Merge branch 'main' into upload_dev

.devcontainer/Dockerfile

@@ -1,4 +1,4 @@
-FROM mcr.microsoft.com/devcontainers/python:3.12
+FROM mcr.microsoft.com/devcontainers/python:3.13
 
 # Install dependency to make bash completion work with invoke
 RUN apt-get update && \

.github/renovate.json

@@ -1,7 +1,12 @@
 {
   "$schema": "https://docs.renovatebot.com/renovate-schema.json",
-  "extends": ["config:recommended", "group:allNonMajor"],
-  "lockFileMaintenance": { "enabled": true },
+  "extends": ["config:recommended", "group:allNonMajor", "schedule:weekly"],
+  "lockFileMaintenance": {
+    "enabled": true,
+    "automerge": true,
+    "schedule": ["before 5am on Monday"]
+  },
+  "automerge": true,
   "packageRules": [
     {
       "managers": ["docker-compose"],
@@ -15,13 +20,13 @@
     },
     {
       "matchManagers": ["poetry"],
-      "matchPackageNames": ["dicognito"],
-      "allowedVersions": "<=0.17"
+      "matchPackageNames": ["pydicom"],
+      "allowedVersions": "<=2"
     },
     {
       "matchManagers": ["poetry"],
-      "matchPackageNames": ["pydicom"],
-      "allowedVersions": "<=2"
+      "matchPackageNames": ["dicognito"],
+      "allowedVersions": "<=0.17"
     }
   ]
 }

.gitignore

@@ -145,13 +145,17 @@ dump.rdb
 # SQLite databases
 *.db
 
-#PyCharm
+# PyCharm
 .idea/
 
-#virtualenv
+# virtualenv
 bin/
 share/
 pyvenv.cfg
 
+# Certificate files
+*.pem
+
 # ADIT specific
-.dicoms
+/backups/*
+!/backups/.gitkeep

Dockerfile

@@ -31,11 +31,13 @@ ENV PYTHONUNBUFFERED=1 \
 # prepend poetry and venv to path
 ENV PATH="$POETRY_HOME/bin:$VENV_PATH/bin:$PATH"
 
+# deps for db management commands and transferring to an archive
+# make sure to match the postgres version to the service in the compose file
 RUN apt-get update \
+    && apt-get install --no-install-recommends -y postgresql-common \
+    && /usr/share/postgresql-common/pgdg/apt.postgresql.org.sh -y \
     && apt-get install --no-install-recommends -y \
-    # deps for db management commands
-    postgresql-client \
-    # deps for transferring to an archive
+    postgresql-client-17 \
     p7zip-full
 
 

README.md

@@ -4,6 +4,32 @@
 
 ADIT (Automated DICOM Transfer) is a Swiss army knife to exchange DICOM data between various systems by using a convenient web frontend.
 
+**Developed at**
+
+<table>
+  <tr>
+    <td align="center"><a href="https://ccibonn.ai/"><img src="https://github.com/user-attachments/assets/adb95263-bc24-424b-b201-c68492965ebe" width="220" alt="CCI Bonn"/><br />CCIBonn.ai</a></td>
+  </tr>
+</table>
+
+**in Partnership with**
+
+<table>
+  <tr>
+
+  </tr>
+  <tr>
+    <td align="center"><a href="https://www.ukbonn.de/"><img src="https://github.com/user-attachments/assets/97a47dc2-5e9d-4903-ad4c-e79206dfb073" height="120" width="auto" alt="UK Bonn"/><br />Universitätsklinikum Bonn</a></td>
+    <td align="center"><a href="https://www.thoraxklinik-heidelberg.de/"><img src="https://github.com/user-attachments/assets/1485b4c8-0749-4a5e-9574-759a3d819d1e" height="120" width="auto" alt="Thoraxklinik HD"/><br />Thoraxklinik Heidelberg</a></td>
+  </tr>
+  <tr>
+    <td align="center"><a href="https://www.klinikum.uni-heidelberg.de/kliniken-institute/kliniken/diagnostische-und-interventionelle-radiologie/klinik-fuer-diagnostische-und-interventionelle-radiologie/"><img src="https://github.com/user-attachments/assets/6d7c402c-aeed-45db-a9dd-aad232128ef6" height="120" width="auto" alt="UK HD"/><br />Universitätsklinikum Heidelberg</a></td>
+  </tr>
+</table>
+
+> [!IMPORTANT]
+> ADIT is currently in early beta stage. While we are actively building and refining its features, users should anticipate ongoing updates and potential breaking changes as the platform evolves. We appreciate your understanding and welcome feedback to help us shape the future of ADIT.
+
 ## Features
 
