# BigQuery Guide for IDC

**Tested with:** `bigquery-public-data.idc_current` and idc-index 0.12.5 (IDC data version v24)

For most queries and downloads, use `idc-index` (see main SKILL.md). This guide covers BigQuery for advanced use cases requiring full DICOM metadata or complex joins.

## Prerequisites

**Requirements:**
1. Google account
2. Google Cloud project with billing enabled (first 1 TB/month free)
3. `google-cloud-bigquery` Python package or BigQuery console access

**Authentication setup:**
```bash
# Install Google Cloud SDK, then:
gcloud auth application-default login
```

## When to Use BigQuery

Use BigQuery instead of `idc-index` when you need:
- Full DICOM metadata (all 4000+ tags, not just the ~50 in idc-index)
- Complex joins across clinical data tables
- DICOM sequence attributes (nested structures)
- Queries on fields not in the idc-index mini-index
- Private DICOM elements (vendor-specific tags in OtherElements column)
- **Per-segment detail from DICOM Segmentation objects** — `idc-index` `seg_index` gives series-level metadata, but not individual segment anatomy codes; use `segmentations` BigQuery table to query by structure name
- **Quantitative measurements from DICOM SR** — radiomics features (volume, diameter, shape descriptors) without downloading and parsing SR files; no idc-index equivalent
- **Qualitative measurements from DICOM SR** — coded evaluations (malignancy rating, texture, margin) without parsing SR files; no idc-index equivalent

## Accessing IDC in BigQuery

### Dataset Structure

All IDC tables are in the `bigquery-public-data` BigQuery project.

**Current version (recommended for exploration):**
- `bigquery-public-data.idc_current.*`
- `bigquery-public-data.idc_current_clinical.*`

**Versioned datasets (recommended for reproducibility):**

- `bigquery-public-data.idc_v{IDC version}.*`
- `bigquery-public-data.idc_v{IDC version}_clinical.*`

Always use versioned datasets for reproducible research!

## Key Tables

### dicom_all
Primary table joining complete DICOM metadata with IDC-specific columns (collection_id, gcs_url, license). Contains all DICOM tags from `dicom_metadata` plus collection and administrative metadata. See [dicom_all.sql](https://github.com/ImagingDataCommons/etl_flow/blob/master/bq/generate_tables_and_views/derived_tables/BQ_Table_Building/derived_data_views/sql/dicom_all.sql) for the exact derivation.

```sql
SELECT 
  collection_id,
  PatientID,
  StudyInstanceUID, 
  SeriesInstanceUID,
  Modality,
  BodyPartExamined,
  SeriesDescription,
  gcs_url,
  license_short_name
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE Modality = 'CT'
  AND BodyPartExamined = 'CHEST'
LIMIT 10
```

### Derived Tables

These tables are derived from DICOM objects (Segmentation and Structured Report) and have **no equivalent in idc-index**. Use them to query per-segment anatomy, radiomics features, and qualitative assessments without downloading DICOM files.

**segmentations** — one row per segment within a DICOM SEG object. Lets you search by anatomical structure name or DICOM coded concept. The `idc-index` `seg_index` gives series-level metadata; this table gives per-segment detail.

**measurement_groups** — one row per SR TID1500 measurement group. The parent grouping for quantitative and qualitative measurements; links measurements to segmentations and source images.

**quantitative_measurements** — one row per numeric measurement within an SR TID1500 group. Contains radiomics features (volume, diameter, shape descriptors, texture) extracted from DICOM SR without downloading or parsing SR files.

**qualitative_measurements** — one row per coded evaluation within an SR TID1500 group. Contains assessed findings (malignancy likelihood, texture, margin type) using coded concept values.

See the [Derived Tables: Detailed Documentation](#derived-tables-detailed-documentation) section below for schemas, column descriptions, and query examples.

### Collection Metadata

**original_collections_metadata** - Collection-level descriptions

```sql
SELECT
  collection_id,
  CancerTypes,
  TumorLocations,
  Subjects,
  src.source_doi,
  src.ImageTypes,
  src.license.license_short_name
FROM `bigquery-public-data.idc_current.original_collections_metadata`,
UNNEST(Sources) AS src
WHERE CancerTypes LIKE '%Lung%'
```

## Common Query Patterns

### Find Collections by Criteria

```sql
SELECT 
  collection_id,
  COUNT(DISTINCT PatientID) as patient_count,
  COUNT(DISTINCT SeriesInstanceUID) as series_count,
  ARRAY_AGG(DISTINCT Modality) as modalities
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE BodyPartExamined LIKE '%BRAIN%'
GROUP BY collection_id
HAVING patient_count > 50
ORDER BY patient_count DESC
```

