DICOM write works - added anonymization

src/deepmr/io/header/__init__.py

@@ -103,29 +103,41 @@ def read_acquisition_header(filepath, device="cpu", verbose=False, *args):
             print(f"Trajectory shape: (ncontrasts={head.traj.shape[0]}, nviews={head.traj.shape[1]}, nsamples={head.traj.shape[2]}, ndim={head.traj.shape[-1]})")      
         if head.dcf is not None:
             print(f"DCF shape: (ncontrasts={head.dcf.shape[0]}, nviews={head.dcf.shape[1]}, nsamples={head.dcf.shape[2]})")
-        if head.FA is not None:
-            if len(np.unique(head.FA)) > 1:
+    if head.FA is not None:
+        if len(np.unique(head.FA)) > 1:
+            if verbose == 2:
                 print(f"Flip Angle train length: {len(head.FA)}")
-            else:
-                FA = float(np.unique(head.FA)[0])
+        else:
+            FA = float(np.unique(head.FA)[0])
+            head.FA = FA
+            if verbose == 2:
                 print(f"Constant FA: {round(abs(FA), 2)} deg")
-        if head.TR is not None:
-            if len(np.unique(head.TR)) > 1:
+    if head.TR is not None:
+        if len(np.unique(head.TR)) > 1:
+            if verbose == 2:
                 print(f"TR train length: {len(head.TR)}")
-            else:
-                TR = float(np.unique(head.TR)[0])
+        else:
+            TR = float(np.unique(head.TR)[0])
+            head.TR = TR
+            if verbose == 2:
                 print(f"Constant TR: {round(TR, 2)} ms")
-        if head.TE is not None:
-            if len(np.unique(head.TE)) > 1:
+    if head.TE is not None:
+        if len(np.unique(head.TE)) > 1:
+            if verbose == 2:
                 print(f"Echo train length: {len(head.TE)}")
-            else:
-                TE = float(np.unique(head.TE)[0])
+        else:
+            TE = float(np.unique(head.TE)[0])
+            head.TE = TE
+            if verbose == 2:
                 print(f"Constant TE: {round(TE, 2)} ms")
-        if head.TI is not None and np.allclose(head.TI, 0.0) is False:
-            if len(np.unique(head.TI)) > 1:
+    if head.TI is not None and np.allclose(head.TI, 0.0) is False:
+        if len(np.unique(head.TI)) > 1:
+            if verbose == 2:
                 print(f"Inversion train length: {len(head.TI)}")
-            else:
-                TI = float(np.unique(head.TI)[0])
+        else:
+            TI = float(np.unique(head.TI)[0])
+            head.TI = TI
+            if verbose == 2:
                 print(f"Constant TI: {round(TI, 2)} ms")
 
     tend = time.time()

src/deepmr/io/image/__init__.py

@@ -89,29 +89,41 @@ def read_image(filepath, acqheader=None, device="cpu", verbose=0):
             print(f"Trajectory shape: (ncontrasts={head.traj.shape[0]}, nviews={head.traj.shape[1]}, nsamples={head.traj.shape[2]}, ndim={head.traj.shape[-1]})")      
         if head.dcf is not None:
             print(f"DCF shape: (ncontrasts={head.dcf.shape[0]}, nviews={head.dcf.shape[1]}, nsamples={head.dcf.shape[2]})")
-        if head.FA is not None:
-            if len(np.unique(head.FA)) > 1:
+    if head.FA is not None:
+        if len(np.unique(head.FA)) > 1:
+            if verbose == 2:
                 print(f"Flip Angle train length: {len(head.FA)}")
-            else:
-                FA = float(np.unique(head.FA)[0])
+        else:
+            FA = float(np.unique(head.FA)[0])
+            head.FA = FA
+            if verbose == 2:
                 print(f"Constant FA: {round(abs(FA), 2)} deg")
-        if head.TR is not None:
-            if len(np.unique(head.TR)) > 1:
+    if head.TR is not None:
+        if len(np.unique(head.TR)) > 1:
+            if verbose == 2:
                 print(f"TR train length: {len(head.TR)}")
-            else:
-                TR = float(np.unique(head.TR)[0])
+        else:
+            TR = float(np.unique(head.TR)[0])
+            head.TR = TR
+            if verbose == 2:
                 print(f"Constant TR: {round(TR, 2)} ms")
-        if head.TE is not None:
-            if len(np.unique(head.TE)) > 1:
+    if head.TE is not None:
+        if len(np.unique(head.TE)) > 1:
+            if verbose == 2:
                 print(f"Echo train length: {len(head.TE)}")
-            else:
-                TE = float(np.unique(head.TE)[0])
+        else:
+            TE = float(np.unique(head.TE)[0])
+            head.TE = TE
+            if verbose == 2:
                 print(f"Constant TE: {round(TE, 2)} ms")
-        if head.TI is not None and np.allclose(head.TI, 0.0) is False:
-            if len(np.unique(head.TI)) > 1:
+    if head.TI is not None and np.allclose(head.TI, 0.0) is False:
+        if len(np.unique(head.TI)) > 1:
+            if verbose == 2:
                 print(f"Inversion train length: {len(head.TI)}")
-            else:
-                TI = float(np.unique(head.TI)[0])
+        else:
+            TI = float(np.unique(head.TI)[0])
+            head.TI = TI
+            if verbose == 2:
                 print(f"Constant TI: {round(TI, 2)} ms")
 
