Inferensys

Glossary

DICOM Tag

A unique 32-bit identifier composed of a Group and Element number used to address a specific data attribute, such as Patient Name (0010,0010), within a DICOM data set.
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DATA ELEMENT IDENTIFIER

What is a DICOM Tag?

A DICOM Tag is the fundamental addressing mechanism for every piece of data within a DICOM object, enabling precise, machine-readable identification of attributes from patient demographics to pixel spacing.

A DICOM Tag is a unique 32-bit identifier composed of a Group Number and an Element Number (written as (GGGG,EEEE) in hexadecimal) that addresses a specific data attribute within a DICOM data set. For example, the tag (0010,0010) universally identifies the Patient's Name, while (0028,0030) specifies the Pixel Spacing. This standardized addressing scheme, defined in the DICOM Data Dictionary (Part 6 of the standard), ensures that any compliant system can parse and interpret the exact meaning of every data element without ambiguity, regardless of the imaging modality or vendor that created the file.

Tags are categorized by their Group Number: even-numbered groups are reserved for the DICOM Standard, while odd-numbered groups are designated for Private Tags used by vendors to store proprietary information. Each tag is further defined by its Value Representation (VR)—a two-character code like 'DA' for Date or 'UI' for Unique Identifier—which dictates the data type and encoding rules for the element's value. The combination of a tag's identifier, its VR, and its value length forms the complete data element structure that underpins all DICOM interoperability, from network transfers using DIMSE commands to RESTful queries via DICOMweb.

TAG CLASSIFICATION

Standard vs. Private DICOM Tags

Comparison of standard DICOM data elements defined in the DICOM standard versus proprietary private tags defined by equipment vendors.

FeatureStandard TagsPrivate Tags

Definition Source

DICOM Part 6 Data Dictionary

Vendor-specific implementation

Group Number Range

Even groups (0008, 0010, 0028, etc.)

Odd groups (0009, 0011, 0013, etc.)

Interoperability

Public Documentation

Reservation Mechanism

Not required; globally defined

Private Creator Data Element (gggg,0010-00FF)

Risk During Anonymization

Known PHI locations; predictable removal

May contain hidden PHI; requires vendor knowledge

Example

(0010,0010) Patient's Name

(0009,1001) Siemens Private Tag

Required for Conformance

DICOM TAG FUNDAMENTALS

Frequently Asked Questions

Essential questions and answers about the structure, parsing, and clinical significance of DICOM Tags, the atomic data identifiers that form the backbone of medical imaging interoperability.

A DICOM Tag is a unique 32-bit unsigned integer identifier that addresses a specific data element within a DICOM data set. It is canonically represented as two 16-bit hexadecimal numbers enclosed in parentheses and separated by a comma, formatted as (GGGG,EEEE). The first component, the Group Number (GGGG), broadly categorizes the type of information—for example, Group 0010 contains patient demographic data. The second component, the Element Number (EEEE), specifies the exact attribute within that group, such as 0010 for Patient Name. Together, the tag (0010,0010) unambiguously identifies the Patient Name attribute across all DICOM-compliant systems. This hierarchical addressing scheme is the fundamental mechanism that allows parsers to navigate the structured, tag-length-value encoded byte stream of a DICOM file or network message without relying on positional offsets.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.