### Get Download URLs

```sql
SELECT
  SeriesInstanceUID,
  gcs_url
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE collection_id = 'rider_pilot'
  AND Modality = 'CT'
```

### Find Studies with Multiple Modalities

```sql
SELECT
  StudyInstanceUID,
  ARRAY_AGG(DISTINCT Modality) as modalities,
  COUNT(DISTINCT SeriesInstanceUID) as series_count
FROM `bigquery-public-data.idc_current.dicom_all`
GROUP BY StudyInstanceUID
HAVING ARRAY_LENGTH(ARRAY_AGG(DISTINCT Modality)) > 1
LIMIT 100
```

### License Filtering

```sql
SELECT
  collection_id,
  license_short_name,
  COUNT(*) as instance_count
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE license_short_name = 'CC BY 4.0'
GROUP BY collection_id, license_short_name
```

### Find Segmentations with Source Images

```sql
SELECT
  src.collection_id,
  seg.SeriesInstanceUID as seg_series,
  seg.SegmentedPropertyType,
  src.SeriesInstanceUID as source_series,
  src.Modality as source_modality
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN `bigquery-public-data.idc_current.dicom_all` src
  ON seg.segmented_SeriesInstanceUID = src.SeriesInstanceUID
WHERE src.collection_id = 'qin_prostate_repeatability'
LIMIT 10
```

## Derived Tables: Detailed Documentation

### segmentations

One row per segment within a DICOM Segmentation (SEG) object. Unlike `idc-index` `seg_index` (one row per SEG series), this table exposes each labeled region individually so you can search by anatomical structure or finding type.

**Key columns:**

| Column | Type | Description |
|--------|------|-------------|
| `SeriesInstanceUID` | STRING | SEG series UID |
| `SOPInstanceUID` | STRING | SEG instance UID |
| `PatientID` | STRING | Patient identifier |
| `StudyInstanceUID` | STRING | Study UID |
| `SegmentNumber` | INTEGER | Segment index within the SEG (starting from 1) |
| `SegmentedPropertyCategory` | RECORD | Coded category (e.g., "Anatomical Structure", "Morphologically Altered Structure") |
| `SegmentedPropertyType` | RECORD | Specific structure (e.g., "Liver", "Kidney", "Neoplasm") |
| `AnatomicRegion` | RECORD | Optional anatomic region modifier |
| `SegmentAlgorithmType` | STRING | AUTOMATIC, SEMIAUTOMATIC, or MANUAL |
| `SegmentAlgorithmName` | STRING (REPEATED) | Algorithm name array (e.g., ["TotalSegmentator"]) |
| `TrackingUID` | STRING | Links segment to SR measurements |
| `TrackingID` | STRING | Human-readable tracking label |
| `segmented_SeriesInstanceUID` | STRING | Source image series UID — join to `dicom_all` to get collection/modality |
| `viewer_url` | STRING | Direct IDC viewer link for the SEG |

`SegmentedPropertyCategory` and `SegmentedPropertyType` are RECORD types with sub-fields `CodeValue`, `CodingSchemeDesignator`, and `CodeMeaning`. Use `.CodeMeaning` for human-readable filtering.

**idc-index gap:** `seg_index` in idc-index has `total_segments`, `AlgorithmName`, and aggregated codes, but does not expose individual segment anatomy per row. Use this BigQuery table when you need to find SEG series that contain a specific structure (e.g., all series with a "Liver" segment).

**Discover what structures are segmented across IDC:**

```sql
SELECT
  SegmentedPropertyCategory.CodeMeaning AS category,
  SegmentedPropertyType.CodeMeaning AS structure,
  SegmentAlgorithmType,
  COUNT(DISTINCT SeriesInstanceUID) AS seg_series_count
FROM `bigquery-public-data.idc_current.segmentations`
GROUP BY 1, 2, 3
ORDER BY seg_series_count DESC
LIMIT 20
```

**Find all SEG series containing a specific structure, with source image context:**

```sql
SELECT
  seg.SeriesInstanceUID AS seg_series,
  seg.SegmentNumber,
  seg.SegmentedPropertyType.CodeMeaning AS structure,
  seg.SegmentAlgorithmType,
  seg.SegmentAlgorithmName,
  img.collection_id,
  img.PatientID,
  img.Modality,
  seg.viewer_url
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN `bigquery-public-data.idc_current.dicom_all` img
  ON seg.segmented_SeriesInstanceUID = img.SeriesInstanceUID
WHERE seg.SegmentedPropertyType.CodeMeaning = 'Liver'
  AND seg.SegmentAlgorithmType = 'AUTOMATIC'
LIMIT 20
```

