Overview
Princeton Medical System needed to bring non-standard medical images — captured as JPEG, PNG, TIFF, and PDF — into their DICOM-based imaging workflow. OVN Technologies engineered an automated image processing pipeline that converts these files into standards-compliant DICOM studies, injecting the required patient, study, series, and modality parameters so the output is ready for PACS.
The Challenge
A significant volume of clinical images existed in non-DICOM formats and could not be ingested into the PACS or shared across systems that expect DICOM. Manually converting images and hand-entering the required DICOM tags — patient identifiers, study and series details, modality, and imaging parameters — was slow, error-prone, and risked producing metadata that failed validation or broke interoperability.
Our Solution
We built an automated medical image conversion pipeline that ingests JPEG, PNG, TIFF, and PDF source files and outputs valid DICOM objects. The system maps and injects the required DICOM tags and imaging parameters, generates proper study/series/instance hierarchies and UIDs, validates each object against the DICOM standard, and delivers PACS-ready studies. Configurable parameter templates ensure every conversion carries the correct modality and study attributes.
The Result
Legacy and non-standard images are now converted into compliant, PACS-ready DICOM studies through an automated, validated workflow. Conversion that was previously manual and inconsistent became fast and repeatable, with correct tags and imaging parameters on every study — enabling reliable storage, retrieval, and interoperability across the medical imaging systems.
“OVN's pipeline reliably turns our non-standard images into valid, PACS-ready DICOM studies with the right parameters every time — it removed a real bottleneck in our imaging workflow.”
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