XyDromatics VNA Research Router-pattern host · RUO

XyDromatics VNA Research — de-identified by design.

The Research Use Only sibling of the VNA family. PHI anonymization at ingest is mandatory and enforced in the binary, not just in the label. Cohort-level access control, project partitioning, research-friendly exports (NIfTI, BIDS, CSV manifests), IRB-aware audit trail.

Deliberately not a medical device. RUO labeling under 21 CFR 809.10(c)(2)(i) keeps clinical decision-making out of scope by design — that’s what VNA Clinical is for. Research and clinical postures kept structurally separate, with the regulatory boundary mirrored at code boundary.

The regulatory posture

“Research Use Only” isn’t a marketing slogan.

RUO is a regulatory category defined by FDA in 21 CFR 809.10(c)(2)(i): a product labeled exclusively for research, not for clinical diagnosis or treatment decisions. Products in this category sit outside FDA medical-device classification because their intended use is investigational — and crossing into clinical use would require a different regulatory pathway.

For VNA Research that means three things, all enforced:

1. Mandatory anonymization

Every study passes through anonymization at ingest. Un-anonymized data never lands.

2. No clinical code paths

No HL7 ORU, no DiagnosticReport, no clinical-tag-edit. The binary cannot reach clinical workflows.

3. License enforcement

anonymization_enforced=true is a license setting, not a config flag — observable on /health.

4. Separate process boundary

Different executable, different port from VNA Clinical. Code that handles clinical data never runs in this process.

That last point is the same engineering principle applied across the family — every product is non-device software under FD&C Act §520(o)(1)(D): regulatory class boundary mirrored at code boundary. Researchers, IRB reviewers, and FDA inspectors all evaluate the same product with the same posture.

Capabilities

What VNA Research does — and doesn’t do.

Two capabilities (marked RUO) are the regulated boundary that keeps this product out of FDA medical-device territory. The rest are research-workflow features built on top.

PHI anonymization at ingest

RUO

The load-bearing capability. Every study entering VNA Research passes through anonymization before it lands in the archive — never lives un-anonymized in this product.

  • DICOM PS3.15 Annex E — Basic Confidentiality Profile (mandatory baseline)
  • Per-IRB-protocol anonymization profile overrides (study-specific tag retention)
  • Pixel-data redaction for burned-in PHI (overlays, scanout, secondary captures)
  • Date-shift jittering to preserve longitudinal relationships while breaking calendar identification
  • Keyed-hash UID remapping — same patient always maps to the same anon-ID (longitudinal linkage); the system's encryption key is the keying material
  • Mapping retained in the IRB-protected layer to enable later authorized re-identification of system-anonymized studies
  • Verification step before commit — anonymization failures route to quarantine, never to the live archive

Cohort + project access control

Researchers don't need access to the whole archive — they need access to their cohort. Access control happens at the cohort/project level, not per-AE-Title.

  • Per-cohort read access (a researcher only sees their own cohort)
  • Per-project data partitioning with logical isolation
  • Honest-broker capability for studies anonymized by this system — IRB-gated re-identification, patient recontact, and longitudinal linkage
  • IRB-protocol metadata attached to every cohort
  • Audit trail records every cohort-data access with researcher identity
  • Cohort expiration — automatic archive purge at protocol end

Research-friendly export formats

Researchers run their tools against NIfTI, BIDS, and tabular cohort manifests — not against raw DICOM. Export pipelines convert at request time.

  • NIfTI conversion (1- and 2-volume)
  • BIDS-format export (Brain Imaging Data Structure) for neuroimaging studies
  • CSV / Parquet cohort manifests with selected DICOM tags as columns
  • DICOM-SR extraction to structured JSON / FHIR DiagnosticReport
  • Configurable per-modality export pipelines
  • Export-job audit trail (what cohort, what format, when, by whom)

Re-identification risk controls

De-identification quality matters. RUO doesn't mean "best-effort" — it means defensible against re-identification within the limits of the anonymization standard, with built-in broker capability for IRB-authorized re-link.