**Find all segment types present in a collection:**

```sql
SELECT
  seg.SegmentedPropertyType.CodeMeaning AS structure,
  seg.SegmentAlgorithmType,
  COUNT(DISTINCT seg.SeriesInstanceUID) AS seg_series_count
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN `bigquery-public-data.idc_current.dicom_all` img
  ON seg.segmented_SeriesInstanceUID = img.SeriesInstanceUID
WHERE img.collection_id = 'nlst'
GROUP BY 1, 2
ORDER BY seg_series_count DESC
```

**Link segments to SR measurements using TrackingUID:**

```sql
-- Find segments that have corresponding SR measurements
SELECT
  seg.SeriesInstanceUID AS seg_series,
  seg.SegmentNumber,
  seg.SegmentedPropertyType.CodeMeaning AS structure,
  qm.Quantity.CodeMeaning AS measurement,
  ROUND(CAST(qm.Value AS FLOAT64), 2) AS value,
  qm.Units.CodeMeaning AS units
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN `bigquery-public-data.idc_current.quantitative_measurements` qm
  ON seg.SeriesInstanceUID = qm.segmentationSeriesUID
  AND seg.SegmentNumber = qm.segmentationSegmentNumber
WHERE seg.SegmentedPropertyType.CodeMeaning = 'Neoplasm'
  AND qm.Quantity.CodeMeaning = 'Volume from Voxel Summation'
LIMIT 10
```

---

### quantitative_measurements

One row per numeric measurement in a DICOM SR TID1500 Measurement Report. Contains radiomics features (shape, intensity, texture) and clinical measurements (volume, diameter, SUV). These measurements are pre-extracted from SR — no download or DICOM parsing needed.

**No idc-index equivalent.** This table is only accessible via BigQuery.

**Key columns:**

| Column | Type | Description |
|--------|------|-------------|
| `SOPInstanceUID` | STRING | SR instance UID |
| `SeriesInstanceUID` | STRING | SR series UID — join to `dicom_all` for collection/modality |
| `SeriesDescription` | STRING | SR series description (e.g., "TotalSegmentator(v1.5.6) shape Measurements") |
| `PatientID` | STRING | Patient identifier |
| `measurementGroup_number` | INTEGER | Group index within the SR (0-based); join key with `measurement_groups` and `qualitative_measurements` |
| `Quantity` | RECORD | What was measured — `CodeValue`, `CodingSchemeDesignator`, `CodeMeaning` (e.g., "Volume from Voxel Summation") |
| `Value` | NUMERIC | The numeric measurement value |
| `Units` | RECORD | Units — `CodeMeaning` (e.g., "cubic millimeter", "no units", "Hounsfield Unit") |
| `derivationModifier` | RECORD | How the value was derived (e.g., "Mean", "Minimum", "Maximum") |
| `lateralityModifier` | RECORD | Laterality qualifier |
| `finding` | RECORD | What finding was measured — `CodeMeaning` (e.g., "Nodule", "Organ", "Anatomical Structure") |
| `findingSite` | RECORD | Where the finding is — `CodeMeaning` (e.g., "Liver", "Esophagus", "Lung") |
| `trackingIdentifier` | STRING | Human-readable tracking label (e.g., "Nodule 1", "Measurements group 26") |
| `trackingUniqueIdentifier` | STRING | Tracking UID — links back to `segmentations.TrackingUID` |
| `segmentationInstanceUID` | STRING | SOPInstanceUID of the referenced SEG object |
| `segmentationSeriesUID` | STRING | SeriesInstanceUID of the referenced SEG object |
| `segmentationSegmentNumber` | INTEGER | Segment number within the SEG — join to `segmentations.SegmentNumber` |
| `sourceSegmentedSeriesUID` | STRING | Source image series — join to `dicom_all.SeriesInstanceUID` |

**Discover available measurement types:**

```sql
SELECT
  Quantity.CodeMeaning AS measurement,
  Units.CodeMeaning AS units,
  COUNT(*) AS measurement_count,
  COUNT(DISTINCT SeriesInstanceUID) AS sr_series_count
FROM `bigquery-public-data.idc_current.quantitative_measurements`
GROUP BY 1, 2
ORDER BY measurement_count DESC
LIMIT 20
```

