pydicom

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Python package for working with DICOM files. It allows you to read, modify, and write DICOM data in a Pythonic way. Essential for medical imaging processing, clinical data extraction, and AI in radiology.

General

What this skill does


# Pydicom - Medical Imaging Standards

DICOM is more than an image; it's a rich data structure containing patient info, spatial orientation, and pixel data. Pydicom provides access to all these tags.

## When to Use

- Processing medical imaging data (CT, MRI, X-ray, ultrasound).
- Extracting patient metadata and clinical information from DICOM files.
- Building AI models for radiology that require both image and metadata.
- Converting DICOM to other formats for analysis.
- Quality assurance and compliance checking in medical imaging workflows.

## Core Principles

### Datasets as Dicts

Access tags by name (e.g., `ds.PatientName`) or ID (`ds[0x0010, 0x0010]`).

### Pixel Data

Raw pixel data is stored in `PixelData`, but should be accessed via `pixel_array` for NumPy integration.

### VR (Value Representation)

Strict typing for dates, ages, and decimals ensures data integrity.

## Quick Reference

### Standard Imports

```python
import pydicom
from pydicom.data import get_testdata_files
import matplotlib.pyplot as plt
import numpy as np
```

### Basic Patterns

```python
# 1. Read file
ds = pydicom.dcmread("scan.dcm")

# 2. Access Metadata
print(f"Patient: {ds.PatientName}, ID: {ds.PatientID}")
print(f"Modality: {ds.Modality}") # CT, MR, DX
print(f"Study Date: {ds.StudyDate}")
print(f"Slice Thickness: {ds.SliceThickness}")

# 3. Access Image
plt.imshow(ds.pixel_array, cmap="gray")
plt.title(f"{ds.Modality} - {ds.PatientName}")
```

## Critical Rules

### ✅ DO

- **Use pixel_array property** - Always access pixel data via `ds.pixel_array` rather than `ds.PixelData` for proper NumPy integration.
- **Check for missing tags** - Use `hasattr(ds, 'TagName')` before accessing optional tags.
- **Respect patient privacy** - DICOM files contain PHI (Protected Health Information). Always anonymize before sharing.
- **Handle different photometric interpretations** - Some images may be inverted or use different color spaces.

### ❌ DON'T

- **Don't modify DICOM files in place** - Always create a copy when modifying to preserve original data.
- **Don't ignore VR types** - DICOM has strict data types. Converting incorrectly can corrupt data.
- **Don't assume all DICOM files have images** - Some contain only metadata (structured reports).

## Advanced Patterns

### Working with DICOM Series

```python
import pydicom
from pathlib import Path

# Load a series of DICOM files
dicom_dir = Path("dicom_series")
files = sorted(dicom_dir.glob("*.dcm"))

# Load and stack slices
slices = [pydicom.dcmread(f) for f in files]
volume = np.stack([s.pixel_array for s in slices])
```

### Anonymization

```python
# Remove patient identifiers
ds.PatientName = "ANONYMOUS"
ds.PatientID = "000000"
ds.PatientBirthDate = ""
ds.PatientSex = ""
```

Pydicom is the foundation of medical imaging in Python, enabling researchers and clinicians to work with the rich, standardized DICOM format that powers modern radiology.

Files: 1

Size: 3.1 KB

Complexity: 12/100

Category: General

Source: https://github.com/tondevrel/scientific-agent-skills/tree/main/skills/pydicom

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