K-Dense-AI · fedorov · May 8, 2026 · May 10, 2026
diff --git a/scientific-skills/imaging-data-commons/SKILL.md b/scientific-skills/imaging-data-commons/SKILL.md
diff --git a/scientific-skills/imaging-data-commons/references/bigquery_guide.md b/scientific-skills/imaging-data-commons/references/bigquery_guide.md
@@ -1,6 +1,6 @@
 # BigQuery Guide for IDC
 
-**Tested with:** IDC data version v23
+**Tested with:** idc-index 0.12.1 (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.
 

diff --git a/scientific-skills/imaging-data-commons/references/clinical_data_guide.md b/scientific-skills/imaging-data-commons/references/clinical_data_guide.md
@@ -1,6 +1,6 @@
 # Clinical Data Guide for IDC
 
-**Tested with:** idc-index 0.11.7 (IDC data version v23)
+**Tested with:** idc-index 0.12.1 (IDC data version v24)
 
 Clinical data (demographics, diagnoses, therapies, lab tests, staging) accompanies many IDC imaging collections. This guide covers how to discover, access, and integrate clinical data with imaging data using `idc-index`.
 

diff --git a/scientific-skills/imaging-data-commons/references/cloud_storage_guide.md b/scientific-skills/imaging-data-commons/references/cloud_storage_guide.md
@@ -205,7 +205,7 @@ IDC releases new data versions every 2-4 months. The versioning system ensures r
 
 ### How Versioning Works
 
-1. **Snapshots**: Each IDC version (v1, v2, ..., v23, etc.) represents a complete snapshot of all data at release time
+1. **Snapshots**: Each IDC version (v1, v2, ..., v24, etc.) represents a complete snapshot of all data at release time
 2. **UUID-based**: When data changes, new CRDC UUIDs are assigned; old UUIDs remain accessible
 3. **Cumulative buckets**: All versions coexist in the same buckets—old series folders
 
@@ -223,7 +223,7 @@ IDC releases new data versions every 2-4 months. The versioning system ensures r
 
 For querying version-specific metadata, BigQuery provides versioned tables. See `bigquery_guide.md` for details.
 - `bigquery-public-data.idc_current` — alias to latest version
-- `bigquery-public-data.idc_v23` — specific version (replace 23 with desired version)
+- `bigquery-public-data.idc_v24` — specific version (replace 24 with desired version)
 
 ### Reproducing a Previous Analysis
 

diff --git a/scientific-skills/imaging-data-commons/references/dicomweb_guide.md b/scientific-skills/imaging-data-commons/references/dicomweb_guide.md
@@ -39,7 +39,7 @@ Replace `{VERSION}` with the IDC release number. To find the current version:
 ```python
 from idc_index import IDCClient
 client = IDCClient()
-print(client.get_idc_version())  # e.g., "23" for v23
+print(client.get_idc_version())  # e.g., "v24" for current version
 ```
 
 - **~96% data coverage** - Only replicates data from `idc-open-data` bucket (missing ~4% from other buckets)
@@ -334,7 +334,7 @@ credentials, project = default()
 credentials.refresh(Request())
 
 # Build authenticated request
-base_url = "https://healthcare.googleapis.com/v1/projects/nci-idc-data/locations/us-central1/datasets/idc/dicomStores/idc-store-v23/dicomWeb"
+base_url = "https://healthcare.googleapis.com/v1/projects/nci-idc-data/locations/us-central1/datasets/idc/dicomStores/idc-store-v24/dicomWeb"
 
 response = requests.get(
     f"{base_url}/studies",

diff --git a/scientific-skills/imaging-data-commons/references/digital_pathology_guide.md b/scientific-skills/imaging-data-commons/references/digital_pathology_guide.md
@@ -1,6 +1,6 @@
 # Digital Pathology Guide for IDC
 
-**Tested with:** IDC data version v23, idc-index 0.11.10
+**Tested with:** idc-index 0.12.1 (IDC data version v24)
 
 For general IDC queries and downloads, use `idc-index` (see main SKILL.md). This guide covers slide microscopy (SM) imaging, microscopy bulk simple annotations (ANN), and segmentations (SEG) in the context of digital pathology in IDC.
 
