Repository: Project-MONAI/MONAI

Issue:
Resampling / Spacing enhancements
https://docs.monai.io/en/stable/transforms.html?highlight=spacing#spacing

Current transform to resample to the desired resolution always resamples  even if the data is already at (almost) the desired resolution.  Since the  augmentation transforms (zoom, rotation, etc) will resample the data again, it would be advantageous to skip the Spacing transform if the current image resolution is already "close" to the desired resolution.  

E.g if the desired resolution is 0.8x0.8x2.5, and the image resolution is 0.7x0.7x.2,5 then skip "Spacing".  Perhaps we can have a parameter to enable it.   This will save time during training, and won't produce interpolation artifacts when it's not necessary. 



Resampling / Spacing enhancements
https://docs.monai.io/en/stable/transforms.html?highlight=spacing#spacing

Current transform to resample to the desired resolution always resamples  even if the data is already at (almost) the desired resolution.  Since the  augmentation transforms (zoom, rotation, etc) will resample the data again, it would be advantageous to skip the Spacing transform if the current image resolution is already "close" to the desired resolution.  

E.g if the desired resolution is 0.8x0.8x2.5, and the image resolution is 0.7x0.7x.2,5 then skip "Spacing".  Perhaps we can have a parameter to enable it.   This will save time during training, and won't produce interpolation artifacts when it's not necessary. 





Discussion:
Hi @myron ,

Thanks for your feedback.
Actually we already have the tolerance to compare affine when resampling:
https://github.com/Project-MONAI/MONAI/blob/dev/monai/data/utils.py#L93
I can create a PR to make this value as an arg for resampling transforms, but I am not very sure whether it's necessary.
Maybe current `1e-3` is already good enough?
@ericspod @wyli @holgerroth @dongyang0122 Could you please help share some opinions before I change something?

Thanks.
 
I think we could have a meta transform to achieve 'SkipIf' or 'SkipUnless' to make the transform conditional. Or have another way to archive this. We should think about it. If it's just a performance issue maybe we should focus on the lazy resampling.

(anyway the tolerance was not designed for this purpose. Please don't revise that API for this feature request.)
I'd recommend to  add an option for a  range of resolutions e.g min_bound=[0.7 0.7 2], max_bound=[0.8 0.8 3.5], in which case the resampling will be skipped if the resolution is already in that range
Hi @myron ,

Thanks for your feedback.
Actually we already have the tolerance to compare affine when resampling:
https://github.com/Project-MONAI/MONAI/blob/dev/monai/data/utils.py#L93
I can create a PR to make this value as an arg for resampling transforms, but I am not very sure whether it's necessary.
Maybe current `1e-3` is already good enough?
@ericspod @wyli @holgerroth @dongyang0122 Could you please help share some opinions before I change something?

Thanks.
 
I think we could have a meta transform to achieve 'SkipIf' or 'SkipUnless' to make the transform conditional. Or have another way to archive this. We should think about it. If it's just a performance issue maybe we should focus on the lazy resampling.

(anyway the tolerance was not designed for this purpose. Please don't revise that API for this feature request.)
I'd recommend to  add an option for a  range of resolutions e.g min_bound=[0.7 0.7 2], max_bound=[0.8 0.8 3.5], in which case the resampling will be skipped if the resolution is already in that range

Resolving patch:
diff --git a/monai/transforms/spatial/array.py b/monai/transforms/spatial/array.py
--- a/monai/transforms/spatial/array.py
+++ b/monai/transforms/spatial/array.py
@@ -15,6 +15,7 @@
 import warnings
 from copy import deepcopy
 from enum import Enum
+from itertools import zip_longest
 from typing import Any, Callable, Dict, List, Optional, Sequence, Tuple, Union
 