     # cast

src/deepmr/io/image/common.py

@@ -54,4 +54,5 @@ def _anonymize(head):
     head.ref_dicom.PatientName = ""
     head.ref_dicom.PatientBirthDate = ""            
     head.ref_dicom.PatientWeight = "" 
-    head.ref_dicom.PatientID = "Anon" 
+    head.ref_dicom.PatientID = "Anon" 
+    return head

src/deepmr/io/image/dicom.py

@@ -73,20 +73,20 @@ def read_dicom(filepath):
 
     # unpack sequence
     TI, TE, EC, TR, FA = uContrasts.transpose()
-    
+
     # squeeze
     if sorted_image.shape[0] == 1:
         sorted_image = sorted_image[0]
 
     # initialize header
     header = Header.from_dicom(dsets, firstVolumeIdx)
-
+            
     # update header
     header.FA = FA
     header.TI = TI
     header.TE = TE
     header.TR = TR
-
+            
     return sorted_image, header
 
 def write_dicom(filename, image, filepath="./", head=None, series_description="", series_number_offset=0, series_number_scale=1000, rescale=False, anonymize=False, verbose=False):
@@ -162,6 +162,9 @@ def write_dicom(filename, image, filepath="./", head=None, series_description=""
     if head is not None:
         transpose = head.transpose
         flip = head.flip
+    else:
+        transpose = None
+        flip = None
 
     # cast image to numpy
     image, windowRange = _prepare_image(image, transpose, flip, rescale)
@@ -198,17 +201,47 @@ def write_dicom(filename, image, filepath="./", head=None, series_description=""
 
     # remove constant parameters from header
     if head.FA is not None:
-        if len(np.unique(head.FA)) == 1:
+        if head.FA.size == 1:
+            head.ref_dicom.FlipAngle = float(abs(head.FA))
             head.FA = None
-    if head.TR is not None:
-        if len(np.unique(head.TR)) == 1:
-            head.TR = None
-    if head.TE is not None:
-        if len(np.unique(head.TE)) == 1:
+        elif len(np.unique(head.FA)) == 1:
+            head.ref_dicom.FlipAngle = float(abs(head.FA[0]))
+            head.FA = None
+        else:
+            head.FA = list(abs(head.FA).astype(float))
+    if head.TE is not None and not(np.isinf(np.sum(head.TE))):
+        if head.TE.size == 1:
+            head.ref_dicom.EchoTime = float(head.TE)
+            head.TE = None
+        elif len(np.unique(head.TE)) == 1:
+            head.ref_dicom.EchoTime = float(head.TE[0])
             head.TE = None
-    if head.TI is not None:
-        if len(np.unique(head.TI)) == 1:
+        else:
+            head.TE = list(head.TE.astype(float))
+    else:
+        head.TE = None
+    if head.TR is not None and not(np.isinf(np.sum(head.TR))):
+        if head.TR.size == 1:
+            head.ref_dicom.RepetitionTime = float(head.TR)
+            head.TR = None
+        elif len(np.unique(head.TR)) == 1: 
+            head.ref_dicom.RepetitionTime = float(head.TR[0])
+            head.TR = None
+        else:
+            head.TR= list(head.TR.astype(float))
+    else:
+        head.TR = None
+    if head.TI is not None and not(np.isinf(np.sum(head.TI))):
+        if head.TI.size == 1:
+            head.ref_dicom.InversionTime = float(head.TI)
             head.TI = None
+        elif len(np.unique(head.TI)) == 1:
+            head.ref_dicom.InversionTime = float(head.TI[0])
+            head.TI = None
+        else:
+            head.TI = list(head.TI.astype(float))
+    else:
+        head.TI = None
 
