diff --git a/src/deepali/core/flow.py b/src/deepali/core/flow.py
index 9af12f4..cea7054 100644
--- a/src/deepali/core/flow.py
+++ b/src/deepali/core/flow.py
@@ -357,6 +357,7 @@ def jacobian_det(
     sigma: Optional[float] = None,
     spacing: Optional[Union[Scalar, Array]] = None,
     stride: Optional[ScalarOrTuple[int]] = None,
+    add_identity: bool = True,
 ) -> Tensor:
     r"""Evaluate Jacobian determinant of spatial deformation.
 
@@ -366,6 +367,9 @@ def jacobian_det(
         sigma: Standard deviation of Gaussian used for computing spatial derivatives.
         spacing: Physical size of image voxels used to compute spatial derivatives.
         stride: Number of output grid points between control points plus one for ``mode='bspline'``.
+        add_identity: Whether to calculate derivatives of :math:`u(x)` (False) or the spatial
+            deformation given by :math:`x + u(x)` (True), where :math:`u` is the flow field,
+            by adding the identity matrix to the Jacobian of :math:`u`.
 
     Returns:
         Scalar field of Jacobian determinant values as tensor of shape ``(N, 1, ..., X)``.
@@ -380,8 +384,9 @@ def jacobian_det(
     which = FlowDerivativeKeys.jacobian(spatial_dims=D)
     deriv = flow_derivatives(flow, which=which, **kwargs)
     # Add 1 to diagonal elements of Jacobian matrix, because T(x) = x + u(x)
-    for i in range(D):
-        deriv[FlowDerivativeKeys.symbol(i, i)].add_(1)
+    if add_identity:
+        for i in range(D):
+            deriv[FlowDerivativeKeys.symbol(i, i)].add_(1)
     if D == 2:
         a = deriv["du/dx"]
         b = deriv["du/dy"]
@@ -452,12 +457,13 @@ def jacobian_dict(
     kwargs = dict(mode=mode, sigma=sigma, spacing=spacing, stride=stride)
     which = FlowDerivativeKeys.jacobian(spatial_dims=D)
     deriv = flow_derivatives(flow, which=which, **kwargs)
+    # Optionally, add 1 to diagonal elements of Jacobian matrix, because T(x) = x + u(x).
+    if add_identity:
+        for i in range(D):
+            deriv[FlowDerivativeKeys.symbol(i, i)].add_(1)
     jac = {}
     for i, j in combinations_with_replacement(range(D), 2):
-        dij = deriv[FlowDerivativeKeys.symbol(i, j)]
-        if add_identity and i == j:
-            dij = dij.add_(1)  # T(x) = x + u(x)
-        jac[(i, j)] = dij
+        jac[(i, j)] = deriv[FlowDerivativeKeys.symbol(i, j)]
     return {(i, j): jac[(i, j) if i < j else (j, i)] for i, j in product(range(D), repeat=2)}