  • k-anonymity / l-diversity reporting per cohort
  • Free-text-field scanning (study description, comments) for residual PHI
  • Configurable risk thresholds with cohort-level gating
  • Documented re-identification model assumptions per IRB
  • Built-in honest-broker capability — keyed-hash pseudonymization keeps the source ↔ anon-ID mapping consultable without external assistance, IRB-protocol-gated
  • External pre-anonymized data: no re-identification path (no mapping captured)
  • Audit-ready report generation for IRB renewal cycles

RUO boundary enforcement

RUO

The "not a medical device" labeling is enforced in code, not just in the EULA. Clinical-decision pathways are physically unreachable from VNA Research.

  • Mandatory `anonymization_enforced=true` license setting (cannot be disabled by config)
  • /health endpoint exposes `anonymization_enforced:true` for external auditing
  • No clinical-tag-edit code paths in the binary (separate executable from VNA Clinical)
  • No HL7 ORU / DiagnosticReport emit (no clinical reporting integration)
  • RUO labeling on web UI, API responses, and exported manifests
  • License-feature gating prevents cross-loading of clinical capabilities

Operations & administration

Research-flavored admin surface — IRB protocols, data dictionaries, cohort definitions instead of clinical workflows.

  • Web administration UI with shared sidebar shell (consistent with the XyDromatics family)
  • Role-based access control (researcher / PI / data steward / IRB observer)
  • Cohort definition workflow with version history
  • Data dictionary management per protocol
  • Storage-utilization reporting per cohort + per project
  • SynthQMS integration for protocol changes as controlled documents

Architecture

Anonymization is the gate, not an option.

The diagram below shows the load-bearing element: every study entering VNA Research passes through anonymization before landing in the archive. There is no bypass path.

XyDromatics VNA Research architecture PHI-bearing data flows from production clinical sources (VNA Clinical, the customer's existing VNA, etc.) through a mandatory anonymization gate before entering VNA Research. Researchers query their assigned cohorts, exporting in research-friendly formats. Anonymization failures route to quarantine and never enter the live RUO archive. CLINICAL SOURCES PHI-bearing ANONYMIZATION GATE mandatory · code-enforced RUO ARCHIVE de-identified only Clinical sources VNA Clinical Production VNAs Site-local archives contain PHI Anonymization gate DICOM PS3.15 Annex E + IRB profile Pixel redaction · UID remap · date shift Verification before commit de-identified VNA Research Cohort archive Project partitioning RUO · not a medical device Anonymization-failure quarantine Never enters the live RUO archive cohort scope Research consumers PIs · AI training NIfTI / BIDS / CSV exports

The anonymization gate is the architectural feature that makes VNA Research possible as an RUO product. Without it, the archive would hold PHI and would need a different regulatory classification entirely. Anonymization failures route to quarantine — never to the live archive — so a partially- anonymized study can’t accidentally land alongside properly-anonymized cohort data.

Common use cases

Five research-workflow patterns.

What customers actually deploy VNA Research for. Each pattern assumes the standard IRB protocol governance and consortium / data-use agreements that academic research requires.

Pattern 1

Longitudinal research cohort

A multi-year IRB-approved study following a defined cohort. Studies arrive over years; researchers need consistent anonymization (same patient → same anon UID across all timepoints) and project-scoped access.

Flow

  1. 1 IRB approves the study + anonymization profile
  2. 2 Production clinical archive forwards qualifying studies through anonymization gate
  3. 3 VNA Research stores anonymized studies with consistent UID remapping
  4. 4 PI + named researchers access only their cohort via the cohort-level RBAC
  5. 5 At protocol end: cohort export + audit-trail report for IRB closeout
Pattern 2

AI training data preparation

An AI-application vendor (internal or external) needs a labeled training corpus from de-identified imaging. VNA Research holds the anonymized corpus and exposes it via cohort-scoped export pipelines.

Flow

  1. 1 Cohort definition: modality, body part, date range, label criteria
  2. 2 Anonymization runs at ingest; pixel-data redaction on overlays
  3. 3 Export pipeline produces NIfTI + label manifest per training run
  4. 4 Audit trail records every export — defensible against later "where did this data come from?" questions
  5. 5 Cohort retention and disposal align with the data-use agreement
Pattern 3

Multi-institution research consortium

Three academic medical centers collaborate on a study. Each contributes data into a shared VNA Research instance after local-site anonymization. Researchers query across the federated cohort without seeing source-site identities.

