Introduction In a previous article, we explored a zero-code approach to storing DICOM metadata by persisting vendor-specific and modality-specific tags as JSONB in an extradicomtags column. This approach embraces the reality of clinical imaging data: the schema is heterogeneous, nested, and constantly evolving. By storing the raw metadata as a JSON document and applying GIN … [Read more…]
The Real Problem: Ingestion Is Not the Product In medical imaging platforms, ingestion is often treated as a prerequisite rather than a core problem. Yet teams repeatedly invest months building custom pipelines just to move DICOM files from point A to point B. A typical from-scratch ingestion pipeline ends up requiring filesystem watchers or DICOM … [Read more…]
Clinical environments generate vast amounts of operational data from electronic medical records, oncology information systems, PACS archives, and imaging and treatment-planning platforms. This data has enormous value for research, AI model development, and quality improvement. Yet pulling this data from production systems in a form suitable for analysis is exceptionally difficult. The core challenge is … [Read more…]