 - Transfer DICOM data between DICOM-compatible servers
@@ -26,18 +52,6 @@ ADIT (Automated DICOM Transfer) is a Swiss army knife to exchange DICOM data bet
 
 [ADIT Client](https://github.com/openradx/adit-client) is a Python library to query, retrieve and upload DICOM images programmatically from a Python script. Thereby it can interact with DICOM (e.g. PACS) servers connected to an ADIT server.
 
-## Screenshots
-
-![Screenshot1](https://github.com/openradx/adit/assets/120626/f03d6af0-510f-4324-95f4-10bf8522fce2)
-
-![Screenshot2](https://github.com/openradx/adit/assets/120626/2b322dd9-0ce3-4e8f-9ca3-a10e00842c62)
-
-![Screenshot3](https://user-images.githubusercontent.com/120626/155511254-95adbed7-ef2e-44bd-aa3b-6e055be527a5.png)
-
-![Screenshot4](https://user-images.githubusercontent.com/120626/155511300-4dafe29f-748f-4d69-81af-89afe63197a0.png)
-
-![Screenshot5](https://user-images.githubusercontent.com/120626/155511342-e64cd37d-4e92-4a9a-bbb0-4e88ea136d3c.png)
-
 ## Architectural overview
 
 The backend of ADIT is built using the Django web framework, and data is stored in a PostgreSQL database. For DICOM transfer [pynetdicom](https://pydicom.github.io/pynetdicom/stable/) of the [pydicom](https://pydicom.github.io/) project is used.
@@ -48,16 +62,22 @@ When the DICOM data to transfer needs to be modified (e.g. pseudonymized) it is
 
 Downloading data from a DICOM server can done by using a DIMSE operation or by using DICOMweb REST calls. When using DIMSE operations C-GET is prioritized over C-MOVE as a worker can fetch the DICOM data directly from the server. When downloading data using a C-MOVE operation, ADIT commands the source DICOM server to send the data to a C-STORE SCP server of ADIT running in a separate container (`Receiver`) that receives the DICOM data and sends it back to the worker using a TCP Socket connection (`FileTransmitter`).
 
-## Contributors
+## Screenshots
+
+![Screenshot1](https://github.com/openradx/adit/assets/120626/f03d6af0-510f-4324-95f4-10bf8522fce2)
+
+![Screenshot2](https://github.com/openradx/adit/assets/120626/2b322dd9-0ce3-4e8f-9ca3-a10e00842c62)
 
-[![medihack](https://github.com/medihack.png?size=50)](https://github.com/medihack)
-[![mdebic](https://github.com/mdebic.png?size=50)](https://github.com/mdebic)
-[![hummerichsander](https://github.com/hummerichsander.png?size=50)](https://github.com/hummerichsander)
+![Screenshot3](https://user-images.githubusercontent.com/120626/155511254-95adbed7-ef2e-44bd-aa3b-6e055be527a5.png)
+
+![Screenshot4](https://user-images.githubusercontent.com/120626/155511300-4dafe29f-748f-4d69-81af-89afe63197a0.png)
+
+![Screenshot5](https://user-images.githubusercontent.com/120626/155511342-e64cd37d-4e92-4a9a-bbb0-4e88ea136d3c.png)
 
 ## Disclaimer
 
-ADIT is not a certified medical product. So use at your own risk.
+ADIT is intended for research purposes only and is not a certified medical device. It should not be used for clinical diagnostics, treatment, or any medical applications. Use this software at your own risk. The developers and contributors are not liable for any outcomes resulting from its use.
 
