diff --git a/src/mrpro/operators/models/MOLLI.py b/src/mrpro/operators/models/MOLLI.py
index 2f04e771..b85ffe94 100644
--- a/src/mrpro/operators/models/MOLLI.py
+++ b/src/mrpro/operators/models/MOLLI.py
@@ -6,7 +6,18 @@
 
 
 class MOLLI(SignalModel[torch.Tensor, torch.Tensor, torch.Tensor]):
-    """Signal model for Modified Look-Locker inversion recovery (MOLLI)."""
+    """Signal model for Modified Look-Locker inversion recovery (MOLLI).
+
+    This model describes
+    :math:`M_z(t) = a(1 - c)e^{(-t / T1^*)}` with :math:`T1^* = T1 / (c - 1)`.
+
+    This is a small modification from the original MOLLI signal model [MESS2004]_:
+    :math:`M_z(t) = a - be^{(-t / T1^*)}` with :math:`T1^* = T1 / (b/a - 1)`.
+
+    .. [MESS2004] Messroghli DR, Radjenovic A, Kozerke S, Higgins DM, Sivananthan MU, Ridgway JP (2004) Modified
+      look-locker inversion recovery (MOLLI) for high-resolution T 1 mapping of the heart. MRM, 52(1).
+      https://doi.org/10.1002/mrm.20110
+    """
 
     def __init__(self, ti: float | torch.Tensor):
         """Initialize MOLLI signal model for T1 mapping.
@@ -21,7 +32,7 @@ def __init__(self, ti: float | torch.Tensor):
         ti = torch.as_tensor(ti)
         self.ti = torch.nn.Parameter(ti, requires_grad=ti.requires_grad)
 
-    def forward(self, a: torch.Tensor, b: torch.Tensor, t1: torch.Tensor) -> tuple[torch.Tensor,]:
+    def forward(self, a: torch.Tensor, c: torch.Tensor, t1: torch.Tensor) -> tuple[torch.Tensor,]:
         """Apply MOLLI signal model.
 
         Parameters
@@ -29,8 +40,8 @@ def forward(self, a: torch.Tensor, b: torch.Tensor, t1: torch.Tensor) -> tuple[t
         a
             parameter a in MOLLI signal model
             with shape (... other, coils, z, y, x)
-        b
-            parameter b in MOLLI signal model
+        c
+            parameter c = b/a in MOLLI signal model
             with shape (... other, coils, z, y, x)
         t1
             longitudinal relaxation time T1
@@ -41,7 +52,5 @@ def forward(self, a: torch.Tensor, b: torch.Tensor, t1: torch.Tensor) -> tuple[t
             signal with shape (time ... other, coils, z, y, x)
         """
         ti = self.expand_tensor_dim(self.ti, a.ndim - (self.ti.ndim - 1))  # -1 for time
-        c = b / torch.where(a == 0, 1e-10, a)
-        t1 = torch.where(t1 == 0, t1 + 1e-10, t1)
         signal = a * (1 - c * torch.exp(ti / t1 * (1 - c)))
         return (signal,)
diff --git a/tests/operators/models/test_molli.py b/tests/operators/models/test_molli.py
index 10f55281..8ee5f911 100644
--- a/tests/operators/models/test_molli.py
+++ b/tests/operators/models/test_molli.py
@@ -3,13 +3,14 @@
 import pytest
 import torch
 from mrpro.operators.models import MOLLI
+from tests import RandomGenerator
 from tests.operators.models.conftest import SHAPE_VARIATIONS_SIGNAL_MODELS, create_parameter_tensor_tuples
 
 
 @pytest.mark.parametrize(
     ('ti', 'result'),
     [
-        (0, 'a-b'),  # short ti
+        (0, 'a(1-c)'),  # short ti
         (20, 'a'),  # long ti
     ],
 )
@@ -17,21 +18,20 @@ def test_molli(ti, result):
     """Test for MOLLI.
 
     Checking that idata output tensor at ti=0 is close to a. Checking
-    that idata output tensor at large ti is close to a-b.
+    that idata output tensor at large ti is close to a(1-c).
     """
-    # Generate qdata tensor, not random as a<b is necessary for t1_star to be >= 0
-    other, coils, z, y, x = 10, 5, 100, 100, 100
-    a = torch.ones((other, coils, z, y, x)) * 2
-    b = torch.ones((other, coils, z, y, x)) * 5
-    t1 = torch.ones((other, coils, z, y, x)) * 2
+    a, t1 = create_parameter_tensor_tuples()
+    # c>2 is necessary for t1_star to be >= 0 and to ensure t1_star < t1
+    random_generator = RandomGenerator(seed=0)
+    c = random_generator.float32_tensor(size=a.shape, low=2.0, high=4.0)
 
     # Generate signal model and torch tensor for comparison
     model = MOLLI(ti)
-    (image,) = model.forward(a, b, t1)
+    (image,) = model.forward(a, c, t1)
 
-    # Assert closeness to a-b for large ti
-    if result == 'a-b':
-        torch.testing.assert_close(image[0, ...], a - b)
+    # Assert closeness to a(1-c) for large ti
+    if result == 'a(1-c)':
+        torch.testing.assert_close(image[0, ...], a * (1 - c))
     # Assert closeness to a for ti=0
     elif result == 'a':
         torch.testing.assert_close(image[0, ...], a)
@@ -42,6 +42,6 @@ def test_molli_shape(parameter_shape, contrast_dim_shape, signal_shape):
     """Test correct signal shapes."""
     (ti,) = create_parameter_tensor_tuples(contrast_dim_shape, number_of_tensors=1)
     model_op = MOLLI(ti)
-    a, b, t1 = create_parameter_tensor_tuples(parameter_shape, number_of_tensors=3)
-    (signal,) = model_op.forward(a, b, t1)
+    a, c, t1 = create_parameter_tensor_tuples(parameter_shape, number_of_tensors=3)
+    (signal,) = model_op.forward(a, c, t1)
     assert signal.shape == signal_shape