Pydicom

VR (Value Representation)

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

Quick Reference

Standard Imports

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

Basic Patterns

# 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

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

# 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.