 import numpy as np
@@ -24,7 +25,7 @@
 from monai.config.type_definitions import NdarrayOrTensor
 from monai.data.meta_obj import get_track_meta
 from monai.data.meta_tensor import MetaTensor
-from monai.data.utils import AFFINE_TOL, compute_shape_offset, iter_patch, to_affine_nd, zoom_affine
+from monai.data.utils import AFFINE_TOL, affine_to_spacing, compute_shape_offset, iter_patch, to_affine_nd, zoom_affine
 from monai.networks.layers import AffineTransform, GaussianFilter, grid_pull
 from monai.networks.utils import meshgrid_ij, normalize_transform
 from monai.transforms.croppad.array import CenterSpatialCrop, ResizeWithPadOrCrop
@@ -437,6 +438,8 @@ def __init__(
         dtype: DtypeLike = np.float64,
         scale_extent: bool = False,
         recompute_affine: bool = False,
+        min_pixdim: Union[Sequence[float], float, np.ndarray, None] = None,
+        max_pixdim: Union[Sequence[float], float, np.ndarray, None] = None,
         image_only: bool = False,
     ) -> None:
         """
@@ -483,13 +486,25 @@ def __init__(
             recompute_affine: whether to recompute affine based on the output shape. The affine computed
                 analytically does not reflect the potential quantization errors in terms of the output shape.
                 Set this flag to True to recompute the output affine based on the actual pixdim. Default to ``False``.
+            min_pixdim: minimal input spacing to be resampled. If provided, input image with a larger spacing than this
+                value will be kept in its original spacing (not be resampled to `pixdim`). Set it to `None` to use the
+                value of `pixdim`. Default to `None`.
+            max_pixdim: maximal input spacing to be resampled. If provided, input image with a smaller spacing than this
+                value will be kept in its original spacing (not be resampled to `pixdim`). Set it to `None` to use the
+                value of `pixdim`. Default to `None`.
 
         """
         self.pixdim = np.array(ensure_tuple(pixdim), dtype=np.float64)
+        self.min_pixdim = np.array(ensure_tuple(min_pixdim), dtype=np.float64)
+        self.max_pixdim = np.array(ensure_tuple(max_pixdim), dtype=np.float64)
         self.diagonal = diagonal
         self.scale_extent = scale_extent
         self.recompute_affine = recompute_affine
 
+        for mn, mx in zip(self.min_pixdim, self.max_pixdim):
+            if (not np.isnan(mn)) and (not np.isnan(mx)) and ((mx < mn) or (mn < 0)):
+                raise ValueError(f"min_pixdim {self.min_pixdim} must be positive, smaller than max {self.max_pixdim}.")
+
         self.sp_resample = SpatialResample(
             mode=mode, padding_mode=padding_mode, align_corners=align_corners, dtype=dtype
         )
@@ -560,6 +575,16 @@ def __call__(
         out_d = self.pixdim[:sr]
         if out_d.size < sr:
             out_d = np.append(out_d, [1.0] * (sr - out_d.size))
+        orig_d = affine_to_spacing(affine_, sr, out_d.dtype)
+        for idx, (_d, mn, mx) in enumerate(
+            zip_longest(orig_d, self.min_pixdim[:sr], self.max_pixdim[:sr], fillvalue=np.nan)
+        ):
+            target = out_d[idx]
+            mn = target if np.isnan(mn) else min(mn, target)
+            mx = target if np.isnan(mx) else max(mx, target)
+            if mn > mx:
+                raise ValueError(f"min_pixdim is larger than max_pixdim at dim {idx}: min {mn} max {mx} out {target}.")
+            out_d[idx] = _d if (mn - AFFINE_TOL) <= _d <= (mx + AFFINE_TOL) else target
 
         if not align_corners and scale_extent:
             warnings.warn("align_corners=False is not compatible with scale_extent=True.")
diff --git a/monai/transforms/spatial/dictionary.py b/monai/transforms/spatial/dictionary.py
--- a/monai/transforms/spatial/dictionary.py
+++ b/monai/transforms/spatial/dictionary.py
@@ -335,6 +335,8 @@ def __init__(
         recompute_affine: bool = False,
         meta_keys: Optional[KeysCollection] = None,
         meta_key_postfix: str = "meta_dict",
+        min_pixdim: Union[Sequence[float], float, None] = None,
+        max_pixdim: Union[Sequence[float], float, None] = None,
         allow_missing_keys: bool = False,
     ) -> None:
         """
@@ -386,11 +388,19 @@ def __init__(
             recompute_affine: whether to recompute affine based on the output shape. The affine computed
                 analytically does not reflect the potential quantization errors in terms of the output shape.
                 Set this flag to True to recompute the output affine based on the actual pixdim. Default to ``False``.
+            min_pixdim: minimal input spacing to be resampled. If provided, input image with a larger spacing than this
+                value will be kept in its original spacing (not be resampled to `pixdim`). Set it to `None` to use the
+                value of `pixdim`. Default to `None`.
+            max_pixdim: maximal input spacing to be resampled. If provided, input image with a smaller spacing than this
+                value will be kept in its original spacing (not be resampled to `pixdim`). Set it to `None` to use the
+                value of `pixdim`. Default to `None`.
             allow_missing_keys: don't raise exception if key is missing.
 
