Update docs

src/deepmr/io/header/__init__.py

@@ -45,53 +45,59 @@ def read_acquisition_header(filepath, *args, device="cpu", verbose=False, **kwar
         
     Notes
     -----
-    The returned 'head' is a structure with the following fields:
+    The returned 'head' (deepmr.io.types.Header) is a structure with the following fields:
     
-        * **shape** (torch.Tensor):
+        * shape (torch.Tensor):
             This is the expected image size of shape (nz, ny, nx).
-        * **t** (torch.Tensor): 
+        * t (torch.Tensor): 
             This is the readout sampling time (0, t_read) in ms.
             with shape (nsamples,).
-        * **traj** (torch.Tensor): 
+        * traj (torch.Tensor): 
             This is the k-space trajectory normalized as (-0.5, 0.5) 
             with shape (ncontrasts, nviews, nsamples, ndims).
-        * **dcf** (torch.Tensor): 
+        * dcf (torch.Tensor): 
             This is the k-space sampling density compensation factor
             with shape (ncontrasts, nviews, nsamples).
-        * **FA** (torch.Tensor, float): 
+        * FA (torch.Tensor, float): 
             This is either the acquisition flip angle in degrees or the list
             of flip angles of shape (ncontrasts,) for each image in the series.
-        * **TR** (torch.Tensor, float): 
+        * TR (torch.Tensor, float): 
             This is either the repetition time in ms or the list
             of repetition times of shape (ncontrasts,) for each image in the series.
-        * **TE** (torch.Tensor, float): 
+        * TE (torch.Tensor, float): 
             This is either the echo time in ms or the list
             of echo times of shape (ncontrasts,) for each image in the series.
-        * **TI** (torch.Tensor, float): 
+        * TI (torch.Tensor, float): 
             This is either the inversion time in ms or the list
             of inversion times of shape (ncontrasts,) for each image in the series.
-        * **user** (dict):
+        * user (dict):
             User parameters. Common parameters are:
-                * ordering (torch.Tensor): indices for reordering (acquisition to reconstruction)
+                * ordering (torch.Tensor): 
+                    Indices for reordering (acquisition to reconstruction)
                     of acquired k-space data, shaped (3, nslices * ncontrasts * nview), whose rows are
                     'contrast_index', 'slice_index' and 'view_index', respectively.
                 * mode (str): Acquisition mode ('2Dcart', '3Dcart', '2Dnoncart', '3Dnoncart').
-                * separable (bool): Whether the acquisition can be decoupled by fft along slice / readout directions
+                * separable (bool): 
+                    Whether the acquisition can be decoupled by fft along slice / readout directions
                     (3D stack-of-noncartesian / 3D cartesian, respectively) or not (3D noncartesian and 2D acquisitions).
-                * slice_profile (torch.Tensor): flip angle scaling along slice profile of shape (nlocs,).
-                * basis (torch.Tensor): low rank subspace basis for subspace reconstruction of shape (ncoeff, ncontrasts).
-        * **affine** (np.ndarray): 
+                * slice_profile (torch.Tensor): 
+                    Flip angle scaling along slice profile of shape (nlocs,).
+                * basis (torch.Tensor): 
+                    Low rank subspace basis for subspace reconstruction of shape (ncoeff, ncontrasts).
+        * affine (np.ndarray): 
             Affine matrix describing image spacing, orientation and origin of shape (4, 4).
-        * **ref_dicom** (pydicom.Dataset): 
+        * ref_dicom (pydicom.Dataset): 
             Template dicom for image export.
-        * **flip** (list): list of spatial axis to be flipped after image reconstruction.
+        * flip (list): 
+            List of spatial axis to be flipped after image reconstruction.
             The default is an empty list (no flipping).
-         **transpose** (list): permutation of image dimensions after reconstruction, depending on acquisition mode:
+        * transpose (list): 
+             Permutation of image dimensions after reconstruction, depending on acquisition mode:
                 * 2Dcart: reconstructed image has (nslices, ncontrasts, ny, nx) -> transpose = [1, 0, 2, 3] 
                 * 2Dnoncart: reconstructed image has (nslices, ncontrasts, ny, nx) -> transpose = [1, 0, 2, 3] 
                 * 3Dcart: reconstructed image has (ncontrasts, nz, ny, nx) -> transpose = [0, 1, 2, 3] 
                 * 3Dnoncart: reconstructed image has (nx, ncontrasts, nz, ny) -> transpose = [1, 2, 3, 0] 
-                The default is an empty list (no transposition).
+            The default is an empty list (no transposition).
 
             
     Returns