**Query measurements for a specific structure (e.g., liver volume across collections):**

```sql
SELECT
  qm.PatientID,
  ROUND(CAST(qm.Value AS FLOAT64) / 1000, 1) AS volume_cm3,
  img.collection_id,
  qm.segmentationSeriesUID
FROM `bigquery-public-data.idc_current.quantitative_measurements` qm
JOIN `bigquery-public-data.idc_current.dicom_all` img
  ON qm.sourceSegmentedSeriesUID = img.SeriesInstanceUID
WHERE qm.Quantity.CodeMeaning = 'Volume from Voxel Summation'
  AND qm.findingSite.CodeMeaning = 'Liver'
ORDER BY volume_cm3 DESC
LIMIT 20
```

**Retrieve all measurements for a specific patient and finding:**

```sql
SELECT
  qm.measurementGroup_number,
  qm.finding.CodeMeaning AS finding,
  qm.findingSite.CodeMeaning AS finding_site,
  qm.lateralityModifier.CodeMeaning AS laterality,
  qm.Quantity.CodeMeaning AS feature,
  ROUND(CAST(qm.Value AS FLOAT64), 3) AS value,
  qm.Units.CodeMeaning AS units
FROM `bigquery-public-data.idc_current.quantitative_measurements` qm
WHERE qm.PatientID = 'LIDC-IDRI-0001'
  AND qm.finding.CodeMeaning = 'Nodule'
ORDER BY qm.measurementGroup_number, qm.Quantity.CodeMeaning
```

---

### qualitative_measurements

One row per coded evaluation in a DICOM SR TID1500 Measurement Report. Instead of numeric values, these record assessed characteristics using coded concept pairs (e.g., Quantity="Malignancy", Value="4 out of 5 (Moderately Suspicious for Cancer)").

**No idc-index equivalent.** This table is only accessible via BigQuery.

**Key columns:**

| Column | Type | Description |
|--------|------|-------------|
| `SOPInstanceUID` | STRING | SR instance UID |
| `SeriesInstanceUID` | STRING | SR series UID — join to `dicom_all` for collection/modality |
| `PatientID` | STRING | Patient identifier |
| `measurementGroup_number` | INTEGER | Group index within the SR — join key with `quantitative_measurements` |
| `Quantity` | RECORD | What was assessed — `CodeMeaning` (e.g., "Malignancy", "Calcification", "Texture") |
| `Value` | RECORD | The coded answer — `CodeMeaning` (e.g., "4 out of 5 (Moderately Suspicious for Cancer)") |
| `finding` | RECORD | What finding was assessed — `CodeMeaning` (e.g., "Nodule") |
| `findingSite` | RECORD | Anatomic site — `CodeMeaning` (e.g., "Lung") |
| `trackingIdentifier` | STRING | Human-readable tracking label |
| `segmentationInstanceUID` | STRING | SOPInstanceUID of the referenced SEG object |
| `segmentationSeriesUID` | STRING | SeriesInstanceUID of the referenced SEG object |
| `segmentationSegmentNumber` | INTEGER | Segment number within the referenced SEG |
| `sourceSegmentedSeriesUID` | STRING | Source image series — join to `dicom_all.SeriesInstanceUID` |

**Discover available qualitative features and their values:**

```sql
SELECT
  Quantity.CodeMeaning AS feature,
  Value.CodeMeaning AS assessed_value,
  finding.CodeMeaning AS finding,
  COUNT(*) AS count
FROM `bigquery-public-data.idc_current.qualitative_measurements`
GROUP BY 1, 2, 3
ORDER BY count DESC
LIMIT 20
```

**Find all nodules with a specific malignancy rating:**

```sql
SELECT
  qm.PatientID,
  qm.trackingIdentifier AS nodule_id,
  qm.Value.CodeMeaning AS malignancy_rating,
  img.collection_id
FROM `bigquery-public-data.idc_current.qualitative_measurements` qm
JOIN `bigquery-public-data.idc_current.dicom_all` img
  ON qm.SeriesInstanceUID = img.SeriesInstanceUID
WHERE qm.Quantity.CodeMeaning = 'Malignancy'
  AND qm.Value.CodeMeaning LIKE '%Suspicious%'
ORDER BY qm.PatientID
LIMIT 20
```

---

### measurement_groups

The parent table for TID1500 measurement groups. Each row represents one measurement group within an SR, with references to the segmentation and source image but without the individual measurement values. Use this table when you need to enumerate groups or check what was tracked, without pulling all measurement values.

