We unlock data liquidity to fuel the next era of radiology innovation and operational excellence

The Quantivly Digital Twin Platform

At the core of the Quantivly’s Digital Twin are two key components: (1) a unified data layer that is continuously evolving via real-time data assimilation, used to construct a model of radiology operations, (2) tools for simulation of interventions in-silico. Beyond reporting, monitoring, and analytics capabilities, we expect the digital twin vision to radically reshape the way radiology manages operations.

A new ontology to describe radiology operations

The Quantivly data layer

To construct the unified data layer, we clean and harmonize DICOM and HL7 while going further to extract new concepts to build a new ontology. Notably, the concepts of an acquisition, examination, and imaging volume are extracted from DICOM “series” and “study” to describe “what was performed” with “what was scheduled” from RIS-HL7.

Harmonize HL7

What really happened is only half the story. To achieve continuous improvement, we need to understand “what should have happened”. Quantivly’s Digital Twin integrates information from the Radiology Information System to compare “what was scheduled” with “what actually happened” allowing for a comparison and a continous feedback loop to improve imaging operations.

Harmonize DICOM

DICOM image metadata contains the details of “what happened” during an imaging exam. Unfortunatley, DICOM is messy and very heterogenous and does not describe imaging operations by default, so Quantivly created a new Ontology that goes beyond DICOM. For example, a DICOM series is not an acquisitions, and a DICOM Study is not an examination.

Acquisition and examination

Quantivly introduces the concept of acquisition and examination into the Digital Twin, so you can understand what really happened

  • What would be the impact of purchasing a new MRI scanner ?
  • Can I reliably shorten my scheduled slot size without incurring delays ?
  • What is the repeat rate of my MRI Brain protocol ?

Data is content, metadata is context - Data and Goliath


Data liquidity is needed to drive the next era of radiology innovation. The data in our ontology is fully query-able and we help you write queries with our professional services package so you can ask any questions of your data. For advanced users, we provide a SQL and GraphQL endpoint. Finally, external databases can access the data via API. Developers can novel to serve the needs of imaging providers with our Software Development Kit (SDK)

The Quantivly Digital Twin provides a rich model of radiology operations with tools to access and interact with the data to address the most pressing needs of your institution.