diff --git a/fileformats/medimage/__init__.py b/fileformats/medimage/__init__.py
index 5c9f7c9..7873030 100644
--- a/fileformats/medimage/__init__.py
+++ b/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,
 )
diff --git a/fileformats/medimage/dicom.py b/fileformats/medimage/dicom.py
index 7231f3b..e57bd03 100644
--- a/fileformats/medimage/dicom.py
+++ b/fileformats/medimage/dicom.py
@@ -39,7 +39,6 @@ class DicomDir(DicomCollection, DirectoryContaining[Dicom]):
 
 
 class DicomSeries(DicomCollection, SetOf[Dicom]):
-
     @classmethod
     def from_paths(
         cls, fspaths: ty.Iterable[Path], common_ok: bool = False
@@ -47,7 +46,7 @@ def from_paths(
         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
 
 
@@ -55,12 +54,16 @@ def from_paths(
 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
diff --git a/fileformats/medimage/raw.py b/fileformats/medimage/raw.py
deleted file mode 100644
index 32d3e38..0000000
--- a/fileformats/medimage/raw.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from fileformats.generic import File
-
-
-class ListMode(File):
-
-    ext = ".bf"
-    binary = True
-
-
-class Kspace(File):
-
-    binary = True
-    iana_mime = None
-
-
-class TwixVb(Kspace):
-    """The format that k-space data is saved in from Siemens scanners
-    with system version vB to (at least) vE"""
-
-    ext = ".dat"
-
-
-# class CustomKspace(Kspace):
-#     """A custom format for saving k-space data in binary amd JSON files.
-
-#     Binary files
-#     ------------
-#     primary : 5-d matrix
-#         Data from "data" scan organised in the following dimension order:
-#         channel, freq-encode, phase-encode, partition-encode (slice), echoes
-#     reference : 5-d matrix
-#         Data from calibration scan organised in the same dimension order as
-#         primary scan
-
-#     JSON side-car
-#     -------------
-#     dims : 3-tuple(int)
-#         The dimensions of the image in freq, phase, partition (slice) order
-#     voxel_size : 3-tuple(float)
-#         Size of the voxels in same order as dims
-#     num_channels : int
-#         Number of channels in the k-space
-#     num_echos : int
-#         Number of echoes in the acquisition
-#     T E : tuple(float)
-#         The echo times
-#     B0_strength : float
-#         Strength of the B0 field
-#     B0_dir : 3-tuple(float)
-#         Direction of the B0 field
-#     larmor_freq : float
-#         The central larmor row_frequency of the scanner"""
-
-#     ext = ".ks"
-#     side_cars = ("ref", "json")
-
-
-class Rda(File):
-    """MRS format"""
-
-    ext = ".rda"
-    binary = True
diff --git a/fileformats/medimage/raw/__init__.py b/fileformats/medimage/raw/__init__.py
new file mode 100644
index 0000000..ac2e3b4
--- /dev/null
+++ b/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,
+)
diff --git a/fileformats/medimage/raw/mri/__init__.py b/fileformats/medimage/raw/mri/__init__.py
new file mode 100644
index 0000000..dd71cba
--- /dev/null
+++ b/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
diff --git a/fileformats/medimage/raw/pet/__init__.py b/fileformats/medimage/raw/pet/__init__.py
new file mode 100644
index 0000000..14b8f70
--- /dev/null
+++ b/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,
+)
diff --git a/fileformats/medimage/raw/pet/base.py b/fileformats/medimage/raw/pet/base.py
new file mode 100644
index 0000000..024248d
--- /dev/null
+++ b/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
diff --git a/fileformats/medimage/raw/pet/siemens.py b/fileformats/medimage/raw/pet/siemens.py
new file mode 100644
index 0000000..ee13e1d
--- /dev/null
+++ b/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"