DICOM Attribute
Study Description (0008,1030)
- Keyword
StudyDescription- VR
LO- VM
1
Definition
Institution-generated description or classification of the Study performed.
From NEMA PS3.3 §C.7.2.1.
Attribute Metadata
This attribute is part of the current DICOM Standard. Its value representation is LO with multiplicity 1.
Used in 2 Modules
Value Representation
This attribute uses the LO
Value Representation. Multiplicity 1 governs how many values may be encoded.
Integration Notes
Real-world integration guidance for engineers implementing this attribute in Mirth Connect, FHIR gateways, HL7 v2 bridges, and cloud DICOM services. Authoritative source data where available; reference examples and operational notes where they add value.
HL7 v2 Equivalent
-
OBR-4Universal Service IdentifierORMORUOBR-4 is CWE coded with text — DICOM StudyDescription typically uses the text component. Same field also feeds (0040,1003) RequestedProcedureCode + ProcedureCodeSequence.
Source: IHE RAD-TF Vol 2 §4.7
Verify against IHE Radiology TF Vol 2 and your facility's interface specification before production use.
Cloud DICOM Support
- AWS HealthImaging Preserved
- Azure DICOM Preserved
- Google Cloud Healthcare Preserved
Study Description is preserved on Store but not a QIDO-RS matching key by default. Some vendors expose it via extended search parameters (Azure: 'extendedQuerytags' feature).
Per-vendor source URLs and verification date in src/content/dicom/cloud-support.json#_meta.
Status taxonomy: required (mandated on Store), queryable (QIDO-RS matching key), preserved (stored as-is), promoted (AWS top-level metadata), unsupported (stripped/rejected).
Real-World Sample Values
-
e+1CT_small.dcm -
ECGwaveform_ecg.dcm -
OFFIS Structured Reporting Templatesreportsi.dcm
Extracted from pydicom test fixtures (MIT, de-identified).
Mirth Connect Example
Sanitize Study Description for downstream display — Vendor scanners sometimes emit ALL-CAPS, weird punctuation, or extra whitespace
Vendor scanners sometimes emit ALL-CAPS, weird punctuation, or extra whitespace. Normalize on ingest.
// (0008,1030) Study Description — clean for downstream display
var desc = String(msg.dataset.attr.(@tag == '00081030').value).trim();
// Title-case, collapse whitespace, strip CR/LF (occasionally embedded)
desc = desc.replace(/[\r\n]+/g, ' ').replace(/\s+/g, ' ');
desc = desc.toLowerCase().replace(/\b\w/g, function(c) { return c.toUpperCase(); });
channelMap.put('study_description', desc); Reference only — validate before production. Snippets are starting points, not turnkey solutions. Always test against your channel's specific message structure and your facility's interface specification. See Mirth Connect User Guide + NEMA PS3.6 + your vendor conformance statement for authoritative specification.
Common Operational Errors
-
low Study Description inconsistency with Procedure Code
Diagnosis: RIS-emitted Procedure Code Sequence (0032,1064) says 'CT CHEST W/CONTRAST' but Modality-set Study Description (0008,1030) says 'PE PROTOCOL'. Downstream reporting templates and AI inference pipelines key off the wrong field.
Fix: Establish convention at the facility: Procedure Code Sequence is canonical from RIS; Study Description is operator-set and ignored downstream. Or: enforce StudyDescription = ProcedureCode.text on the perimeter and notify when they diverge.
Reference only — validate before applying a fix. Scenarios are drawn from common DICOM/HL7 v2 integration patterns; reproduce against your environment before acting on any diagnosis. See NEMA PS3.6 + your vendor conformance statement for authoritative specification. Severity levels: high (data-loss / patient-safety), medium (workflow disruption), low (cosmetic).
DICOM Integration Services
Saga IT builds production DICOM integrations across Mirth Connect, FHIR gateways, and cloud imaging platforms. If you're working with StudyDescription or the broader (0008,1030) context:
- Medical Imaging & DICOM Integration — end-to-end PACS, VNA, and cloud imaging deployments
- Mirth Connect Services — production-grade Mirth channel development
- Healthcare AI Integration — DICOM pipelines for AI/ML inference