Skip to content

Commit

Permalink
Merge pull request #161 from AustralianCancerDataNetwork/mr-changes
Browse files Browse the repository at this point in the history 
Add support for converting MR images
  • Loading branch information
@dalmouiee
dalmouiee authored Dec 13, 2023
2 parents d53ec2c + 61702fa commit 9e02c37
Show file tree
Hide file tree
Showing 5 changed files with 48 additions and 23 deletions.
23 changes: 8 additions & 15 deletions examples/ObjectGeneration.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -69,14 +69,11 @@
"df_doses = df[df[\"modality\"] == \"RTDOSE\"]\n",
"\n",
"for _, dose_row in df_doses.iterrows():\n",
" \n",
" if \"EQD2_ab\" in dose_row.hashed_uid:\n",
" # This is an already scaled dose\n",
" continue\n",
" \n",
" df_linked_plan = df[\n",
" df[\"sop_instance_uid\"] == dose_row.referenced_sop_instance_uid\n",
" ]\n",
"\n",
" df_linked_plan = df[df[\"sop_instance_uid\"] == dose_row.referenced_sop_instance_uid]\n",
"\n",
" linked_plan = df_linked_plan.iloc[0]\n",
" ds_plan = load_object_metadata(linked_plan)\n",
Expand All @@ -85,30 +82,26 @@
" fractions = int(ds_plan.FractionGroupSequence[0].NumberOfFractionsPlanned)\n",
"\n",
" print(f\"{dose_row.patient_id} has {fractions} fractions\")\n",
" \n",
"\n",
" # Load the dose grid\n",
" dose_path = Path(dose_row.path).joinpath(\"RTDOSE.nii.gz\")\n",
" dose = sitk.ReadImage(str(dose_path))\n",
" dose = sitk.Cast(dose, sitk.sitkFloat64)\n",
"\n",
" dose_id = f\"{dose_row.hashed_uid}_EQD2_ab{alpha_beta}\"\n",
" \n",
"\n",
" if len(df_doses[df_doses.hashed_uid == dose_id]) > 0:\n",
" print(f\"Already converted dose for {dose_id}\")\n",
" continue\n",
" \n",
"\n",
" # Apply the EQD2 correction\n",
" eqd2_dose = dose * (((dose/actual_frac) + alpha_beta) / (2 + alpha_beta))\n",
" \n",
" eqd2_dose = dose * (((dose / fractions) + alpha_beta) / (2 + alpha_beta))\n",
"\n",
" # Save off the new dose grid\n",
" try:\n",
" print(f\"Saving dose grid with ID: {dose_id}\")\n",
" pydicer.add_dose_object(\n",
" eqd2_dose,\n",
" dose_id,\n",
" dose_row.patient_id,\n",
" linked_plan,\n",
" dose_row.for_uid\n",
" eqd2_dose, dose_id, dose_row.patient_id, linked_plan, dose_row.for_uid\n",
" )\n",
" except SystemError:\n",
" print(f\"Dose object {dose_id} already exists!\")"
Expand Down
1 change: 1 addition & 0 deletions pydicer/constants.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
RT_PLAN_STORAGE_UID = "1.2.840.10008.5.1.4.1.1.481.5"
CT_IMAGE_STORAGE_UID = "1.2.840.10008.5.1.4.1.1.2"
PET_IMAGE_STORAGE_UID = "1.2.840.10008.5.1.4.1.1.128"
MR_IMAGE_STORAGE_UID = "1.2.840.10008.5.1.4.1.1.4"

PYDICER_DIR_NAME = ".pydicer"
CONVERTED_DIR_NAME = "data"
Expand Down
29 changes: 29 additions & 0 deletions pydicer/convert/data.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -26,6 +26,7 @@
RT_STRUCTURE_STORAGE_UID,
CT_IMAGE_STORAGE_UID,
PET_IMAGE_STORAGE_UID,
MR_IMAGE_STORAGE_UID,
)
from pydicer.logger import PatientLogger