**Key columns:** `SOPInstanceUID`, `SeriesInstanceUID`, `PatientID`, `measurementGroup_number`, `trackingIdentifier`, `trackingUniqueIdentifier`, `finding`, `findingSite`, `segmentationInstanceUID`, `segmentationSeriesUID`, `segmentationSegmentNumber`, `sourceSegmentedSeriesUID`, `contentSequence` (raw SR content sequence).

In most workflows, join `quantitative_measurements` and `qualitative_measurements` directly using `SOPInstanceUID` + `measurementGroup_number` rather than going through `measurement_groups`.

---

### Combining quantitative and qualitative measurements

The primary use case requiring both tables: correlate numeric features (volume, diameter) with coded assessments (malignancy, texture) for the same finding. Join on `SOPInstanceUID` + `measurementGroup_number`.

**Example: LIDC-IDRI lung nodule analysis — malignancy rating with volume and diameter:**

```sql
SELECT
  qual.PatientID,
  qual.trackingIdentifier AS nodule_id,
  qual.Value.CodeMeaning AS malignancy_rating,
  ROUND(CAST(vol.Value AS FLOAT64), 1) AS volume_mm3,
  ROUND(CAST(diam.Value AS FLOAT64), 1) AS diameter_mm
FROM `bigquery-public-data.idc_current.qualitative_measurements` qual
JOIN `bigquery-public-data.idc_current.quantitative_measurements` vol
  ON qual.SOPInstanceUID = vol.SOPInstanceUID
  AND qual.measurementGroup_number = vol.measurementGroup_number
JOIN `bigquery-public-data.idc_current.quantitative_measurements` diam
  ON qual.SOPInstanceUID = diam.SOPInstanceUID
  AND qual.measurementGroup_number = diam.measurementGroup_number
WHERE qual.Quantity.CodeMeaning = 'Malignancy'
  AND vol.Quantity.CodeMeaning = 'Volume'
  AND diam.Quantity.CodeMeaning = 'Diameter'
ORDER BY qual.PatientID, qual.trackingIdentifier
LIMIT 20
```

**Joining all three derived tables to get full segment context:**

```sql
SELECT
  seg.SegmentedPropertyType.CodeMeaning AS structure,
  qual.Quantity.CodeMeaning AS qualitative_feature,
  qual.Value.CodeMeaning AS qualitative_value,
  qm.Quantity.CodeMeaning AS quantitative_feature,
  ROUND(CAST(qm.Value AS FLOAT64), 3) AS numeric_value,
  qm.Units.CodeMeaning AS units,
  img.collection_id
FROM `bigquery-public-data.idc_current.segmentations` seg
JOIN `bigquery-public-data.idc_current.qualitative_measurements` qual
  ON seg.SeriesInstanceUID = qual.segmentationSeriesUID
  AND seg.SegmentNumber = qual.segmentationSegmentNumber
JOIN `bigquery-public-data.idc_current.quantitative_measurements` qm
  ON qual.SOPInstanceUID = qm.SOPInstanceUID
  AND qual.measurementGroup_number = qm.measurementGroup_number
JOIN `bigquery-public-data.idc_current.dicom_all` img
  ON seg.segmented_SeriesInstanceUID = img.SeriesInstanceUID
WHERE seg.SegmentedPropertyType.CodeMeaning = 'Neoplasm'
LIMIT 10
```

## Private DICOM Elements

Private DICOM elements are vendor-specific attributes not defined in the DICOM standard. They often contain essential acquisition parameters (like diffusion b-values, gradient directions, or scanner-specific settings) that are critical for image interpretation and analysis.

### Understanding Private Elements

**How private elements work:**
- Private elements use odd-numbered group numbers (e.g., 0019, 0043, 2001)
- Each vendor reserves blocks of 256 elements using Private Creator identifiers at positions (gggg,0010-00FF)
- For example, GE uses Private Creator "GEMS_PARM_01" at (0043,0010) to reserve elements (0043,1000-10FF)

**Standard vs. private tags:** Some parameters exist in both forms:
| Parameter | Standard Tag | GE | Siemens | Philips |
|-----------|--------------|-----|---------|---------|
| Diffusion b-value | (0018,9087) | (0043,1039) | (0019,100C) | (2001,1003) |
| Private Creator | - | GEMS_PARM_01 | SIEMENS CSA HEADER | Philips Imaging |

Older scanners typically populate only private tags; newer scanners may use standard tags. Always check both.