@@ -251,12 +251,12 @@ client.sql_query("""
     SELECT
         ar.analysis_result_id,
         ar.analysis_result_title,
-        ar.Modalities,
-        ar.Subjects,
-        ar.Collections
+        ar.modalities,
+        ar.subjects,
+        ar.collections
     FROM analysis_results_index ar
-    WHERE ar.Modalities LIKE '%ANN%' OR ar.Modalities LIKE '%SEG%'
-    ORDER BY ar.Subjects DESC
+    WHERE ar.modalities LIKE '%ANN%' OR ar.modalities LIKE '%SEG%'
+    ORDER BY ar.subjects DESC
 """)
 ```
 

diff --git a/scientific-skills/imaging-data-commons/references/index_tables_guide.md b/scientific-skills/imaging-data-commons/references/index_tables_guide.md
@@ -1,6 +1,6 @@
 # Index Tables Guide for IDC
 
-**Tested with:** idc-index 0.11.14 (IDC data version v23)
+**Tested with:** idc-index 0.12.3 (IDC data version v24)
 
 This guide covers the structure and access patterns for IDC index tables: programmatic schema discovery, DataFrame access, and join column references. For the overview of available tables and their purposes, see the "Index Tables" section in the main SKILL.md.
 
@@ -34,7 +34,7 @@ results = client.sql_query("SELECT * FROM index WHERE Modality = 'CT' LIMIT 10")
 
 # Fetch and query additional indices
 client.fetch_index("collections_index")
-collections = client.sql_query("SELECT collection_id, CancerTypes, TumorLocations FROM collections_index")
+collections = client.sql_query("SELECT collection_id, cancer_types, tumor_locations FROM collections_index")
 
 client.fetch_index("analysis_results_index")
 analysis = client.sql_query("SELECT * FROM analysis_results_index LIMIT 5")
@@ -130,6 +130,9 @@ Use this table to identify join columns between index tables. Always call `clien
 | `index` | `volume_geometry_index` | `index.SeriesInstanceUID = volume_geometry_index.SeriesInstanceUID` |
 | `index` | `rtstruct_index` | `index.SeriesInstanceUID = rtstruct_index.SeriesInstanceUID` |
 | `rtstruct_index` | `index` (source images) | `rtstruct_index.referenced_SeriesInstanceUID = index.SeriesInstanceUID` |
+| `index` | `ct_index` | `index.SeriesInstanceUID = ct_index.SeriesInstanceUID` |
+| `index` | `mr_index` | `index.SeriesInstanceUID = mr_index.SeriesInstanceUID` |
+| `index` | `pt_index` | `index.SeriesInstanceUID = pt_index.SeriesInstanceUID` |
 
 For complete query examples using these joins, see `references/sql_patterns.md`.
 

diff --git a/scientific-skills/imaging-data-commons/references/parquet_access_guide.md b/scientific-skills/imaging-data-commons/references/parquet_access_guide.md
@@ -38,6 +38,9 @@ https://storage.googleapis.com/idc-index-data-artifacts/current/release_artifact
 | `collections_index.parquet` | — | Collection-level metadata |
 | `analysis_results_index.parquet` | — | Derived dataset metadata |
 | `clinical_index.parquet` | ~0.2 MB | Clinical data column dictionary |
+| `ct_index.parquet` | — | CT acquisition/reconstruction parameters |
+| `mr_index.parquet` | — | MR sequence/acquisition parameters |
+| `pt_index.parquet` | — | PET acquisition/radiopharmaceutical parameters |
 | `prior_versions_index.parquet` | — | Series from previous IDC releases |
 
 **Note:** the main index file is named `idc_index.parquet`, not `index.parquet`. Reference it with an alias in SQL queries (e.g., `FROM read_parquet(...) AS index`).

diff --git a/scientific-skills/imaging-data-commons/references/sql_patterns.md b/scientific-skills/imaging-data-commons/references/sql_patterns.md
@@ -1,6 +1,6 @@
 # SQL Query Patterns for IDC
 
-**Tested with:** idc-index 0.11.14 (IDC data version v23)
+**Tested with:** idc-index 0.12.3 (IDC data version v24)
 
 Quick reference for common SQL query patterns when working with IDC data. For detailed examples with context, see the "Core Capabilities" section in the main SKILL.md.
 
@@ -14,6 +14,7 @@ Load this guide when you need quick-reference SQL patterns for:
 - Linking imaging data to clinical data
 - Filtering by 3D volume geometry validity (volume_geometry_index)
 - Finding RT Structure Set series and ROI metadata (rtstruct_index)
+- Filtering by CT/MR/PET acquisition parameters (ct_index, mr_index, pt_index)
 
 For table schemas, DataFrame access, and join column references, see `references/index_tables_guide.md`.
 
@@ -74,7 +75,7 @@ client.sql_query("""
 # List analysis result collections (curated derived datasets)
 client.fetch_index("analysis_results_index")
 client.sql_query("""
-    SELECT analysis_result_id, analysis_result_title, Collections, Modalities
+    SELECT analysis_result_id, analysis_result_title, collections, modalities
     FROM analysis_results_index
 """)
 
@@ -277,6 +278,84 @@ client.sql_query("""
 """)
 ```
 