     # generate position and slice location
     pos, zloc = dicom._make_geometry_tags(affine, shape, resolution)
@@ -402,19 +435,24 @@ def _cast_to_complex(dsets_in):
 
     if vendor == "Siemens":
         return _cast_to_complex_siemens(dsets_in)
+
+    if vendor == "DeepMR":
+        return _cast_to_complex_deepmr(dsets_in)
 
 def _get_vendor(dset):
     """
     Get vendor from DICOM header.
     """
-    if dset.Manufacturer == "GE MEDICAL SYSTEMS":
-        return "GE"
+    if "GE MEDICAL SYSTEMS" in dset.Manufacturer.upper():
+        return "GEHC"
 
-    if dset.Manufacturer == "Philips Medical Systems":
+    if "PHILIPS" in dset.Manufacturer.upper():
         return "Philips"
 
-    if dset.Manufacturer == "SIEMENS":
+    if "SIEMENS" in dset.Manufacturer.upper():
         return "Siemens"
+
+    return "DeepMR"
 
 def _cast_to_complex_ge(dsets_in):
     """
@@ -557,6 +595,32 @@ def _cast_to_complex_siemens(dsets_in):
     # count number of instances
     ninstances = img.shape[0]
 
+    # assign to pixel array
+    for n in range(ninstances):
+        dsets_out[n].pixel_array[:] = 0.0
+
+    return img, dsets_out
+
+def _cast_to_complex_deepmr(dsets_in):
+    """
+    Attempt to get magnitude from DeepMR and cast to complex.
+    """
+    # initialize
+    magnitude = []
+
+    # allocate template out
+    dsets_out = []
+
+    # loop over dataset
+    for dset in dsets_in:
+        magnitude.append(dset.pixel_array)
+        dsets_out.append(dset)
+
+    img = np.stack(magnitude, axis=0).astype(np.complex64)
+
+    # count number of instances
+    ninstances = img.shape[0]
+
     # assign to pixel array
     for n in range(ninstances):
         dsets_out[n].pixel_array[:] = 0.0

src/deepmr/io/image/nifti.py

@@ -116,6 +116,7 @@ def write_nifti(filename, image, filepath="./", head=None, series_description=""
     if head is not None:
         head = copy.deepcopy(head)
         head.numpy()
+        # print(head)
 
     # anonymize
     if head is not None and anonymize:
@@ -147,6 +148,9 @@ def write_nifti(filename, image, filepath="./", head=None, series_description=""
     if head is not None:
         transpose = head.transpose
         flip = head.flip
+    else:
+        transpose = None
+        flip = None
 