 ## License
 
-- AGPL 3.0 or later
+AGPL 3.0 or later

TODO.md

@@ -3,7 +3,6 @@
 ## Top
 
 - Delete VSCode stuff inside containers (I think its only when using the container itself as devcontainer)
-- When populate_data then also reset_orthancs
 - Study Date is changed when using Selective Transfer
 - Fix that ADIT DICOMweb supports STOW of multiple image files at once
   -- Currently it only allow to upload only one file after another

adit/batch_query/processors.py

@@ -4,7 +4,7 @@
 from adit.core.errors import DicomError
 from adit.core.models import DicomNode, DicomTask
 from adit.core.processors import DicomTaskProcessor
-from adit.core.types import ProcessingResult
+from adit.core.types import DicomLogEntry, ProcessingResult
 from adit.core.utils.dicom_dataset import QueryDataset, ResultDataset
 from adit.core.utils.dicom_operator import DicomOperator
 
@@ -24,27 +24,27 @@ def __init__(self, dicom_task: DicomTask) -> None:
         assert source.node_type == DicomNode.NodeType.SERVER
         self.operator = DicomOperator(source.dicomserver)
 
-    def process(self) -> ProcessingResult:
-        # TODO: Allow to get multiple patients (but provide a warning if there are multiple
-        # patients). We also have to use a for loop then. Make sure that users that provide
-        # only a patient name must provide a birth date as well.
-        # Also make sure that patient name doesn't contain wildcards.
+    def _get_logs(self) -> list[DicomLogEntry]:
+        logs: list[DicomLogEntry] = []
+        logs.extend(self.operator.get_logs())
+        logs.extend(self.logs)
+        return logs
 
-        patient = self._fetch_patient()
+    def process(self) -> ProcessingResult:
+        patients = self._fetch_patients()
 
         is_series_query = self.query_task.series_description or self.query_task.series_numbers
-
         if is_series_query:
-            results = self._query_series(patient.PatientID)
+            results = self._query_series([patient.PatientID for patient in patients])
             msg = f"{len(results)} series found"
         else:
-            results = self._query_studies(patient.PatientID)
+            results = self._query_studies([patient.PatientID for patient in patients])
             msg = f"{len(results)} stud{pluralize(len(results), 'y,ies')} found"
 
         BatchQueryResult.objects.bulk_create(results)
 
         status: BatchQueryTask.Status = BatchQueryTask.Status.SUCCESS
-        logs = self.operator.get_logs()
+        logs = self._get_logs()
         for log in logs:
             if log["level"] == "Warning":
                 status = BatchQueryTask.Status.WARNING
@@ -55,7 +55,7 @@ def process(self) -> ProcessingResult:
             "log": "\n".join([log["message"] for log in logs]),
         }
 
-    def _fetch_patient(self) -> ResultDataset:
+    def _fetch_patients(self) -> list[ResultDataset]:
         patient_id = self.query_task.patient_id
         patient_name = self.query_task.patient_name
         birth_date = self.query_task.patient_birth_date
@@ -65,32 +65,32 @@ def _fetch_patient(self) -> ResultDataset:
         # if those were provided beside the PatientID
         if patient_id:
             patients = list(self.operator.find_patients(QueryDataset.create(PatientID=patient_id)))
+            if len(patients) > 1:
+                raise DicomError("Multiple patients found for this PatientID.")
+            if len(patients) == 0:
+                raise DicomError("No patient found with this PatientID.")
+
+            # Some checks if they provide a PatientID and also a PatientName or PatientBirthDate.
+            assert len(patients) == 1
+            patient = patients[0]
+            # We can test for equality cause wildcards are not allowed during batch query
+            # (in contrast to selective transfer).
+            if patient_name and patient.PatientName != patient_name:
+                raise DicomError("PatientName doesn't match found patient by PatientID.")
+            if birth_date and patient.PatientBirthDate != birth_date:
+                raise DicomError("PatientBirthDate doesn't match found patient by PatientID.")
         elif patient_name and birth_date:
             patients = list(
                 self.operator.find_patients(
                     QueryDataset.create(PatientName=patient_name, PatientBirthDate=birth_date)
                 )
             )
+            if len(patients) == 0:
+                raise DicomError("No patient found with this PatientName and PatientBirthDate.")
         else:
-            raise DicomError("PatientName and PatientBirthDate are required.")
-
-        if len(patients) == 0:
-            raise DicomError("Patient not found.")
-
-        if len(patients) > 1:
-            raise DicomError("Multiple patients found.")
-
-        patient = patients[0]
+            raise DicomError("PatientID or PatientName and PatientBirthDate are required.")
 