Flow

  1. 1 Each site's production archive forwards qualifying studies through site-local anonymization
  2. 2 Site-of-origin metadata stripped or coded per consortium policy
  3. 3 Shared VNA Research holds the federated cohort
  4. 4 Researchers query across all sites with a single cohort definition
  5. 5 Per-site contribution audit retained for paper-authorship attribution
Pattern 4

Population-health analytics

Health-system epidemiology and quality teams need access to anonymized imaging at population scale to track quality metrics, screening rates, follow-up adherence. Research-Use-Only because outputs feed quality reports, not individual clinical decisions.

Flow

  1. 1 All clinical studies forwarded through anonymization at ingest
  2. 2 VNA Research holds the population corpus with longitudinal linkage via consistent UID remapping
  3. 3 Analytics platforms run cohort queries (e.g., "all 60+ y.o. patients with low-dose CT in 2024 with follow-up imaging")
  4. 4 Reports aggregate at population level — no individual identification
  5. 5 IRB protocol covers the population-health use under the institutional QI / research framework
Pattern 5

AI-vendor integration without sharing PHI

Send studies to an AI vendor for analysis without exposing PHI — and still get the findings linked back to the right patient. Keyed-hash pseudonymization makes the round-trip work: de-identify on the way out, re-identify on the way back. AI vendors avoid BAA scope; the institution keeps PHI in-house; clinical workflow still gets findings on the right patient.

Flow

  1. 1 Clinical source archive forwards qualifying studies through the anonymization gate
  2. 2 Gate uses keyed-hash pseudonymization (anon-ID derived from PHI + system encryption key) — same patient always maps to same anon-ID
  3. 3 De-identified studies sent to AI vendor (vendor sees only anon-ID)
  4. 4 AI vendor analyzes, returns findings tagged with the same anon-ID
  5. 5 System uses the retained mapping to re-identify the anon-ID and forward findings to the source clinical archive linked to the correct patient
  6. 6 AI vendor never received PHI; clinical workflow benefits from AI findings; HIPAA exposure minimized
Pattern 6

AI-application validation against retrospective data

Validating a clinical AI application against historical performance — does it perform as claimed against our patient population? VNA Research holds the validation cohort with ground-truth labels; AI vendor receives only de-identified data.

Flow

  1. 1 Validation cohort defined per protocol (modality + clinical scenario + ground-truth source)
  2. 2 Cohort de-identified into VNA Research with ground-truth labels attached
  3. 3 AI vendor receives access scoped to validation cohort only
  4. 4 Performance metrics computed against ground truth
  5. 5 Validation report archived; cohort retained per data-use agreement

Integration points

What VNA Research connects to.

The integration surface is research-flavored: identity domains separate from clinical, IRB-protocol governance, export pipelines for research tools, and the always-mandatory anonymization gate sitting between PHI sources and the RUO archive.

Inbound (anonymization-gated)

  • Production clinical archive (VNA Clinical, customer's existing VNA, etc.) — feeds qualifying studies through the gate
  • Site-local anonymization tools at multi-institution consortia
  • Direct STOW-RS supported but rarely used (anonymization should run upstream)
  • Modalities NEVER write directly to VNA Research (PHI would land un-anonymized)

Outbound (research consumers)

  • Researcher workstations querying their own cohorts
  • AI training pipelines (export to NIfTI, BIDS, label manifests)
  • Statistical analysis platforms (R, SAS, Python via cohort manifests)
  • External validation engagements (scoped cohort export with data-use agreement)
  • Population-health analytics platforms

Anonymization & governance

  • DICOM PS3.15 Annex E baseline profile (mandatory)
  • Per-IRB-protocol overrides for tag retention
  • Pixel-data redaction tooling (burned-in PHI, overlays, scanout)
  • Honest-broker integration for protocols requiring re-identification
  • k-anonymity / l-diversity reporting for risk assessment
  • IRB-protocol versioning via SynthQMS document control

Storage & catalog (shared with the VNA family)

  • Local filesystem (default for departmental research)
  • S3-compatible object stores
  • On-prem NAS (NFS / SMB)
  • PostgreSQL / SQL Server / Oracle catalog (no SQLite)
  • Cohort partitioning at the catalog level

Identity & audit

  • OIDC / SAML for the web admin UI
  • Researcher accounts separate from clinical RBAC (different identity domain)
  • Per-cohort access logs with researcher identity
  • IRB-observer read-only access role
  • Audit-export tool for IRB renewal cycles

System requirements & sizing

Sized to cohort scale, not population.

