diff --git a/monai/networks/blocks/selfattention.py b/monai/networks/blocks/selfattention.py
index 7c81c1704f..7b410b1a7c 100644
--- a/monai/networks/blocks/selfattention.py
+++ b/monai/networks/blocks/selfattention.py
@@ -32,6 +32,7 @@ def __init__(
         dropout_rate: float = 0.0,
         qkv_bias: bool = False,
         save_attn: bool = False,
+        dim_head: int | None = None,
     ) -> None:
         """
         Args:
@@ -40,6 +41,7 @@ def __init__(
             dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.
             qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False.
             save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.
+            dim_head (int, optional): dimension of each head. Defaults to hidden_size // num_heads.
 
         """
 
@@ -52,14 +54,16 @@ def __init__(
             raise ValueError("hidden size should be divisible by num_heads.")
 
         self.num_heads = num_heads
-        self.out_proj = nn.Linear(hidden_size, hidden_size)
-        self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias)
+        self.dim_head = hidden_size // num_heads if dim_head is None else dim_head
+        self.inner_dim = self.dim_head * num_heads
+
+        self.out_proj = nn.Linear(self.inner_dim, hidden_size)
+        self.qkv = nn.Linear(hidden_size, self.inner_dim * 3, bias=qkv_bias)
         self.input_rearrange = Rearrange("b h (qkv l d) -> qkv b l h d", qkv=3, l=num_heads)
         self.out_rearrange = Rearrange("b h l d -> b l (h d)")
         self.drop_output = nn.Dropout(dropout_rate)
         self.drop_weights = nn.Dropout(dropout_rate)
-        self.head_dim = hidden_size // num_heads
-        self.scale = self.head_dim**-0.5
+        self.scale = self.dim_head**-0.5
         self.save_attn = save_attn
         self.att_mat = torch.Tensor()
 
diff --git a/tests/test_selfattention.py b/tests/test_selfattention.py
index b8be4fd1b6..0ebed84159 100644
--- a/tests/test_selfattention.py
+++ b/tests/test_selfattention.py
@@ -74,6 +74,40 @@ def test_access_attn_matrix(self):
         matrix_acess_blk(torch.randn(input_shape))
         assert matrix_acess_blk.att_mat.shape == (input_shape[0], input_shape[0], input_shape[1], input_shape[1])
 
+    def test_number_of_parameters(self):
+
+        def count_sablock_params(*args, **kwargs):
+            """Count the number of parameters in a SABlock."""
+            sablock = SABlock(*args, **kwargs)
+            return sum([x.numel() for x in sablock.parameters() if x.requires_grad])
+
+        hidden_size = 128
+        num_heads = 8
+        default_dim_head = hidden_size // num_heads
+
+        # Default dim_head is hidden_size // num_heads
+        nparams_default = count_sablock_params(hidden_size=hidden_size, num_heads=num_heads)
+        nparams_like_default = count_sablock_params(
+            hidden_size=hidden_size, num_heads=num_heads, dim_head=default_dim_head
+        )
+        self.assertEqual(nparams_default, nparams_like_default)
+
+        # Increasing dim_head should increase the number of parameters
+        nparams_custom_large = count_sablock_params(
+            hidden_size=hidden_size, num_heads=num_heads, dim_head=default_dim_head * 2
+        )
+        self.assertGreater(nparams_custom_large, nparams_default)
+
+        # Decreasing dim_head should decrease the number of parameters
+        nparams_custom_small = count_sablock_params(
+            hidden_size=hidden_size, num_heads=num_heads, dim_head=default_dim_head // 2
+        )
+        self.assertGreater(nparams_default, nparams_custom_small)
+
+        # Increasing the number of heads with the default behaviour should not change the number of params.
+        nparams_default_more_heads = count_sablock_params(hidden_size=hidden_size, num_heads=num_heads * 2)
+        self.assertEqual(nparams_default, nparams_default_more_heads)
+
 
 if __name__ == "__main__":
     unittest.main()