mrpro/operators/NonUniformFastFourierOp.py b/src/mrpro/operators/NonUniformFastFourierOp.py
index d8609a55..80eefcc4 100644
--- a/src/mrpro/operators/NonUniformFastFourierOp.py
+++ b/src/mrpro/operators/NonUniformFastFourierOp.py
@@ -271,13 +271,15 @@ def __repr__(self) -> str:
         device = self._omega.device if self._omega is not None else 'none'
         zyx = ['z', 'y', 'x']
         k2k1k0 = ['k2', 'k1', 'k0']
-        direction_zyx = [zyx[i] for i in self._direction_zyx if i in range(-3, 0)]
-        dimension_210 = [k2k1k0[i] for i in self._dimension_210 if i in range(-3, 0)]
-
+        direction_zyx = tuple(zyx[i] for i in self._direction_zyx if i in range(-3, 0))
+        dimension_210 = tuple(k2k1k0[i] for i in self._dimension_210 if i in range(-3, 0))
+        recon_size_str = ', '.join(f'{dim}={size}' for dim, size in zip(direction_zyx, self._im_size, strict=False))
+        direction_zyx_str = ', '.join(direction_zyx)
+        dimension_210_str = ', '.join(dimension_210)
         out = (
             f'{type(self).__name__} on device: {device}\n'
-            f'Dimension(s) along which NUFFT is applied: {list(direction_zyx)!s} / {dimension_210}\n'
-            f'Reconstructed image size in dimension {direction_zyx}: {self._im_size}'
+            f'Dimension(s) along which NUFFT is applied: ({direction_zyx_str}) / ({dimension_210_str})\n'
+            f'Reconstructed image size {recon_size_str}'
         )
         return out