Merge branch 'main' into cd_pipeline

src/mrpro/algorithms/csm/__init__.py

@@ -1,2 +1,2 @@
-from mrpro.algorithms.csm.iterative_walsh import iterative_walsh
+from mrpro.algorithms.csm.walsh import walsh
 from mrpro.algorithms.csm.inati import inati

src/mrpro/algorithms/csm/iterative_walsh.py → src/mrpro/algorithms/csm/walsh.py

@@ -1,16 +1,12 @@
-"""Iterative Walsh method for coil sensitivity map calculation."""
+"""(Iterative) Walsh method for coil sensitivity map calculation."""
 
 import torch
 
 from mrpro.data.SpatialDimension import SpatialDimension
 from mrpro.utils.filters import uniform_filter
 
 
-def iterative_walsh(
-    coil_images: torch.Tensor,
-    smoothing_width: SpatialDimension[int] | int,
-    power_iterations: int,
-) -> torch.Tensor:
+def walsh(coil_images: torch.Tensor, smoothing_width: SpatialDimension[int] | int) -> torch.Tensor:
     """Calculate a coil sensitivity map (csm) using an iterative version of the Walsh method.
 
     This is for a single set of coil images. The input should be a tensor with dimensions
@@ -26,13 +22,14 @@ def iterative_walsh(
         images for each coil element
     smoothing_width
         width of the smoothing filter
-    power_iterations
-        number of iterations used to determine dominant eigenvector
 
     References
     ----------
     .. [WAL2000] Walsh DO, Gmitro AF, Marcellin MW (2000) Adaptive reconstruction of phased array MR imagery. MRM 43
     """
+    # After 10 power iterations we will have a very good estimate of the singular vector
+    n_power_iterations = 10
+
     if isinstance(smoothing_width, int):
         smoothing_width = SpatialDimension(smoothing_width, smoothing_width, smoothing_width)
     # Compute the pointwise covariance between coils
@@ -44,7 +41,7 @@ def iterative_walsh(
     # At each point in the image, find the dominant eigenvector
     # of the signal covariance matrix using the power method
     v = coil_covariance.sum(dim=0)
-    for _ in range(power_iterations):
+    for _ in range(n_power_iterations):
         v /= v.norm(dim=0)
         v = torch.einsum('abzyx,bzyx->azyx', coil_covariance, v)
     csm = v / v.norm(dim=0)

src/mrpro/data/CsmData.py

@@ -22,7 +22,6 @@ def from_idata_walsh(
         cls,
         idata: IData,
         smoothing_width: int | SpatialDimension[int] = 5,
-        power_iterations: int = 3,
         chunk_size_otherdim: int | None = None,
     ) -> Self:
         """Create csm object from image data using iterative Walsh method.
@@ -33,20 +32,18 @@ def from_idata_walsh(
             IData object containing the images for each coil element.
         smoothing_width
             width of smoothing filter.
-        power_iterations
-            number of iterations used to determine dominant eigenvector
         chunk_size_otherdim:
             How many elements of the other dimensions should be processed at once.
             Default is None, which means that all elements are processed at once.
         """
-        from mrpro.algorithms.csm.iterative_walsh import iterative_walsh
+        from mrpro.algorithms.csm.walsh import walsh
 
         # convert smoothing_width to SpatialDimension if int
         if isinstance(smoothing_width, int):
             smoothing_width = SpatialDimension(smoothing_width, smoothing_width, smoothing_width)
 
         csm_fun = torch.vmap(
-            lambda img: iterative_walsh(img, smoothing_width, power_iterations),
+            lambda img: walsh(img, smoothing_width),
             chunk_size=chunk_size_otherdim,
         )
         csm_tensor = csm_fun(idata.data)

tests/algorithms/csm/test_iterative_walsh.py → tests/algorithms/csm/test_walsh.py

@@ -1,20 +1,20 @@
 """Tests the iterative Walsh algorithm."""
 
 import torch
-from mrpro.algorithms.csm import iterative_walsh
+from mrpro.algorithms.csm import walsh
 from mrpro.data import SpatialDimension
 from tests.algorithms.csm.conftest import multi_coil_image
 from tests.helper import relative_image_difference
 
 
-def test_iterative_Walsh(ellipse_phantom, random_kheader):
-    """Test the iterative Walsh method."""
+def test_walsh(ellipse_phantom, random_kheader):
+    """Test the Walsh method."""
     idata, csm_ref = multi_coil_image(n_coils=4, ph_ellipse=ellipse_phantom, random_kheader=random_kheader)
 
     # Estimate coil sensitivity maps.
-    # iterative_walsh should be applied for each other dimension separately
+    # walsh should be applied for each other dimension separately
     smoothing_width = SpatialDimension(z=1, y=5, x=5)
-    csm = iterative_walsh(idata.data[0, ...], smoothing_width, power_iterations=3)
+    csm = walsh(idata.data[0, ...], smoothing_width)
 
     # Phase is only relative in csm calculation, therefore only the abs values are compared.
     assert relative_image_difference(torch.abs(csm), torch.abs(csm_ref[0, ...])) <= 0.01

tests/data/test_csm_data.py

@@ -6,7 +6,7 @@
 import torch
 from mrpro.data import CsmData, SpatialDimension
 
-from tests.algorithms.csm.test_iterative_walsh import multi_coil_image
+from tests.algorithms.csm.test_walsh import multi_coil_image
 from tests.helper import relative_image_difference