Expand Down Expand Up @@ -411,6 +412,34 @@ def convert(self, patient=None, force=True):
entry["path"] = str(output_dir.relative_to(self.working_directory))

self.add_entry(entry)

elif sop_class_uid == MR_IMAGE_STORAGE_UID:
# TODO Handle inconsistent slice spacing
if config.get_config("interp_missing_slices"):
series_files = handle_missing_slice(df_files)
else:
raise ValueError("Slice Locations are not evenly spaced")

series_files = [str(f) for f in series_files]
series = sitk.ReadImage(series_files)

output_dir = patient_directory.joinpath("images", sop_instance_hash)
output_dir.mkdir(exist_ok=True, parents=True)

nifti_file = output_dir.joinpath("MR.nii.gz")
sitk.WriteImage(series, str(nifti_file))
logger.debug("Writing MR Image Series to: %s", nifti_file)

json_file = output_dir.joinpath("metadata.json")
convert_dicom_headers(
series_files[0],
str(nifti_file.relative_to(self.output_directory)),
json_file,
)

entry["path"] = str(output_dir.relative_to(self.working_directory))

self.add_entry(entry)
patient_logger.eval_module_process("convert", sop_instance_hash)

elif sop_class_uid == RT_STRUCTURE_STORAGE_UID:
Expand Down
9 changes: 6 additions & 3 deletions pydicer/preprocess/data.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@
RT_PLAN_STORAGE_UID,
RT_STRUCTURE_STORAGE_UID,
CT_IMAGE_STORAGE_UID,
MR_IMAGE_STORAGE_UID,
)
from pydicer.quarantine import copy_file_to_quarantine
from pydicer.utils import read_preprocessed_data, get_iterator
Expand Down Expand Up @@ -109,7 +110,11 @@ def scan_file(self, file):
except AttributeError:
logger.warning("Unable to determine Reference Series UID")

elif dicom_type_uid in (CT_IMAGE_STORAGE_UID, PET_IMAGE_STORAGE_UID):
elif dicom_type_uid in (
CT_IMAGE_STORAGE_UID,
PET_IMAGE_STORAGE_UID,
MR_IMAGE_STORAGE_UID,
):
image_position = np.array(ds.ImagePositionPatient, dtype=float)
image_orientation = np.array(ds.ImageOrientationPatient, dtype=float)

Expand Down Expand Up @@ -213,8 +218,6 @@ def preprocess(self, input_directory, force=True):

files = [f for f in files if str(f) not in files_already_scanned]

logger.info("Found %d files to scan", len(files))

result_list = []

for f in get_iterator(files, unit="files", name="preprocess"):
Expand Down
9 changes: 4 additions & 5 deletions pydicer/visualise/data.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,7 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True):
join_working_directory=True,
)

visualise_modalities = ["CT", "RTSTRUCT", "RTDOSE", "PT"]
visualise_modalities = ["CT", "MR", "RTSTRUCT", "RTDOSE", "PT"]
df_process = df_process[df_process.modality.isin(visualise_modalities)]

for _, row in get_iterator(
Expand Down Expand Up @@ -139,17 +139,16 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True):
patient_logger.eval_module_process("visualise", row.hashed_uid)
logger.debug("Created CT visualisation: %s", vis_filename)

if row.modality == "PT":
if row.modality in ("MR", "PT"):
img_path = Path(row.path)
vis_filename = img_path.joinpath("PT.png")
vis_filename = img_path.joinpath(f"{row.modality}.png")

if vis_filename.exists() and not force:
logger.info("Visualisation already exists at %s", vis_filename)
continue

img = sitk.ReadImage(str(img_path.joinpath(f"{row.modality}.nii.gz")))

# TODO find linked CT and render PT on top of that
vis = ImageVisualiser(img)
fig = vis.show()

Expand All @@ -160,7 +159,7 @@ def visualise(self, dataset_name=CONVERTED_DIR_NAME, patient=None, force=True):
plt.close(fig)

patient_logger.eval_module_process("visualise", row.hashed_uid)
logger.debug("Created PT visualisation: %s", vis_filename)
logger.debug("Created %s visualisation: %s", row.modality, vis_filename)

# Visualise the structures on top of their linked image
if row.modality == "RTSTRUCT":
Expand Down

0 comments on commit 9e02c37

Please sign in to comment.