-        # We can test for equality cause wildcards are not allowed during
-        # batch query (only in selective transfer)
-        if patient_id and patient_name and patient.PatientName != patient_name:
-            raise DicomError("PatientName doesn't match found patient by PatientID.")
-
-        if patient_id and birth_date and patient.PatientBirthDate != birth_date:
-            raise DicomError("PatientBirthDate doesn't match found patient by PatientID.")
-
-        return patient
+        return patients
 
     def _fetch_studies(self, patient_id: str) -> list[ResultDataset]:
         start_date = self.query_task.study_date_start
@@ -170,39 +170,21 @@ def _fetch_series(self, patient_id: str, study_uid: str) -> list[ResultDataset]:
 
         return sorted(series_results, key=lambda series: int(series.get("SeriesNumber", 0)))
 
-    def _query_studies(self, patient_id: str) -> list[BatchQueryResult]:
-        studies = self._fetch_studies(patient_id)
+    def _query_studies(self, patient_ids: list[str]) -> list[BatchQueryResult]:
         results: list[BatchQueryResult] = []
-        for study in studies:
-            batch_query_result = BatchQueryResult(
-                job=self.query_task.job,
-                query=self.query_task,
-                patient_id=study.PatientID,
-                patient_name=study.PatientName,
-                patient_birth_date=study.PatientBirthDate,
-                study_uid=study.StudyInstanceUID,
-                accession_number=study.AccessionNumber,
-                study_date=study.StudyDate,
-                study_time=study.StudyTime,
-                study_description=study.StudyDescription,
-                modalities=study.ModalitiesInStudy,
-                image_count=study.NumberOfStudyRelatedInstances,
-                pseudonym=self.query_task.pseudonym,
-                series_uid="",
-                series_description="",
-                series_number="",
-            )
-            results.append(batch_query_result)
-
-        return results
-
-    def _query_series(self, patient_id: str) -> list[BatchQueryResult]:
-        studies = self._fetch_studies(patient_id)
+        for patient_id in patient_ids:
+            studies = self._fetch_studies(patient_id)
+
+            if results and studies:
+                self.logs.append(
+                    {
+                        "level": "Warning",
+                        "title": "Indistinct patients",
+                        "message": "Studies of multiple patients were found for this query.",
+                    }
+                )
 
-        results: list[BatchQueryResult] = []
-        for study in studies:
-            series_list = self._fetch_series(patient_id, study.StudyInstanceUID)
-            for series in series_list:
+            for study in studies:
                 batch_query_result = BatchQueryResult(
                     job=self.query_task.job,
                     query=self.query_task,
@@ -214,13 +196,52 @@ def _query_series(self, patient_id: str) -> list[BatchQueryResult]:
                     study_date=study.StudyDate,
                     study_time=study.StudyTime,
                     study_description=study.StudyDescription,
-                    modalities=[series.Modality],
+                    modalities=study.ModalitiesInStudy,
                     image_count=study.NumberOfStudyRelatedInstances,
                     pseudonym=self.query_task.pseudonym,
-                    series_uid=series.SeriesInstanceUID,
-                    series_description=series.SeriesDescription,
-                    series_number=str(series.SeriesNumber),
+                    series_uid="",
+                    series_description="",
+                    series_number="",
                 )
                 results.append(batch_query_result)
 
         return results
+
+    def _query_series(self, patient_ids: list[str]) -> list[BatchQueryResult]:
+        results: list[BatchQueryResult] = []
+        for patient_id in patient_ids:
+            studies = self._fetch_studies(patient_id)
+
+            if results and studies:
+                self.logs.append(
+                    {
+                        "level": "Warning",
+                        "title": "Indistinct patients",
+                        "message": "Studies of multiple patients were found for this query.",
+                    }
+                )
+
+            for study in studies:
+                series_list = self._fetch_series(patient_id, study.StudyInstanceUID)
+                for series in series_list:
+                    batch_query_result = BatchQueryResult(
+                        job=self.query_task.job,
+                        query=self.query_task,
+                        patient_id=study.PatientID,
+                        patient_name=study.PatientName,
+                        patient_birth_date=study.PatientBirthDate,
+                        study_uid=study.StudyInstanceUID,
+                        accession_number=study.AccessionNumber,
+                        study_date=study.StudyDate,
+                        study_time=study.StudyTime,
+                        study_description=study.StudyDescription,
+                        modalities=[series.Modality],
+                        image_count=study.NumberOfStudyRelatedInstances,
+                        pseudonym=self.query_task.pseudonym,
+                        series_uid=series.SeriesInstanceUID,
+                        series_description=series.SeriesDescription,
+                        series_number=str(series.SeriesNumber),
+                    )
+                    results.append(batch_query_result)
+
+        return results