3881 using sform/qform code 1 for the writers (#3882)

Fixes #3881 
Follow-up of Project-MONAI/tutorials#481

### Description
following
https://github.com/InsightSoftwareConsortium/ITK/blob/15aa8d58a01a1279e1fafa2214c82edd80b17e92/Modules/IO/NIFTI/src/itkNiftiImageIO.cxx#L2174-L2176

### Status
**Ready**

### Types of changes
<!--- Put an `x` in all the boxes that apply, and remove the not
applicable items -->
- [x] Non-breaking change (fix or new feature that would not break
existing functionality).
- [ ] Breaking change (fix or new feature that would cause existing
functionality to change).
- [x] New tests added to cover the changes.
- [ ] Integration tests passed locally by running `./runtests.sh -f -u
--net --coverage`.
- [x] Quick tests passed locally by running `./runtests.sh --quick
--unittests --disttests`.
- [x] In-line docstrings updated.
- [ ] Documentation updated, tested `make html` command in the `docs/`
folder.

Signed-off-by: Wenqi Li <wenqil@nvidia.com>

Author: wyli

monai/data/image_writer.py

@@ -601,6 +601,12 @@ def write(self, filename: PathLike, verbose: bool = False, **obj_kwargs):
         self.data_obj = self.create_backend_obj(
             self.data_obj, affine=self.affine, dtype=self.output_dtype, **obj_kwargs  # type: ignore
         )
+        if self.affine is None:
+            self.affine = np.eye(4)
+        # ITK v5.2.1/Modules/IO/NIFTI/src/itkNiftiImageIO.cxx#L2175-L2176
+        _affine = to_affine_nd(r=3, affine=convert_data_type(self.affine, np.ndarray)[0])
+        self.data_obj.set_sform(_affine, code=1)
+        self.data_obj.set_qform(_affine, code=1)
         nib.save(self.data_obj, filename)
 
     @classmethod

tests/test_image_rw.py

@@ -65,6 +65,8 @@ def nifti_rw(self, test_data, reader, writer, dtype, resample=True):
                 _test_data = test_data[0]
             if resample:
                 _test_data = moveaxis(_test_data, 0, 1)
+            assert_allclose(meta["qform_code"], 1, type_test=False)
+            assert_allclose(meta["sform_code"], 1, type_test=False)
             assert_allclose(data, torch.as_tensor(_test_data))
 
     @parameterized.expand(itertools.product([NibabelReader, ITKReader], [NibabelWriter, "ITKWriter"]))

tests/testing_data/integration_answers.py

@@ -433,6 +433,64 @@
             "infer_metric": 0.9326590299606323,
         },
     },
+    {  # test answers for PyTorch 1.13
+        "integration_workflows": {
+            "output_sums_2": [
+                0.14264830205979873,
+                0.15264129328718357,
+                0.1519652511118344,
+                0.14003114557361543,
+                0.18870416611118465,
+                0.1699260498246968,
+                0.14727475398203582,
+                0.16870874483246967,
+                0.15757932277023196,
+                0.1797779694564011,
+                0.16310501082450635,
+                0.16850569170136015,
+                0.14472958359864832,
+                0.11402527744419455,
+                0.16217657428257873,
+                0.20135486560244975,
+                0.17627557567092866,
+                0.09802074024435596,
+                0.19418729084978026,
+                0.20339278025379662,
+                0.1966174446916041,
+                0.20872528599049203,
+                0.16246183433492764,
+                0.1323750751202327,
+                0.14830347036335728,
+                0.14300732028781024,
+                0.23163101813922762,
+                0.1612925258625139,
+                0.1489573676973957,
+                0.10299491921717041,
+                0.11921404797064328,
+                0.1300212751422368,
+                0.11437829790254125,
+                0.1524755276727056,
+                0.16350584736767904,
+                0.19424317961257148,
+                0.2229762916892286,
+                0.18121074825540173,
+                0.19064286213535897,
+                0.0747544243069024,
+            ]
+        },
+        "integration_segmentation_3d": {  # for the mixed readers
+            "losses": [
+                0.5451162219047546,
+                0.4709601759910583,
+                0.45201429128646853,
+                0.4443251401185989,
+                0.4341257899999619,
+                0.4350819975137711,
+            ],
+            "best_metric": 0.9316844940185547,
+            "infer_metric": 0.9316383600234985,
+        },
+    },
     {  # test answers for PyTorch 21.10
         "integration_classification_2d": {
             "losses": [0.7806222991199251, 0.16259610306495315, 0.07529311385124353, 0.04640352608529246],