dicomtrolley 0.5.1

Retrieve medical images via WADO, MINT and DICOM-QR

dicomtrolley

Retrieve medical images via WADO, MINT and DICOM-QR. Requires python 3.7, 3.8 or 3.9 Represents images as pydicom.Dataset instances.

Usage

Basic example

# Create a logged-in http session
session = VitreaConnection(
    "https://server/login").log_in(user,password,realm)
                           
# Use this session to create a trolley using MINT and WADO
trolley = Trolley(searcher=Mint(session, "https://server/mint"),
                  wado=Wado(session, "https://server/wado"]))

# find some studies (using MINT)
studies = trolley.find_studies(MintQuery(patientName='B*'))  

# download the fist one (using WADO)
trolley.download(studies[0], output_dir='/tmp/trolley')

Finding studies

studies = trolley.find_studies(MintQuery(patientName='B*'))

Query parameters can be found in dicomtrolley.query.Query. Valid include fields (which information gets sent back) can be found in include_fields.py:

studies = trolley.find_studies_mint(
    MintQuery(modalitiesInStudy='CT*', 
              patientSex="F", 
              minStudyDate=datetime(year=2015, month=3, day=1),
              maxStudyDate=datetime(year=2020, month=3, day=1),
              includeFields=['PatientBirthDate', 'SOPClassesInStudy']))

Finding series and instance details

To include series and instance level information as well, use the queryLevel parameter

studies = trolley.find_studies(  # find studies series and instances
    MintQuery(studyInstanceID='B*', 
              queryLevel=QueryLevels.INSTANCE)

a_series = studies.series[0]  # studies now contain series    
an_instance = a_series.instances[0]  # and series contain instances

Downloading data

Any study, series or instance can be downloaded

studies = trolley.find_studies(MintQuery(patientName='B*',
                                         queryLevel=QueryLevels.INSTANCE))

path = '/tmp/trolley'
trolley.download(studies, path)                             # all studies
trolley.download(studies[0]), path                          # a single study
trolley.download(studies[0].series[0], path)                # a single series
trolley.download(studies[0].series[0].instances[:3], path)  # first 3 instances

More control over download: obtain pydicom.Dataset instances directly

studies = trolley.find_studies(              # find study including instances
    Query(PatientID='1234', 
          queryLevel=QueryLevels.INSTANCE)

for ds in trolley.get_dataset(studies):      # obtain Dataset for each instance
    ds.save_as(f'/tmp/{ds.SOPInstanceUID}.dcm')

DICOM-QR

Trolley can use DICOM-QR instead of MINT as a search method

dicom_qr = DICOMQR(host,port,aet,aec)
trolley = Trolley(searcher=dicom_qr, wado=wado)

# Finding is similar to MINT, but a DICOMQuery is used instead
trolley.find_studies(  
    query=DICOMQuery(PatientName="BAL*",   
                     minStudyDate=datetime(year=2015, month=3, day=1),
                     maxStudyDate=datetime(year=2015, month=4, day=1),
                     includeFields=["PatientBirthDate", "SOPClassesInStudy"],
                     QueryRetrieveLevel=QueryRetrieveLevels.STUDY)) 

Examples

• search for studies in MINT
• search for studies in DICOM-QR
• Find and download studies

Caveats

Dicomtrolley has been developed for and tested on a Vitrea Connection 8.2.0.1 system. This claims to be consistent with WADO and MINT 1.2 interfaces, but does not implement all parts of these standards.

Certain query parameter values and restraints might be specific to Vitrea Connection 8.2.0.1. For example, the exact list of DICOM elements that can be returned from a query might be different for different servers.