         """
         super().__init__(keys, allow_missing_keys)
-        self.spacing_transform = Spacing(pixdim, diagonal=diagonal, recompute_affine=recompute_affine)
+        self.spacing_transform = Spacing(
+            pixdim, diagonal=diagonal, recompute_affine=recompute_affine, min_pixdim=min_pixdim, max_pixdim=max_pixdim
+        )
         self.mode = ensure_tuple_rep(mode, len(self.keys))
         self.padding_mode = ensure_tuple_rep(padding_mode, len(self.keys))
         self.align_corners = ensure_tuple_rep(align_corners, len(self.keys))


Test patch:
diff --git a/tests/test_spacing.py b/tests/test_spacing.py
--- a/tests/test_spacing.py
+++ b/tests/test_spacing.py
@@ -19,7 +19,7 @@
 from monai.data.meta_tensor import MetaTensor
 from monai.data.utils import affine_to_spacing
 from monai.transforms import Spacing
-from monai.utils import ensure_tuple, fall_back_tuple
+from monai.utils import fall_back_tuple
 from tests.utils import TEST_DEVICES, TEST_NDARRAYS_ALL, assert_allclose
 
 TESTS = []
@@ -245,7 +245,6 @@ def test_spacing(self, init_param, img, affine, data_param, expected_output, dev
         sr = min(len(res.shape) - 1, 3)
         if isinstance(init_param["pixdim"], float):
             init_param["pixdim"] = [init_param["pixdim"]] * sr
-        init_pixdim = ensure_tuple(init_param["pixdim"])
         init_pixdim = init_param["pixdim"][:sr]
         norm = affine_to_spacing(res.affine, sr).cpu().numpy()
         assert_allclose(fall_back_tuple(init_pixdim, norm), norm, type_test=False)
@@ -287,6 +286,33 @@ def test_inverse(self, device, recompute, align, scale_extent):
         l2_norm_affine = ((affine - img.affine) ** 2).sum() ** 0.5
         self.assertLess(l2_norm_affine, 5e-2)
 
+    @parameterized.expand(TEST_INVERSE)
+    def test_inverse_mn_mx(self, device, recompute, align, scale_extent):
+        img_t = torch.rand((1, 10, 9, 8), dtype=torch.float32, device=device)
+        affine = torch.tensor(
+            [[0, 0, -1, 0], [1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 1]], dtype=torch.float32, device="cpu"
+        )
+        img = MetaTensor(img_t, affine=affine, meta={"fname": "somewhere"})
+        choices = [(None, None), [1.2, None], [None, 0.7], [0.7, 0.9]]
+        idx = np.random.choice(range(len(choices)), size=1)[0]
+        tr = Spacing(
+            pixdim=[1.1, 1.2, 0.9],
+            recompute_affine=recompute,
+            align_corners=align,
+            scale_extent=scale_extent,
+            min_pixdim=[0.9, None, choices[idx][0]],
+            max_pixdim=[1.1, 1.1, choices[idx][1]],
+        )
+        img_out = tr(img)
+        if isinstance(img_out, MetaTensor):
+            assert_allclose(
+                img_out.pixdim, [1.0, 1.125, 0.888889] if recompute else [1.0, 1.2, 0.9], type_test=False, rtol=1e-4
+            )
+        img_out = tr.inverse(img_out)
+        self.assertEqual(img_out.applied_operations, [])
+        self.assertEqual(img_out.shape, img_t.shape)
+        self.assertLess(((affine - img_out.affine) ** 2).sum() ** 0.5, 5e-2)
+
 
 if __name__ == "__main__":
     unittest.main()