diff --git a/src/torchio/transforms/preprocessing/intensity/rescale.py b/src/torchio/transforms/preprocessing/intensity/rescale.py
index 34bcc4dd..2051f18f 100644
--- a/src/torchio/transforms/preprocessing/intensity/rescale.py
+++ b/src/torchio/transforms/preprocessing/intensity/rescale.py
@@ -1,6 +1,5 @@
 import warnings
 from typing import Optional
-from typing import Tuple
 
 import numpy as np
 import torch
@@ -48,7 +47,7 @@ def __init__(
             out_min_max: TypeRangeFloat = (0, 1),
             percentiles: TypeRangeFloat = (0, 100),
             masking_method: TypeMaskingMethod = None,
-            in_min_max: Optional[Tuple[float, float]] = None,
+            in_min_max: Optional[TypeRangeFloat] = None,
             **kwargs
     ):
         super().__init__(masking_method=masking_method, **kwargs)
@@ -60,7 +59,21 @@ def __init__(
         self.percentiles = self._parse_range(
             percentiles, 'percentiles', min_constraint=0, max_constraint=100,
         )
-        self.args_names = ['out_min_max', 'percentiles', 'masking_method']
+
+        self.in_min: Optional[float]
+        self.in_max: Optional[float]
+        if self.in_min_max is not None:
+            self.in_min, self.in_max = self._parse_range(
+                self.in_min_max, 'in_min_max',
+            )
+        else:
+            self.in_min = None
+            self.in_max = None
+
+        self.args_names = [
+            'out_min_max', 'percentiles', 'masking_method', 'in_min_max',
+        ]
+        self.invert_transform = False
 
     def apply_normalization(
             self,
@@ -91,10 +104,13 @@ def rescale(
         cutoff = np.percentile(values, self.percentiles)
         np.clip(array, *cutoff, out=array)  # type: ignore[call-overload]
         if self.in_min_max is None:
-            in_min, in_max = array.min(), array.max()
-        else:
-            in_min, in_max = self.in_min_max
-        in_range = in_max - in_min
+            self.in_min_max = self._parse_range(
+                (array.min(), array.max()), 'in_min_max',
+            )
+            self.in_min, self.in_max = self.in_min_max
+        assert self.in_min is not None
+        assert self.in_max is not None
+        in_range = self.in_max - self.in_min
         if in_range == 0:  # should this be compared using a tolerance?
             message = (
                 f'Rescaling image "{image_name}" not possible'
@@ -102,9 +118,15 @@ def rescale(
             )
             warnings.warn(message, RuntimeWarning, stacklevel=2)
             return tensor
-        array -= in_min
-        array /= in_range
         out_range = self.out_max - self.out_min
-        array *= out_range
-        array += self.out_min
+        if self.invert_transform:
+            array -= self.out_min
+            array /= out_range
+            array *= in_range
+            array += self.in_min
+        else:
+            array -= self.in_min
+            array /= in_range
+            array *= out_range
+            array += self.out_min
         return torch.as_tensor(array)
diff --git a/tests/transforms/preprocessing/test_rescale.py b/tests/transforms/preprocessing/test_rescale.py
index 1b2f01e6..4053bebb 100644
--- a/tests/transforms/preprocessing/test_rescale.py
+++ b/tests/transforms/preprocessing/test_rescale.py
@@ -107,3 +107,14 @@ def test_empty_mask(self):
         rescale = tio.RescaleIntensity(masking_method='label')
         with pytest.warns(RuntimeWarning):
             rescale(subject)
+
+    def test_invert_rescaling(self):
+        torch.manual_seed(0)
+        transform = tio.RescaleIntensity(out_min_max=(0, 1))
+        data = torch.rand(1, 2, 3, 4).double()
+        subject = tio.Subject(t1=tio.ScalarImage(tensor=data))
+        transformed = transform(subject)
+        assert transformed.t1.data.min() == 0
+        assert transformed.t1.data.max() == 1
+        inverted = transformed.apply_inverse_transform()
+        self.assert_tensor_almost_equal(inverted.t1.data, data)