**Challenges with private elements:**
- Require manufacturer DICOM Conformance Statements to interpret
- Tag meanings can change between software versions
- May be removed during de-identification for HIPAA compliance
- Value encoding varies (string vs. numeric, different units)

### Accessing Private Elements in BigQuery

Private elements are stored in the `OtherElements` column of `dicom_all` as an array of structs with `Tag` and `Data` fields.

**Tag notation:** DICOM notation (0043,1039) becomes BigQuery format `Tag_00431039`.

### Private Element Query Patterns

#### Discover Available Private Tags

List all non-empty private tags for a collection:

```sql
SELECT
  other_elements.Tag,
  COUNT(*) AS instance_count,
  ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)] IGNORE NULLS LIMIT 5) AS sample_values
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE collection_id = 'qin_prostate_repeatability'
  AND Modality = 'MR'
  AND ARRAY_LENGTH(other_elements.Data) > 0
  AND other_elements.Data[SAFE_OFFSET(0)] IS NOT NULL
  AND other_elements.Data[SAFE_OFFSET(0)] != ''
GROUP BY other_elements.Tag
ORDER BY instance_count DESC
```

For a specific series:

```sql
SELECT
  other_elements.Tag,
  ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)] IGNORE NULLS) AS values
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE SeriesInstanceUID = '1.3.6.1.4.1.14519.5.2.1.7311.5101.206828891270520544417996275680'
  AND ARRAY_LENGTH(other_elements.Data) > 0
  AND other_elements.Data[SAFE_OFFSET(0)] IS NOT NULL
  AND other_elements.Data[SAFE_OFFSET(0)] != ''
GROUP BY other_elements.Tag
```

To identify the Private Creator for a tag, look for the reservation element in the same group. For example, if you find `Tag_00431039`, the Private Creator is at `Tag_00430010` (the tag that reserves block 10xx in group 0043).

#### Identify Equipment Manufacturer

Determine what equipment produced the data to find the correct DICOM Conformance Statement:

```sql
SELECT DISTINCT Manufacturer, ManufacturerModelName
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE collection_id = 'qin_prostate_repeatability'
  AND Modality = 'MR'
```

#### Access Private Element Values

Use `UNNEST` to access individual private elements:

```sql
SELECT
  SeriesInstanceUID,
  SeriesDescription,
  other_elements.Data[SAFE_OFFSET(0)] AS b_value
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE collection_id = 'qin_prostate_repeatability'
  AND other_elements.Tag = 'Tag_00431039'
LIMIT 10
```

#### Aggregate Values by Series

Collect all unique values across slices in a series:

```sql
SELECT
  SeriesInstanceUID,
  ANY_VALUE(SeriesDescription) AS SeriesDescription,
  ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)]) AS b_values
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE collection_id = 'qin_prostate_repeatability'
  AND other_elements.Tag = 'Tag_00431039'
GROUP BY SeriesInstanceUID
```

#### Combine Standard and Private Filters

Filter using both standard DICOM attributes and private element values:

```sql
SELECT
  PatientID,
  SeriesInstanceUID,
  ANY_VALUE(SeriesDescription) AS SeriesDescription,
  ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)]) AS b_values,
  COUNT(DISTINCT SOPInstanceUID) AS n_slices
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE collection_id = 'qin_prostate_repeatability'
  AND Modality = 'MR'
  AND other_elements.Tag = 'Tag_00431039'
  AND ImageType[SAFE_OFFSET(0)] = 'ORIGINAL'
  AND other_elements.Data[SAFE_OFFSET(0)] = '1400'
GROUP BY PatientID, SeriesInstanceUID
ORDER BY PatientID
```

#### Cross-Collection Analysis

Survey usage of a private tag across all IDC collections:

```sql
SELECT
  collection_id,
  ARRAY_TO_STRING(ARRAY_AGG(DISTINCT other_elements.Data[SAFE_OFFSET(0)] IGNORE NULLS), ', ') AS values_found,
  ARRAY_AGG(DISTINCT Manufacturer IGNORE NULLS) AS manufacturers
FROM `bigquery-public-data.idc_current.dicom_all`,
  UNNEST(OtherElements) AS other_elements
WHERE other_elements.Tag = 'Tag_00431039'
  AND other_elements.Data[SAFE_OFFSET(0)] IS NOT NULL
  AND other_elements.Data[SAFE_OFFSET(0)] != ''
GROUP BY collection_id
ORDER BY collection_id
```

### Workflow: Finding and Using Private Tags

1. **Discover available private tags** in your collection using the discovery query above
2. **Identify the manufacturer** to know which conformance statement to consult
3. **Find the DICOM Conformance Statement** from the manufacturer's website (see Resources below)
4. **Search the conformance statement** for the parameter you need (e.g., "b_value", "gradient") to understand what each tag contains
5. **Convert tag to BigQuery format:** (gggg,eeee) → `Tag_ggggeeee`
6. **Query and verify** results visually in the IDC Viewer