+## Modality Acquisition Parameters
+
+`ct_index`, `mr_index`, and `pt_index` (added in idc-index 0.12.3) expose acquisition and reconstruction parameters for CT, MR, and PET series. All join on `SeriesInstanceUID`. Dose-modulated CT acquisitions have `_min`/`_max` columns for tube current, exposure, and exposure time.
+
+```python
+client.fetch_index("ct_index")
+client.fetch_index("mr_index")
+client.fetch_index("pt_index")
+
+# CT: thin-slice series (≤2mm) with standard reconstruction
+client.sql_query("""
+    SELECT i.collection_id, i.SeriesInstanceUID, i.BodyPartExamined,
+           c.SliceThickness, c.ConvolutionKernel, c.KVP
+    FROM index i
+    JOIN ct_index c ON i.SeriesInstanceUID = c.SeriesInstanceUID
+    WHERE c.SliceThickness <= 2.0
+      AND c.ConvolutionKernel IS NOT NULL
+    LIMIT 10
+""")
+
+# CT: dose-modulated acquisitions (tube current varies across slices)
+client.sql_query("""
+    SELECT i.collection_id, c.SeriesInstanceUID,
+           c.XRayTubeCurrent_min, c.XRayTubeCurrent_max, c.SliceThickness
+    FROM ct_index c
+    JOIN index i ON c.SeriesInstanceUID = i.SeriesInstanceUID
+    WHERE c.XRayTubeCurrent_min != c.XRayTubeCurrent_max
+    LIMIT 10
+""")
+
+# MR: DWI series (have non-null DiffusionBValue) at 3T
+client.sql_query("""
+    SELECT i.collection_id, i.SeriesInstanceUID, i.SeriesDescription,
+           m.MagneticFieldStrength, m.DiffusionBValue
+    FROM index i
+    JOIN mr_index m ON i.SeriesInstanceUID = m.SeriesInstanceUID
+    WHERE m.DiffusionBValue IS NOT NULL
+      AND m.MagneticFieldStrength >= 2.9
+    LIMIT 10
+""")
+
+# MR: multi-echo series (EchoTime stored as array with multiple values)
+client.sql_query("""
+    SELECT i.collection_id, i.SeriesInstanceUID,
+           m.EchoTime, m.EchoTrainLength, m.ScanningSequence
+    FROM index i
+    JOIN mr_index m ON i.SeriesInstanceUID = m.SeriesInstanceUID
+    WHERE m.EchoTrainLength > 1
+    LIMIT 10
+""")
+
+# PET: FDG studies with specific reconstruction method
+client.sql_query("""
+    SELECT i.collection_id, i.SeriesInstanceUID,
+           p.RadionuclideCodeMeaning, p.ReconstructionMethod,
+           p.Units, p.DecayCorrection
+    FROM index i
+    JOIN pt_index p ON i.SeriesInstanceUID = p.SeriesInstanceUID
+    WHERE p.RadionuclideCodeMeaning LIKE '%fluorodeoxyglucose%'
+    LIMIT 10
+""")
+
+# PET: dynamic acquisitions (ActualFrameDuration is array with multiple values)
+client.sql_query("""
+    SELECT i.collection_id, i.SeriesInstanceUID,
+           p.NumberOfTimeSlices, p.ActualFrameDuration
+    FROM index i
+    JOIN pt_index p ON i.SeriesInstanceUID = p.SeriesInstanceUID
+    WHERE p.NumberOfTimeSlices > 1
+    LIMIT 10
+""")
+```
+
+Key columns by table (use `client.indices_overview["ct_index"]["schema"]` for the full list):
+- **ct_index**: `SliceThickness`, `KVP`, `ConvolutionKernel`, `SpiralPitchFactor`, `XRayTubeCurrent_min/max`, `Exposure_min/max`, `PixelSpacing_row_mm/col_mm`, `Rows`, `Columns`
+- **mr_index**: `MagneticFieldStrength`, `ScanningSequence`, `SequenceVariant`, `MRAcquisitionType`, `EchoTime` (array), `RepetitionTime`, `FlipAngle`, `DiffusionBValue` (array), `NumberOfTemporalPositions`, `ReceiveCoilName`
+- **pt_index**: `RadionuclideCodeMeaning`, `Radiopharmaceutical`, `RadionuclideTotalDose`, `ReconstructionMethod`, `DecayCorrection`, `AttenuationCorrectionMethod`, `ActualFrameDuration` (array), `NumberOfTimeSlices`
+
 ## Resources
 
