diff --git a/direct/data/datasets.py b/direct/data/datasets.py
index 11e82aa8..bd178901 100644
--- a/direct/data/datasets.py
+++ b/direct/data/datasets.py
@@ -18,6 +18,7 @@
 from omegaconf import DictConfig
 from torch.utils.data import Dataset, IterableDataset
 
+from direct.common.subsample import centered_disk_mask
 from direct.data.fake import FakeMRIData
 from direct.data.h5_data import H5SliceData
 from direct.data.sens import simulate_sensitivity_maps
@@ -87,6 +88,51 @@ def _et_query(
     return str(value.text)
 
 
+class NKIKSpaceBreastDataset(H5SliceData):
+    def __init__(
+        self,
+        data_root: pathlib.Path,
+        transform: Optional[Callable] = None,
+        filenames_filter: Optional[list[PathOrString]] = None,
+        filenames_lists: Union[list[PathOrString], None] = None,
+        filenames_lists_root: Union[PathOrString, None] = None,
+        slice_data: Optional[tuple[int, int]] = None,
+        acs_ratio: float = 0.1,
+        **kwargs,
+    ) -> None:
+        super().__init__(
+            root=data_root,
+            filenames_filter=filenames_filter,
+            filenames_lists=filenames_lists,
+            filenames_lists_root=filenames_lists_root,
+            regex_filter=None,
+            metadata=None,
+            pass_attrs=False,
+            text_description=kwargs.get("text_description", None),
+            pass_h5s=None,
+            pass_dictionaries=kwargs.get("pass_dictionaries", None),
+            sensitivity_maps=kwargs.get("sensitivity_maps", None),
+            slice_data=slice(slice_data[0], slice_data[1]) if slice_data is not None else None,
+        )
+        self.acs_ratio = acs_ratio
+        self.transform = transform
+
+    def __getitem__(self, idx: int) -> dict[str, Any]:
+        sample = super().__getitem__(idx)
+
+        sample["sampling_mask"] = sample["kspace"].sum(0) != 0
+        sample["acs_mask"] = (
+            centered_disk_mask(sample["sampling_mask"].squeeze().shape, self.acs_ratio) * sample["sampling_mask"]
+        )
+
+        sample["sampling_mask"] = sample["sampling_mask"][None, ..., None]
+        sample["acs_mask"] = sample["acs_mask"][None, ..., None]
+        if self.transform:
+            sample = self.transform(sample)
+
+        return sample
+
+
 class FakeMRIBlobsDataset(Dataset):
     """A PyTorch Dataset class which outputs random fake k-space images which reconstruct into Gaussian blobs.