### Data Quality Notes

- Some collections show unrealistic values (e.g., b-value "1000000600") indicating encoding issues or different conventions
- IDC data is de-identified; private tags containing PHI may have been removed or modified
- The same tag may have different meanings across software versions
- Always verify query results visually using the [IDC Viewer](https://viewer.imaging.datacommons.cancer.gov/) before large-scale analysis

### Private Element Resources

**Manufacturer DICOM Conformance Statements:**
- [GE Healthcare MR](https://www.gehealthcare.com/products/interoperability/dicom/magnetic-resonance-imaging-dicom-conformance-statements)
- [Siemens MR](https://www.siemens-healthineers.com/services/it-standards/dicom-conformance-statements-magnetic-resonance)
- [Siemens CT](https://www.siemens-healthineers.com/services/it-standards/dicom-conformance-statements-computed-tomography)

**DICOM Standard:**
- [Part 5 Section 7.8 - Private Data Elements](https://dicom.nema.org/medical/dicom/current/output/chtml/part05/sect_7.8.html)
- [Part 15 Appendix E - De-identification Profiles](https://dicom.nema.org/medical/dicom/current/output/chtml/part15/chapter_e.html)

**Community Resources:**
- [NAMIC Wiki: DWI/DTI DICOM](https://web.archive.org/web/20260520044207/https://www.na-mic.org/wiki/NAMIC_Wiki:DTI:DICOM_for_DWI_and_DTI) - comprehensive vendor comparison for diffusion imaging (archived; original NA-MIC wiki retired)
- [StandardizeBValue](https://github.com/nslay/StandardizeBValue) - tool to extract vendor b-values to standard tags

## Using Query Results with idc-index

Combine BigQuery for complex queries with idc-index for downloads (no GCP auth needed for downloads):

```python
from google.cloud import bigquery
from idc_index import IDCClient

# Initialize BigQuery client
# Requires: the Python google-cloud-bigquery package
# Auth: gcloud auth application-default login
# Project: needed for billing even on public datasets (free tier applies)
bq_client = bigquery.Client(project="your-gcp-project-id")

# Query for series with specific criteria
query = """
SELECT DISTINCT SeriesInstanceUID
FROM `bigquery-public-data.idc_current.dicom_all`
WHERE collection_id = 'tcga_luad'
  AND Modality = 'CT'
  AND Manufacturer = 'GE MEDICAL SYSTEMS'
LIMIT 100
"""

df = bq_client.query(query).to_dataframe()
print(f"Found {len(df)} GE CT series")

# Download with idc-index (no GCP auth required)
idc_client = IDCClient()
idc_client.download_from_selection(
    seriesInstanceUID=list(df['SeriesInstanceUID'].values),
    downloadDir="./tcga_luad_thin_ct"
)
```

## Cost and Optimization

**Pricing:** $5 per TB scanned (first 1 TB/month free). Most users stay within free tier.

**Minimize data scanned:**
- Select only needed columns (not `SELECT *`)
- Filter early with `WHERE` clauses
- Use `LIMIT` when testing
- Use `dicom_all` instead of `dicom_metadata` when possible (smaller)
- Preview queries in BQ console (free, shows bytes to scan)

**Check cost before running:**
```python
query_job = client.query(query, job_config=bigquery.QueryJobConfig(dry_run=True))
print(f"Query will scan {query_job.total_bytes_processed / 1e9:.2f} GB")
```

**Use materialized tables:** IDC provides both views (`table_name_view`) and materialized tables (`table_name`). Always use the materialized tables (faster, lower cost).

## Clinical Data

Clinical data is in separate datasets with collection-specific tables. All clinical data available via `idc-index` is also available in BigQuery, with the same content and structure. Use BigQuery when you need complex cross-collection queries or joins that aren't possible with the local `idc-index` tables.

**Datasets:**
- `bigquery-public-data.idc_current_clinical` - current release (for exploration)
- `bigquery-public-data.idc_v{version}_clinical` - versioned datasets (for reproducibility)

Currently there are ~130 clinical tables representing ~70 collections. Not all collections have clinical data (started in IDC v11).

### Clinical Table Naming

Most collections use a single table: `<collection_id>_clinical`

**Exception:** ACRIN collections use multiple tables for different data types (e.g., `acrin_6698_A0`, `acrin_6698_A1`, etc.).

