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"""DICOM IO connector
This module implements several tools to facilitate the interaction between
a Google Cloud Healthcare DICOM store and a Beam pipeline.
For more details on DICOM store and API:
https://cloud.google.com/healthcare/docs/how-tos/dicom
The DICOM IO connector can be used to search metadata or write DICOM files
to DICOM store.
When used together with Google Pubsub message connector, the
`FormatToQido` PTransform implemented in this module can be used
to convert Pubsub messages to search requests.
Since Traceability is crucial for healthcare
API users, every input or error message will be recorded in the output of
the DICOM IO connector. As a result, every PTransform in this module will
return a PCollection of dict that encodes results and detailed error messages.
Search instance's metadata (QIDO request)
===================================================
DicomSearch() wraps the QIDO request client and supports 3 levels of search.
Users should specify the level by setting the 'search_type' entry in the input
dict. They can also refine the search by adding tags to filter the results using
the 'params' entry. Here is a sample usage:
with Pipeline() as p:
input_dict = p | beam.Create(
[{'project_id': 'abc123', 'type': 'instances',...},
{'project_id': 'dicom_go', 'type': 'series',...}])
results = input_dict | io.gcp.DicomSearch()
results | 'print successful search' >> beam.Map(
lambda x: print(x['result'] if x['success'] else None))
results | 'print failed search' >> beam.Map(
lambda x: print(x['result'] if not x['success'] else None))
In the example above, successful qido search results and error messages for
failed requests are printed. When used in real life, user can choose to filter
those data and output them to wherever they want.
Convert DICOM Pubsub message to Qido search request
===================================================
Healthcare API users might read messages from Pubsub to monitor the store
operations (e.g. new file) in a DICOM storage. Pubsub message encode
DICOM as a web store path as well as instance ids. If users are interested in
getting new instance's metadata, they can use the `FormatToQido` transform
to convert the message into Qido Search dict then use the `DicomSearch`
transform. Here is a sample usage:
pipeline_options = PipelineOptions()
pipeline_options.view_as(StandardOptions).streaming = True
p = beam.Pipeline(options=pipeline_options)
pubsub = p | beam.io.ReadStringFromPubsub(subscription='a_dicom_store')
results = pubsub | FormatToQido()
success = results | 'filter message' >> beam.Filter(lambda x: x['success'])
qido_dict = success | 'get qido request' >> beam.Map(lambda x: x['result'])
metadata = qido_dict | DicomSearch()
In the example above, the pipeline is listening to a pubsub topic and waiting
for messages from DICOM API. When a new DICOM file comes into the storage, the
pipeline will receive a pubsub message, convert it to a Qido request dict and
feed it to DicomSearch() PTransform. As a result, users can get the metadata for
every new DICOM file. Note that not every pubsub message received is from DICOM
API, so we to filter the results first.
Store a DICOM file in a DICOM storage
===================================================
UploadToDicomStore() wraps store request API and users can use it to send a
DICOM file to a DICOM store. It supports two types of input: 1.file data in
byte[] 2.fileio object. Users should set the 'input_type' when initialzing
this PTransform. Here are the examples:
with Pipeline() as p:
input_dict = {'project_id': 'abc123', 'type': 'instances',...}
path = "gcs://bucketname/something/a.dcm"
match = p | fileio.MatchFiles(path)
fileio_obj = match | fileio.ReadAll()
results = fileio_obj | UploadToDicomStore(input_dict, 'fileio')
with Pipeline() as p:
input_dict = {'project_id': 'abc123', 'type': 'instances',...}
f = open("abc.dcm", "rb")
dcm_file = f.read()
byte_file = p | 'create byte file' >> beam.Create([dcm_file])
results = byte_file | UploadToDicomStore(input_dict, 'bytes')
The first example uses a PCollection of fileio objects as input.
UploadToDicomStore will read DICOM files from the objects and send them
to a DICOM storage.
The second example uses a PCollection of byte[] as input. UploadToDicomStore
will directly send those DICOM files to a DICOM storage.
Users can also get the operation results in the output PCollection if they want
to handle the failed store requests.