Research workloads are different from clinical: bursty (cohort builds + export jobs) rather than continuous, and read-heavy rather than write-heavy. Sizing reflects active cohort count and aggregate cohort size more than ingest throughput.

Tier Cohort scale CPU RAM Typical site
Departmental < 10 active cohorts, < 50 TB 4 vCPU 16 GB Single research group, single PI, departmental infrastructure.
Institutional 10–50 active cohorts, 50 – 500 TB 8 vCPU 32 GB Academic medical center research-IT, multiple PIs, multiple modalities.
Multi-institution 50+ cohorts, 500 TB+, federated query 16 vCPU per site, federation layer 64 GB per node Research consortium, federated multi-site query.

Licensing

Three packages, anonymization always-on.

The anonymization-enforced license setting is mandatory at every tier — there’s no Core-without-anonymization path because there can’t be (that wouldn’t be RUO). Tiers differ in advanced anonymization and federation features.

VNA Research Core

Single-institution RUO archive with full anonymization and cohort-level access control.

  • PHI anonymization at ingest (DICOM PS3.15 Annex E baseline)
  • Cohort + project access control
  • Standard export formats (NIfTI, CSV manifests)
  • IRB-friendly audit trail
  • Web admin UI with researcher-flavored RBAC
  • Up to 500 TB archive

VNA Research + Advanced Anonymization

Adds re-identification risk controls and pixel-data redaction for studies with burned-in PHI.

  • Everything in Core
  • Pixel-data redaction (overlays, burned-in PHI, scanout)
  • k-anonymity / l-diversity reporting
  • Free-text-field PHI scanning
  • Honest-broker integration
  • Date-shift jittering with longitudinal preservation

VNA Research Enterprise

Multi-institution federation, BIDS exports, and full IRB lifecycle tooling.

  • Everything in Core + Advanced Anonymization
  • Multi-site federation layer
  • BIDS-format export for neuroimaging
  • Per-IRB-protocol anonymization profile management
  • Cohort lifecycle (creation → active → archived → disposed) with audit
  • IRB-observer role and audit-export tooling
  • Unlimited archive capacity

Pricing reflects the research-workload profile — typically per-institution annual subscription with cohort-count and storage-capacity tiers. Bundle pricing is available with VNA Clinical for institutions running both postures, or with VNA Clinical Research for a single-binary combined deployment.

Documentation

The controlled-document set.

Every VNA Research deployment ships with the documents below, all managed under SynthQMS document control. The DICOM Conformance Statement is publicly available; the rest are shared under mutual NDA.

Document Title Notes
DOC-2026-130 Hardware & Software Requirements (VNA Research) Sizing, OS support, dependencies
DOC-2026-140 DICOM Conformance Statement (VNA Research) Public — full SOP class + transfer-syntax matrix
DOC-2026-135 EULA Annex (VNA Research) Annex to the Synthology Master EULA (DOC-2026-063)
DOC-2026-150 Penetration Test Results (VNA Research) Available under NDA
DOC-2026-145 QA Runner Manual (VNA Research) Internal QA + customer-validated test runs
DOC-2026-163 User Guide (VNA Research) Cohort workflows, export pipelines, IRB protocols
DOC-2026-166 Installation Guide (VNA Research) Includes MSI deploy + Linux tarball install

Additional documents

VNA Research-specific documents beyond the standard set. Items marked Pending are tracked for authoring under SynthQMS change control.