### Metadata Tables

Two metadata tables help navigate clinical data:

**table_metadata** - Collection-level information:
```sql
SELECT
  collection_id,
  table_name,
  table_description
FROM `bigquery-public-data.idc_current_clinical.table_metadata`
WHERE collection_id = 'nlst'
```

**column_metadata** - Attribute-level details with value mappings:
```sql
SELECT
  collection_id,
  table_name,
  column,
  column_label,
  data_type,
  values
FROM `bigquery-public-data.idc_current_clinical.column_metadata`
WHERE collection_id = 'nlst'
  AND column_label LIKE '%stage%'
```

The `values` field contains observed attribute values with their descriptions (same as in `idc-index` clinical_index).

### Common Clinical Queries

**List available clinical tables:**
```sql
SELECT table_name
FROM `bigquery-public-data.idc_current_clinical.INFORMATION_SCHEMA.TABLES`
WHERE table_name NOT IN ('table_metadata', 'column_metadata')
```

**Find collections with specific clinical attributes:**
```sql
SELECT DISTINCT collection_id, table_name, column, column_label
FROM `bigquery-public-data.idc_current_clinical.column_metadata`
WHERE LOWER(column_label) LIKE '%chemotherapy%'
```

**Query clinical data for a collection:**
```sql
-- Example: NLST cancer staging data
SELECT
  dicom_patient_id,
  clinical_stag,
  path_stag,
  de_stag
FROM `bigquery-public-data.idc_current_clinical.nlst_canc`
WHERE clinical_stag IS NOT NULL
LIMIT 10
```

**Join clinical with imaging data:**
```sql
SELECT
  d.PatientID,
  d.StudyInstanceUID,
  d.Modality,
  c.clinical_stag,
  c.path_stag
FROM `bigquery-public-data.idc_current.dicom_all` d
JOIN `bigquery-public-data.idc_current_clinical.nlst_canc` c
  ON d.PatientID = c.dicom_patient_id
WHERE d.collection_id = 'nlst'
  AND d.Modality = 'CT'
  AND c.clinical_stag = '400'  -- Stage IV
LIMIT 20
```

**Cross-collection clinical search:**
```sql
-- Find all collections with staging information
SELECT
  cm.collection_id,
  cm.table_name,
  cm.column,
  cm.column_label
FROM `bigquery-public-data.idc_current_clinical.column_metadata` cm
WHERE LOWER(cm.column_label) LIKE '%stage%'
ORDER BY cm.collection_id
```

### Key Column: dicom_patient_id

Every clinical table includes `dicom_patient_id`, which matches the DICOM `PatientID` attribute in imaging tables. This is the join key between clinical and imaging data.

**Note:** Clinical table schemas vary significantly by collection. Always check available columns first:
```sql
SELECT column_name, data_type
FROM `bigquery-public-data.idc_current_clinical.INFORMATION_SCHEMA.COLUMNS`
WHERE table_name = 'nlst_canc'
```

See `references/clinical_data_guide.md` for detailed workflows using `idc-index`, which provides the same clinical data without requiring BigQuery authentication.

## Important Notes

- Tables are read-only (public dataset)
- Schema changes between IDC versions
- Use versioned datasets for reproducibility
- Some DICOM sequences >15 levels deep are not extracted
- Very large sequences (>1MB) may be truncated
- Always check data license before use

## Common Errors

**Issue: Billing must be enabled**
- Cause: BigQuery requires a billing-enabled GCP project
- Solution: Enable billing in Google Cloud Console or use idc-index mini-index instead

**Issue: Query exceeds resource limits**
- Cause: Query scans too much data or is too complex
- Solution: Add more specific WHERE filters, use LIMIT, break into smaller queries

**Issue: Column not found**
- Cause: Field name typo or not in selected table
- Solution: Check table schema first with `INFORMATION_SCHEMA.COLUMNS`

**Issue: Permission denied**
- Cause: Not authenticated to Google Cloud
- Solution: Run `gcloud auth application-default login` or set GOOGLE_APPLICATION_CREDENTIALS

## Resources

- [Understanding the BigQuery DICOM schema](https://docs.cloud.google.com/healthcare-api/docs/how-tos/dicom-bigquery-schema)
- [BigQuery Query Syntax](https://docs.cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax)
- [Kaggle Intro to SQL](https://www.kaggle.com/learn/intro-to-sql)
- [Sample BigQuery queries of IDC data](https://github.com/ImagingDataCommons/idc-bigquery-cookbook)