> For the complete documentation index, see [llms.txt](https://docs.rhinofcp.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.rhinofcp.com/rhino-sdk/calculating-sdk-metrics-on-dicom-studies-directly-via-a-dicomweb-connection.md).

# Calculating SDK metrics on DICOM studies directly via a DICOMWeb connection

You can use the `DicomwebQueryCreateInput` and `DicomwebQueryEndpoint` methods in the Rhino SDK to extract metrics directly from a Dicomweb server without extracting any image data or importing it into the Rhino client.

Below is some example code for how this workflow works:

### Import relevant libraries

```python
from rhino_health import RhinoSession
from rhino_health.lib.endpoints.dicomweb_query.dicomweb_query_endpoints import DicomwebQueryEndpoint
from rhino_health.lib.endpoints.dicomweb_query.dicomweb_query_dataclass import DicomwebQueryCreateInput
```

### Define Dicomweb connection parameters

```python
dicomweb_server_url="http://orthanc-external:4321/dicom-web/"
dicomweb_auth_credentials=dict(
    auth_type="basic_http",
    username="orthanc-user-external",
    password=getpass("DICOMweb Server Password:"),
)
```

### Define which studies to include in your query with `DicomwebQueryCreateInput`, then run your query with `DicomwebQueryEndpoint`

```python
create_query_input = DicomwebQueryCreateInput(
    project=project_uid,
    workgroup=workgroup_uid,
    dicomweb_server_url=dicomweb_server_url,
    dicomweb_auth_credentials=dicomweb_auth_credentials,
    dicomweb_query={"Study Instance UID": "1.2.156.14702.1.1000.16.0.19480514*"},
    dicom_object_level="Series",
    metric_definitions=[dict(\
        metric_name="count",\
        metric_params={},\
        request_arguments=dict(\
            variable="Modality",\
        ),\
    )],
)

query = DicomwebQueryEndpoint(session).run_query(create_query_input, timeout_seconds=30)

if query.results:
    for metric in query.results:
        print("Calculated Metric:", metric.calculated_metric)
        if metric.errors:
            print("Metric Calculation Errors:", metric.errors)
else:
    print("Errors:", query.errors)
```

**Example output:**

```
DICOMweb query status: Started
DICOMweb query status: Completed
Calculated Metric: {
    '': {'count': 250}
}
```

```python
create_query_input = DicomwebQueryCreateInput(
    project=project_uid,
    workgroup=workgroup_uid,
    dicomweb_server_url=dicomweb_server_url,
    dicomweb_auth_credentials=dicomweb_auth_credentials,
    dicomweb_query={},
    dicom_object_level="Instance",
    metric_definitions=[dict(\
        metric_name="count",\
        metric_params={},\
        request_arguments=dict(\
            variable="Modality",\
            group_by={"groupings": ["Modality", "PatientSex"]}\
        ),\
    )],
)

query = DicomwebQueryEndpoint(session).run_query(create_query_input, timeout_seconds=30)

if query.results:
    for metric in query.results:
        print("Calculated Metric:", metric.calculated_metric)
        if metric.errors:
            print("Metric Calculation Errors:", metric.errors)
else:
    print("Errors:", query.errors)
```

**Example output:**

```
DICOMweb query status: Started
DICOMweb query status: Completed
Calculated Metric: {
    "('Ultrasound', 'Male')": { 'count': 59 },
    "('Ultrasound', 'Female')": { 'count': 58 },
    "('Magnetic Resonance', 'Male')": { 'count': 57 },
    "('Computed Tomography', 'Male')": { 'count': 70 },
    "('Magnetic Resonance', 'Female')": { 'count': 61 },
    "('Computed Tomography', 'Female')": { 'count': 78 }
}
```

```python
create_query_input = DicomwebQueryCreateInput(
    project=project_uid,
    workgroup=workgroup_uid,
    dicomweb_server_url=dicomweb_server_url,
    dicomweb_auth_credentials=dicomweb_auth_credentials,
    dicomweb_query={},
    dicom_object_level="Series",
    metric_definitions=[\
        dict(\
            metric_name="mean",\
            metric_params={},\
            request_arguments=dict(\
                variable="PatientAge",\
                group_by={"groupings": ["PatientSex"]}\
            ),\
        ),\
        dict(\
            metric_name="std",\
            metric_params={},\
            request_arguments=dict(\
                variable="PatientAge",\
                group_by={"groupings": ["PatientSex"]}\
            ),\
        ),\
    ],
)

query = DicomwebQueryEndpoint(session).run_query(create_query_input, timeout_seconds=30)

if query.results:
    for metric in query.results:
        print("Calculated Metric:", metric.calculated_metric)
        if metric.errors:
            print("Metric Calculation Errors:", metric.errors)
else:
    print("Errors:", query.errors)
```

**Example output:**

```
DICOMweb query status: Started
DICOMweb query status: Completed
Calculated Metric: {
    'Male': {
        'mean': 46.65,
        'variable_count': 120
    },
    'Female': {
        'mean': 47.03076923076923,
        'variable_count': 130
    }
}
Calculated Metric: {
    'Male': {
        'std': 25.11130223624414,
        'variable_count': 120
    },
    'Female': {
        'std': 25.49447555890626,
        'variable_count': 130
    }
}
```


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