"""
# pytype: skip-file
from concurrent.futures import ThreadPoolExecutor
from concurrent.futures import as_completed
import apache_beam as beam
from apache_beam.io.gcp.healthcare.dicomclient import DicomApiHttpClient
from apache_beam.transforms import PTransform
[docs]class DicomSearch(PTransform):
"""A PTransform used for retrieving DICOM instance metadata from Google
Cloud DICOM store. It takes a PCollection of dicts as input and return
a PCollection of dict as results:
INPUT:
The input dict represents DICOM web path parameters, which has the following
string keys and values:
{
'project_id': str,
'region': str,
'dataset_id': str,
'dicom_store_id': str,
'search_type': str,
'params': dict(str,str) (Optional),
}
Key-value pairs:
project_id: Id of the project in which the DICOM store is
located. (Required)
region: Region where the DICOM store resides. (Required)
dataset_id: Id of the dataset where DICOM store belongs to. (Required)
dicom_store_id: Id of the dicom store. (Required)
search_type: Which type of search it is, could only be one of the three
values: 'instances', 'series', or 'studies'. (Required)
params: A dict of str:str pairs used to refine QIDO search. (Optional)
Supported tags in three categories:
1.Studies:
* StudyInstanceUID,
* PatientName,
* PatientID,
* AccessionNumber,
* ReferringPhysicianName,
* StudyDate,
2.Series: all study level search terms and
* SeriesInstanceUID,
* Modality,
3.Instances: all study/series level search terms and
* SOPInstanceUID,
e.g. {"StudyInstanceUID":"1","SeriesInstanceUID":"2"}
OUTPUT:
The output dict wraps results as well as error messages:
{
'result': a list of dicts in JSON style.
'success': boolean value telling whether the operation is successful.
'input': detail ids and dicomweb path for this retrieval.
'status': status code from the server, used as error message.
}
"""
def __init__(
self, buffer_size=8, max_workers=5, client=None, credential=None):
"""Initializes DicomSearch.
Args:
buffer_size: # type: Int. Size of the request buffer.
max_workers: # type: Int. Maximum number of threads a worker can
create. If it is set to one, all the request will be processed
sequentially in a worker.
client: # type: object. If it is specified, all the Api calls will
made by this client instead of the default one (DicomApiHttpClient).
credential: # type: Google credential object, if it is specified, the
Http client will use it to create sessions instead of the default.
"""
self.buffer_size = buffer_size
self.max_workers = max_workers
self.client = client or DicomApiHttpClient()
self.credential = credential
[docs] def expand(self, pcoll):
return pcoll | beam.ParDo(
_QidoReadFn(
self.buffer_size, self.max_workers, self.client, self.credential))
class _QidoReadFn(beam.DoFn):
"""A DoFn for executing every qido query request."""
def __init__(self, buffer_size, max_workers, client, credential=None):
self.buffer_size = buffer_size
self.max_workers = max_workers
self.client = client
self.credential = credential
def start_bundle(self):
self.buffer = []
def finish_bundle(self):
for item in self._flush():
yield item
def validate_element(self, element):
# Check if all required keys present.
required_keys = [
'project_id', 'region', 'dataset_id', 'dicom_store_id', 'search_type'
]
for key in required_keys:
if key not in element:
error_message = 'Must have %s in the dict.' % (key)
