# Monitoring inclusions and quality criteria on MRI imaging data in a multi-center context

Laurent Vallet, Solène Jouan, Julien Louis, Alban Gaignard, Michael Kain,

Romain Bourcier, Richard Redon

► To cite this version:

Laurent Vallet, Solène Jouan, Julien Louis, Alban Gaignard, Michael Kain, et al.. Monitoring inclusions and quality criteria on MRI imaging data in a multi-center context. PEPR-SanteNum 2025 - Journées Annuelles du PEPR Santé Numérique, Oct 2025, Lille, France. pp.1-1, 2025. hal-05368780

HAL Id: hal-05368780

https://hal.science/hal-05368780v1

Submitted on 17 Nov 2025

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# Monitoring inclusions and quality criteria on MRI imaging data in a multi-center context

Laurent VALLET¹, Solène JOUAN⁴, Julien LOUIS², Alban GAIGNARD¹,³, Michael KAIN², Romain BOURCIER¹,⁴
and Richard REDON¹

1. Nantes Université, CHU Nantes, CNRS, INSERM, l'Institut du thorax, F-44000 Nantes, France

2. INRIA, Empenn Team, ERL U1228, F-35000 Rennes, France

3. IFB-core, Institut Français de Bioinformatique (IFB), CNRS, INSERM, INRAE, CEA, 91057 Evry, France

4. Neuroradiological Department, University Hospital Center of Nantes, France

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PROGRAMME
DE RECHERCHE
SANTÉ
NUMÉRIQUE

## FROM UNIVERSITY HOSPITALS TO A LARGE SCALE IMAGING DATASET FOR CLINICAL RESEARCH

Intracranial aneurysms are most often discovered incidentally. In the event of rupture, they cause subarachnoid hemorrhage with a mortality rate of 30-50%¹. A better understanding of their pathophysiology is crucial to detect aneurysms, predict their evolution and to optimize therapeutic strategies. The objective of the PEPR Santé Numérique Neurovasc is to set an optimal digital ecosystem to develop predictive tools and models for clinical research on intracranial aneurysms and stroke outcomes. Gathering a group of 29 University hospitals as contributors for the imaging dataset, we face the challenge of facilitating, filtering and monitoring this data collection to ensure quality and reliability, key factors for the effective training of predictive models.

## COLLECTING THE DATA

Imaging examinations are collected during care routine from different hospitals. The diversity of practices and acquisition devices among these contributing centers produces heterogeneous imaging data. This data source, stored in hospitals storage systems called PACS, contains clinical and personal informations at this point.

**Collecting those data requires querying the PACS and performing de-identification.**

Moreover, a **quality control at import** is required to ensure that only imaging data meeting the usage requirements are uploaded to the research database. It aims at saving storage resources as well as avoiding costly data curation efforts.

## THE DICOM STANDARD

The Digital Imaging and Communications in Medicine (DICOM) standard defines both a file format and a communication protocol. It is the worldwide reference for the storage, exchange, and interoperability of medical imaging data.

A DICOM study is organized as follows:

* it contains one folder for each acquisition, with each folder identified by a unique identifier.

* each acquisition folder stores the image data together with **associated metadata**, indexed by standardized identifying codes known as **DICOM tags**.

## SHANOIR SOFTWARE

Shanoir² is an **open-source database software** developed by INRIA with the resources and support from France Life Imaging. It is composed of:

* **Shanoir**: a web platform to administer medical imaging data. It **supports DICOM, BIDS, Bruker & EEG formats** and allows clinical study promoters to manage users rights, set participating centers informations, acquisition equipments and other features.

* **Shanoir Uploader**: a cross platform client made to **upload imaging data from a PACS** to the Shanoir server after pseudonymization³. Shanoir Uploader can run a quality filter at import as described below.

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## MONITORING THE IMAGING DATASET

In addition to the quality control, we developed a **secured API exposing real-time statistics** from Shanoir database for the benefit of study administrators and contributors.

* Follow progression of the studies, detect issues or delays

* Ensure consistency of the collected data

* Statistics are enriched with JSON-LD annotations to enhance discoverability

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Projet financé dans le cadre de France 2030 par l'ANR sous la référence ANR-22-PESN-0008

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