Merge pull request #18 from ArcanaFramework/develop

Added Siemens Biograph Raw data types

fileformats/medimage/__init__.py

@@ -30,9 +30,15 @@
     # SiemensDicomDir,
 )
 from .raw import (  # noqa: F401
-    ListMode,
     Kspace,
-    TwixVb,
-    # CustomKspace,
     Rda,
+    PetListMode,
+    PetSinogram,
+    PetCountRate,
+    PetNormalisation,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetRawData,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetListMode,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetSinogram,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetCountRate,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetNormalisation,
 )

fileformats/medimage/dicom.py

@@ -39,28 +39,31 @@ class DicomDir(DicomCollection, DirectoryContaining[Dicom]):
 
 
 class DicomSeries(DicomCollection, SetOf[Dicom]):
-
     @classmethod
     def from_paths(
         cls, fspaths: ty.Iterable[Path], common_ok: bool = False
     ) -> ty.Tuple[ty.Set[FileSet], ty.Set[Path]]:
         dicoms, remaining = Dicom.from_paths(fspaths, common_ok=common_ok)
         series_dict = defaultdict(list)
         for dicom in dicoms:
-            series_dict[dicom["SeriesNumber"]].append(dicom)
+            series_dict[(str(dicom["StudyInstanceUID"]), str(dicom["SeriesNumber"]))].append(dicom)
         return set([cls(s) for s in series_dict.values()]), remaining
 
 
 @FileSet.read_metadata.register
 def dicom_collection_read_metadata(collection: DicomCollection) -> ty.Dict[str, ty.Any]:
     # Collated DICOM headers across series
     collated = copy(collection.contents[0].metadata)
-    if len(collection.contents) > 1:
-        for key, val in collection.contents[1].metadata.items():
-            if val != collated[key]:  # Turn field into list
-                collated[key] = [collated[key], val]
-    for dicom in collection.contents[2:]:
+    for i, dicom in enumerate(collection.contents[1:], start=1):
         for key, val in dicom.metadata.items():
             if val != collated[key]:
+                # Check whether the value is the same as the values in the previous
+                # images in the series
+                if (
+                    not isinstance(collated[key], list)
+                    or isinstance(val, list)
+                    and not isinstance(collated[key][0], list)
+                ):
+                    collated[key] = [collated[key]] * i + [val]
                 collated[key].append(val)
     return collated

fileformats/medimage/raw/__init__.py

@@ -0,0 +1,16 @@
+from .mri import (  # noqa: F401
+    Kspace,
+    Rda,
+)
+from .pet import (
+    PetRawData,
+    PetListMode,
+    PetSinogram,
+    PetCountRate,
+    PetNormalisation,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetRawData,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetListMode,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetSinogram,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetCountRate,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetNormalisation,
+)

fileformats/medimage/raw/mri/__init__.py

@@ -0,0 +1,14 @@
+from fileformats.generic import File
+
+
+class Kspace(File):
+
+    binary = True
+    iana_mime = None
+
+
+class Rda(File):
+    """MRS format"""
+
+    ext = ".rda"
+    binary = True

fileformats/medimage/raw/pet/__init__.py

@@ -0,0 +1,14 @@
+from .base import (
+    PetRawData,
+    PetListMode,
+    PetSinogram,
+    PetCountRate,
+    PetNormalisation,
+)
+from .siemens import (
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetRawData,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetListMode,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetSinogram,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetCountRate,
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetNormalisation,
+)

fileformats/medimage/raw/pet/base.py

@@ -0,0 +1,27 @@
+from fileformats.generic import File
+
+
+class PetRawData(File):
+
+    binary = True
+    iana_mime = None
+
+
+class PetListMode(PetRawData):
+    "raw projection data"
+    iana_mime = None
+
+
+class PetSinogram(PetRawData):
+    "histogrammed projection data in a reconstruction-friendly format"
+    iana_mime = None
+
+
+class PetCountRate(PetRawData):
+    "number of prompt/random/single events per unit time"
+    iana_mime = None
+
+
+class PetNormalisation(PetRawData):
+    "normalisation scan or the current cross calibration factor"
+    iana_mime = None

fileformats/medimage/raw/pet/siemens.py

@@ -0,0 +1,36 @@
+from .base import (
+    PetRawData,
+    PetListMode,
+    PetSinogram,
+    PetCountRate,
+    PetNormalisation,
+)
+
+
+class Vnd_Siemens_Biograph128Vision_Vr20b_PetRawData(PetRawData):
+    iana_mime = None
+    ext = ".ptd"
+
+
+class Vnd_Siemens_Biograph128Vision_Vr20b_PetListMode(
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetRawData, PetListMode
+):
+    pass
+
+
+class Vnd_Siemens_Biograph128Vision_Vr20b_PetSinogram(
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetRawData, PetSinogram
+):
+    "histogrammed projection data in a reconstruction-friendly format"
+
+
+class Vnd_Siemens_Biograph128Vision_Vr20b_PetCountRate(
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetRawData, PetCountRate
+):
+    "number of prompt/random/single events per unit time"
+
+
+class Vnd_Siemens_Biograph128Vision_Vr20b_PetNormalisation(
+    Vnd_Siemens_Biograph128Vision_Vr20b_PetRawData, PetNormalisation
+):
+    "normalisation scan or the current cross calibration factor"