 - `references/index_tables_guide.md` for table schemas, DataFrame access, and join column references

diff --git a/scientific-skills/imaging-data-commons/references/use_cases.md b/scientific-skills/imaging-data-commons/references/use_cases.md
@@ -1,6 +1,6 @@
 # Common Use Cases for IDC
 
-**Tested with:** idc-index 0.11.9 (IDC data version v23)
+**Tested with:** idc-index 0.12.1 (IDC data version v24)
 
 This guide provides complete end-to-end workflow examples for common IDC use cases. Each use case demonstrates the full workflow from query to download with best practices.
 
@@ -178,6 +178,98 @@ client.download_from_selection(
 cc_by_data.to_csv('commercial_dataset_manifest_CC-BY_ONLY.csv', index=False)
 ```
 
+## Use Case 5: Batch Download with Filtering
+
+**Objective:** Download a large filtered dataset in batches to avoid timeouts
+
+**Steps:**
+```python
+from idc_index import IDCClient
+import pandas as pd
+
+client = IDCClient()
+
+# Find chest CT scans from GE scanners with a permissive license
+query = """
+SELECT
+  SeriesInstanceUID,
+  PatientID,
+  collection_id,
+  ManufacturerModelName
+FROM index
+WHERE Modality = 'CT'
+  AND BodyPartExamined = 'CHEST'
+  AND Manufacturer = 'GE MEDICAL SYSTEMS'
+  AND license_short_name = 'CC BY 4.0'
+LIMIT 100
+"""
+
+results = client.sql_query(query)
+
+# Save manifest for reproducibility
+results.to_csv('lung_ct_manifest.csv', index=False)
+
+# Download in batches to avoid timeout
+batch_size = 10
+for i in range(0, len(results), batch_size):
+    batch = results.iloc[i:i+batch_size]
+    client.download_from_selection(
+        seriesInstanceUID=list(batch['SeriesInstanceUID'].values),
+        downloadDir=f"./data/batch_{i//batch_size}"
+    )
+```
+
+## Use Case 6: Integration with Analysis Pipelines
+
+**Objective:** Load downloaded DICOM files into Python for processing
+
+**Read individual DICOM files with pydicom:**
+```python
+import pydicom
+import os
+
+series_dir = "./data/rider/rider_pilot/RIDER-1007893286/CT_1.3.6.1..."
+
+dicom_files = [os.path.join(series_dir, f) for f in os.listdir(series_dir)
+               if f.endswith('.dcm')]
+
+ds = pydicom.dcmread(dicom_files[0])
+print(f"Patient ID: {ds.PatientID}")
+print(f"Modality: {ds.Modality}")
+print(f"Image shape: {ds.pixel_array.shape}")
+```
+
+**Build 3D volume from CT series:**
+```python
+import pydicom
+import numpy as np
+from pathlib import Path
+
+def load_ct_series(series_path):
+    files = sorted(Path(series_path).glob('*.dcm'))
+    slices = [pydicom.dcmread(str(f)) for f in files]
+    slices.sort(key=lambda x: float(x.ImagePositionPatient[2]))
+    volume = np.stack([s.pixel_array for s in slices])
+    return volume, slices[0]
+
+volume, metadata = load_ct_series("./data/lung_ct/series_dir")
+print(f"Volume shape: {volume.shape}")  # (z, y, x)
+```
+
+**Load DICOM series with SimpleITK (recommended for correct geometry):**
+```python
+import SimpleITK as sitk
+
+series_path = "./data/ct_series"
+reader = sitk.ImageSeriesReader()
+dicom_names = reader.GetGDCMSeriesFileNames(series_path)
+reader.SetFileNames(dicom_names)
+image = reader.Execute()
+
+smoothed = sitk.CurvatureFlow(image1=image, timeStep=0.125, numberOfIterations=5)
+sitk.WriteImage(smoothed, "processed_volume.nii.gz")
+```
+
 ## Resources
 
 - Main SKILL.md for core API patterns (query, download, visualize)