return False, error_message
# Check if return type is correct.
if element['search_type'] in ['instances', "studies", "series"]:
return True, None
else:
error_message = (
'Search type can only be "studies", '
'"instances" or "series"')
return False, error_message
def process(
self,
element,
window=beam.DoFn.WindowParam,
timestamp=beam.DoFn.TimestampParam):
# Check if the element is valid
valid, error_message = self.validate_element(element)
if valid:
self.buffer.append((element, window, timestamp))
if len(self.buffer) >= self.buffer_size:
for item in self._flush():
yield item
else:
# Return this when the input dict dose not meet the requirements
out = {}
out['result'] = []
out['status'] = error_message
out['input'] = element
out['success'] = False
yield out
def make_request(self, element):
# Sending Qido request to DICOM Api
project_id = element['project_id']
region = element['region']
dataset_id = element['dataset_id']
dicom_store_id = element['dicom_store_id']
search_type = element['search_type']
params = element['params'] if 'params' in element else None
# Call qido search http client
result, status_code = self.client.qido_search(
project_id, region, dataset_id, dicom_store_id,
search_type, params, self.credential
)
out = {}
out['result'] = result
out['status'] = status_code
out['input'] = element
out['success'] = (status_code == 200)
return out
def process_buffer_element(self, buffer_element):
# Thread job runner - each thread makes a Qido search request
value = self.make_request(buffer_element[0])
windows = [buffer_element[1]]
timestamp = buffer_element[2]
return beam.utils.windowed_value.WindowedValue(
value=value, timestamp=timestamp, windows=windows)
def _flush(self):
# Create thread pool executor and process the buffered elements in paralllel
executor = ThreadPoolExecutor(max_workers=self.max_workers)
futures = [
executor.submit(self.process_buffer_element, ele) for ele in self.buffer
]
self.buffer = []
for f in as_completed(futures):
yield f.result()
class _ConvertStringToQido(beam.DoFn):
"""A DoFn for converting pubsub string to qido search parameters."""
def process(self, element):
# Some constants for DICOM pubsub message
NUM_PUBSUB_STR_ENTRIES = 15
NUM_DICOM_WEBPATH_PARAMETERS = 5
NUM_TOTAL_PARAMETERS = 8
INDEX_PROJECT_ID = 1
INDEX_REGION = 3
INDEX_DATASET_ID = 5
INDEX_DICOMSTORE_ID = 7
INDEX_STUDY_ID = 10
INDEX_SERIE_ID = 12
INDEX_INSTANCE_ID = 14
entries = element.split('/')
# Output dict with error message, used when
# receiving invalid pubsub string.
error_dict = {}
error_dict['result'] = {}
error_dict['input'] = element
error_dict['success'] = False
if len(entries) != NUM_PUBSUB_STR_ENTRIES:
return [error_dict]
required_keys = [
'projects',
'locations',
'datasets',
'dicomStores',
'dicomWeb',
'studies',
'series',
'instances'
]
# Check if the required keys present and
# the positions of those keys are correct
for i in range(NUM_DICOM_WEBPATH_PARAMETERS):
if required_keys[i] != entries[i * 2]:
return [error_dict]
for i in range(NUM_DICOM_WEBPATH_PARAMETERS, NUM_TOTAL_PARAMETERS):
if required_keys[i] != entries[i * 2 - 1]:
return [error_dict]
# Compose dicom webpath parameters for qido search
qido_dict = {}
qido_dict['project_id'] = entries[INDEX_PROJECT_ID]
qido_dict['region'] = entries[INDEX_REGION]
qido_dict['dataset_id'] = entries[INDEX_DATASET_ID]
qido_dict['dicom_store_id'] = entries[INDEX_DICOMSTORE_ID]
qido_dict['search_type'] = 'instances'
# Compose instance level params for qido search
params = {}
params['StudyInstanceUID'] = entries[INDEX_STUDY_ID]
params['SeriesInstanceUID'] = entries[INDEX_SERIE_ID]
params['SOPInstanceUID'] = entries[INDEX_INSTANCE_ID]
qido_dict['params'] = params
out = {}
out['result'] = qido_dict
out['input'] = element
out['success'] = True
return [out]
[docs]class UploadToDicomStore(PTransform):
"""A PTransform for storing instances to a DICOM store.
Takes PCollection of byte[] as input and return a PCollection of dict as
results. The inputs are normally DICOM file in bytes or str filename.
INPUT:
This PTransform supports two types of input:
1. Byte[]: representing dicom file.
2. Fileio object: stream file object.
OUTPUT:
The output dict encodes status as well as error messages:
{
'success': boolean value telling whether the store is successful.
'input': undeliverable data. Exactly the same as the input,
only set if the operation is failed.
'status': status code from the server, used as error messages.
}
"""
def __init__(
self,
destination_dict,
input_type,
buffer_size=8,
max_workers=5,
client=None,
credential=None):
"""Initializes UploadToDicomStore.