Document Title Notes
DOC-2026-402 RUO Labeling Statement (VNA Research) Formal Research Use Only labeling per 21 CFR 809.10(c)(2)(i)
DOC-2026-403 Anonymization Profile Catalog (VNA Research) Standard + per-IRB-protocol profiles
DOC-2026-404 De-Identification Verification SOP (VNA Research) How VNA Research validates anonymization quality
DOC-2026-405 IRB Reviewer Briefing Pack (VNA Research) For institutional IRBs evaluating VNA Research deployments

Frequently asked

The questions PIs and IRBs ask.

Why isn't this an FDA-listed medical device?

It's deliberately not. "Research Use Only" is a regulatory category defined in 21 CFR 809.10(c)(2)(i): a product labeled for research use, not for clinical diagnosis or treatment decisions. RUO products sit outside the FDA medical-device classification system because their intended use is investigational. Crossing into clinical use would require a different regulatory pathway -- which is exactly what VNA Clinical is for.

Can researchers use this archive for clinical decisions?

No. The RUO labeling is enforced in two places. First, the web UI, API responses, and exported manifests carry the RUO label. Second -- and more importantly -- the binary itself physically cannot run clinical-decision code paths. There's no HL7 ORU emission, no DiagnosticReport integration, no clinical-tag-edit, no critical-results notification. The license-feature enforcement keeps the RUO boundary at code level, not just at label level.

What anonymization standard do you use?

DICOM PS3.15 Annex E (Basic Confidentiality Profile) as the mandatory baseline, with per-IRB-protocol overrides for tag retention. Pixel-data redaction handles burned-in PHI in overlays, scanout, and secondary captures. Date-shift jittering preserves longitudinal relationships within a patient while breaking calendar identification. The specific profile applied to a cohort is captured in the cohort metadata for IRB and audit purposes.

Can we re-identify patients if the protocol requires follow-up?

Yes — for studies that entered VNA Research through its own anonymization gate, with no external assistance required. The anonymization gate uses keyed-hash pseudonymization: the system's encryption key is the keying material for the hash that produces each anon-ID. Same patient always maps to the same anon-ID (preserves longitudinal linkage); the key remains under the system's key-management custody. The honest-broker capability is built into the system — a designated broker role (with explicit permission, audit-trailed, IRB-protocol-gated) consults the mapping to support patient recontact, longitudinal-linkage confirmation without exposing PHI to researchers, or — under narrow protocol authorization — returning identified data for specific IRB-approved purposes. No external honest-broker entity needed. Important constraint: studies ingested already-de-identified from external sources (consortium partners, etc.) cannot be re-identified — we never had the mapping or the source PHI in the first place.

How does this interact with our IRB?

IRB protocols become controlled documents in SynthQMS. Each protocol carries its own anonymization profile, access list, data-use agreement reference, and retention policy. The audit-export tool produces IRB-renewal-ready documentation: who accessed what cohort, when, what was exported, and to whom. Optional IRB-observer read-only role lets an IRB reviewer audit the deployment in real time.

Can VNA Research be deployed alongside VNA Clinical?

Yes -- this is a common pattern. VNA Clinical handles the production clinical archive; VNA Research holds the de-identified research corpus. Studies flow from Clinical through the anonymization gate into Research. The two run as separate executables on separate ports, with separate identity domains -- a researcher account on Research has no access to Clinical, and vice versa. For combined-posture institutions that want one host running both, see VNA Clinical Research.

How do you verify the anonymization actually worked?

Three layers. First: every anonymization run produces a verification report (which tags were removed, which were remapped, which were retained per protocol). Second: free-text-field PHI scanning catches residual PHI in study descriptions, comments, and other free-text tags. Third: k-anonymity / l-diversity reporting per cohort gives a quantified re-identification-risk metric that an IRB can evaluate against protocol-specific thresholds.

What's the right pattern for an AI-vendor validation engagement?

Define the validation cohort under an IRB-approved protocol. Pull qualifying studies through anonymization into VNA Research. Grant the AI vendor a researcher account scoped to that cohort only. Vendor receives only de-identified data and cannot reach beyond their assigned cohort. Validation results flow back per data-use agreement; the audit trail records every export. Cohort retention follows the protocol's retention clause.

Plan a research-archive deployment.

Whether the goal is a longitudinal cohort, an AI-training corpus, or a federated multi-institution study, the conversation starts with your IRB protocol and your anonymization profile. We’ll come back within one business day.