     # cast image to numpy
     image, windowRange = _prepare_image(image, transpose, flip, rescale)
@@ -177,22 +181,30 @@ def write_nifti(filename, image, filepath="./", head=None, series_description=""
     json_dict["SliceThickness"] = str(head.ref_dicom.SliceThickness)
     json_dict["EchoNumber"] = [str(n) for n in range(ncontrasts)]
     if head.FA is not None:
-        if len(np.unique(head.FA)) == 1:
+        if head.FA.size == 1:
+            json_dict["FlipAngle"] = float(abs(head.FA))
+        elif len(np.unique(head.FA)) == 1:
             json_dict["FlipAngle"] = float(abs(head.FA[0]))
         else:
             json_dict["FlipAngle"] = list(abs(head.FA).astype(float))
-    if head.TE is not None and not(np.isinf(head.TE).any()):
-        if len(np.unique(head.TE)) == 1:
+    if head.TE is not None and not(np.isinf(np.sum(head.TE))):
+        if head.TE.size == 1:
+            json_dict["EchoTime"] = float(head.TE) * 1e-3
+        elif len(np.unique(head.TE)) == 1:
             json_dict["EchoTime"] = float(head.TE[0]) * 1e-3
         else:
             json_dict["EchoTime"] = list(head.TE.astype(float) * 1e-3)
-    if head.TR is not None and not(np.isinf(head.TR).any()):
-        if len(np.unique(head.TR)) == 1:
+    if head.TR is not None and not(np.isinf(np.sum(head.TR))):
+        if head.TR.size == 1:
+            json_dict["RepetitionTime"] = float(head.TR) * 1e-3
+        elif len(np.unique(head.TR)) == 1: 
             json_dict["RepetitionTime"] = float(head.TR[0]) * 1e-3
         else:
             json_dict["RepetitionTime"] = list(head.TR.astype(float) * 1e-3)
-    if head.TI is not None and not(np.isinf(head.TI).any()):
-        if len(np.unique(head.TI)) == 1:
+    if head.TI is not None and not(np.isinf(np.sum(head.TI))):
+        if head.TI.size == 1:
+            json_dict["InversionTime"] = float(head.TI) * 1e-3
+        elif len(np.unique(head.TI)) == 1:
             json_dict["InversionTime"] = float(head.TI[0]) * 1e-3
         else:
             json_dict["InversionTime"] = list(head.TI.astype(float) * 1e-3)

src/deepmr/io/kspace/__init__.py

@@ -179,29 +179,41 @@ def read_rawdata(filepath, acqheader=None, device="cpu", verbose=0):
             print(f"Trajectory shape: (ncontrasts={head.traj.shape[0]}, nviews={head.traj.shape[1]}, nsamples={head.traj.shape[2]}, ndim={head.traj.shape[-1]})")      
         if head.dcf is not None:
             print(f"DCF shape: (ncontrasts={head.dcf.shape[0]}, nviews={head.dcf.shape[1]}, nsamples={head.dcf.shape[2]})")
-        if head.FA is not None:
-            if len(np.unique(head.FA)) > 1:
+    if head.FA is not None:
+        if len(np.unique(head.FA)) > 1:
+            if verbose == 2:
                 print(f"Flip Angle train length: {len(head.FA)}")
-            else:
-                FA = float(np.unique(head.FA)[0])
+        else:
+            FA = float(np.unique(head.FA)[0])
+            head.FA = FA
+            if verbose == 2:
                 print(f"Constant FA: {round(abs(FA), 2)} deg")
-        if head.TR is not None:
-            if len(np.unique(head.TR)) > 1:
+    if head.TR is not None:
+        if len(np.unique(head.TR)) > 1:
+            if verbose == 2:
                 print(f"TR train length: {len(head.TR)}")
-            else:
-                TR = float(np.unique(head.TR)[0])
+        else:
+            TR = float(np.unique(head.TR)[0])
+            head.TR = TR
+            if verbose == 2:
                 print(f"Constant TR: {round(TR, 2)} ms")
-        if head.TE is not None:
-            if len(np.unique(head.TE)) > 1:
+    if head.TE is not None:
+        if len(np.unique(head.TE)) > 1:
+            if verbose == 2:
                 print(f"Echo train length: {len(head.TE)}")
-            else:
-                TE = float(np.unique(head.TE)[0])
+        else:
+            TE = float(np.unique(head.TE)[0])
+            head.TE = TE
+            if verbose == 2:
                 print(f"Constant TE: {round(TE, 2)} ms")
-        if head.TI is not None and np.allclose(head.TI, 0.0) is False:
-            if len(np.unique(head.TI)) > 1:
+    if head.TI is not None and np.allclose(head.TI, 0.0) is False:
+        if len(np.unique(head.TI)) > 1:
+            if verbose == 2:
                 print(f"Inversion train length: {len(head.TI)}")
-            else:
-                TI = float(np.unique(head.TI)[0])
+        else:
+            TI = float(np.unique(head.TI)[0])
+            head.TI = TI
+            if verbose == 2:
                 print(f"Constant TI: {round(TI, 2)} ms")
 
     # cast