Args:
destination_dict: # type: python dict, encodes DICOM endpoint information:
{
'project_id': str,
'region': str,
'dataset_id': str,
'dicom_store_id': str,
}
Key-value pairs:
* project_id: Id of the project in which DICOM store locates. (Required)
* region: Region where the DICOM store resides. (Required)
* dataset_id: Id of the dataset where DICOM store belongs to. (Required)
* dicom_store_id: Id of the dicom store. (Required)
input_type: # type: string, could only be 'bytes' or 'fileio'
buffer_size: # type: Int. Size of the request buffer.
max_workers: # type: Int. Maximum number of threads a worker can
create. If it is set to one, all the request will be processed
sequentially in a worker.
client: # type: object. If it is specified, all the Api calls will
made by this client instead of the default one (DicomApiHttpClient).
credential: # type: Google credential object, if it is specified, the
Http client will use it instead of the default one.
"""
self.destination_dict = destination_dict
# input_type pre-check
if input_type not in ['bytes', 'fileio']:
raise ValueError("input_type could only be 'bytes' or 'fileio'")
self.input_type = input_type
self.buffer_size = buffer_size
self.max_workers = max_workers
self.client = client
self.credential = credential
[docs] def expand(self, pcoll):
return pcoll | beam.ParDo(
_StoreInstance(
self.destination_dict,
self.input_type,
self.buffer_size,
self.max_workers,
self.client,
self.credential))
class _StoreInstance(beam.DoFn):
"""A DoFn read or fetch dicom files then push it to a dicom store."""
def __init__(
self,
destination_dict,
input_type,
buffer_size,
max_workers,
client,
credential=None):
# pre-check destination dict
required_keys = ['project_id', 'region', 'dataset_id', 'dicom_store_id']
for key in required_keys:
if key not in destination_dict:
raise ValueError('Must have %s in the dict.' % (key))
self.destination_dict = destination_dict
self.input_type = input_type
self.buffer_size = buffer_size
self.max_workers = max_workers
self.client = client
self.credential = credential
def start_bundle(self):
self.buffer = []
def finish_bundle(self):
for item in self._flush():
yield item
def process(
self,
element,
window=beam.DoFn.WindowParam,
timestamp=beam.DoFn.TimestampParam):
self.buffer.append((element, window, timestamp))
if len(self.buffer) >= self.buffer_size:
for item in self._flush():
yield item
def make_request(self, dicom_file):
# Send file to DICOM store and records the results.
project_id = self.destination_dict['project_id']
region = self.destination_dict['region']
dataset_id = self.destination_dict['dataset_id']
dicom_store_id = self.destination_dict['dicom_store_id']
# Feed the dicom file into store client
if self.client:
_, status_code = self.client.dicomweb_store_instance(
project_id, region, dataset_id, dicom_store_id, dicom_file,
self.credential
)
else:
_, status_code = DicomApiHttpClient().dicomweb_store_instance(
project_id, region, dataset_id, dicom_store_id, dicom_file,
self.credential
)
out = {}
out['status'] = status_code
out['success'] = (status_code == 200)
return out
def read_dicom_file(self, buffer_element):
# Read the file based on different input. If the read fails ,return
# an error dict which records input and error messages.
try:
if self.input_type == 'fileio':
f = buffer_element.open()
data = f.read()
f.close()
return True, data
else:
return True, buffer_element
except Exception as error_message:
error_out = {}
error_out['status'] = error_message
error_out['success'] = False
return False, error_out
def process_buffer_element(self, buffer_element):
# Thread job runner - each thread stores a DICOM file
success, read_result = self.read_dicom_file(buffer_element[0])
windows = [buffer_element[1]]
timestamp = buffer_element[2]
value = None
if success:
value = self.make_request(read_result)
else:
value = read_result
# save the undeliverable data
if not value['success']:
value['input'] = buffer_element[0]
return beam.utils.windowed_value.WindowedValue(
value=value, timestamp=timestamp, windows=windows)
def _flush(self):
# Create thread pool executor and process the buffered elements in paralllel
executor = ThreadPoolExecutor(max_workers=self.max_workers)
futures = [
executor.submit(self.process_buffer_element, ele) for ele in self.buffer
]
self.buffer = []
for f in as_completed(futures):
yield f.result()