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BIMCV Medical Imaging Databank of the Valencia Region

Synergy between data in population medical imaging, computer aided diagnosis and augmented reality

María de la Iglesia-Vayá & Luis Martí-Bonmatídelaiglesia_mar@gva.es, marti_lui@gva.es

Other facilities

Single Technology Flagship Node EoI EuBI

http://www.eurobioimaging-interim.eu/spain.html

BIMCV consortium. https://ceib.cipf.es/bimcv

Consortium Members:

1.- CEIB-CS (Regional Ministry of Health)2.- La Fe Polytechnic University Hospital3.- CIPF4.- I3M. U.P.V.5.- Universidad Valencia5.- Universidad Alicante6.- QuiBIM

BIMCV consortium. https://ceib.cipf.es/xnat

Genesis: GIMD

P.A.S. community

VNA - Vendor Neutral Archive (Regional)

2 x CPD (Data Center)

Community 1

Arterias Network

center

CommunityPACS - AD

Community 2

Arterias Network

center

CommunityPACS - AD

Community 3

Arterias Network

center

CommunityPACS - AD

... ... ...

Solution. Logical Scheme

Two levels of image storage

(Local and Regional)

Image request associated with an

ongoing study anonymised

Functional DICOM Circuit

Population Imaging – 24 Departments of Heath

ARTERIAS - WAN

Population Imaging – Arterias WAN

PROTECTED ARTERIAS WAN Research LAN

VPNVirtual Private Network

BIMCVGIMD

• Biobanks Repositories of biological samples.• Emerged as a fundamental tool for clinical research and innovation in

genomics and personalized medicine through quality control issues for sample collections, standardized pathways for extraction and sophisticated protocols for data protection.

• More recently, virtual biobanks, as repositories of digital information, have increased the opportunities for sharing, federating and exploiting biobank’s data.

Imaging Biobanks

• Generate a structured and fully anonymized information, including medical images and relevant clinical and associated biological data and/or samples.

‒ Standard data formats and annotation through ontologies.‒ Dissociated data will allow traceability of cases in unexpected findings. ‒ Verified quality of the data.

• An infrastructure with massive storage and computing capacity.‒ Large data samples involve establishing case scenarios and determine the

universalization of the results.‒ High performance computing resources to facilitate image processing comparison,

standardization and validation.‒ Integrate resources and services through a platform managing information flow and

image processing and extraction‒ Provide support to users for its utilization.

Imaging Biobanks

What is BIMCV ?

BIMCV - Medical Imaging Databank of the Valencia Region …… is an infrastructure with mass storage capacity (through GIMD – Project from the Regional Ministry of Health in the Valencia Region) and high throughput computational modeling capabilities.Aim To transform the Medical Imaging Databank into an environment for translational innovation in healthcare interventions and management.

• Generate a structured and fully anonymized information, including medical images and relevant clinical and associated biological data and/or samples. Besides, dissociated data will allow traceability of cases in unexpected findings (De- identification).

• Standard data formats and annotation through ontologies.

• Quality Control. Verified quality of the data.

Principal Components of a Imaging Biobank

• Anonymization of DICOM Headers following the standard http://dicom.nema.org/standard.html

• Face anonymization

• Anonymization of the text printed on the image

Anonymization

Recepción de normas de anonimización (en bruto)

Transformación de las normas en estructuras para lacomputación

DICOM Part 15: Security and System Management Profiles

Creación código de anonimización

Imagen con cabeceras sin anonimizar

Cabeceras DICOManonimizadas

Ejecución de la anonimización.

Código de anonimización

Anonymization of DICOM Headers

Population Imaging – Anonymization

Implementation of the 10 DICOM Confidentiality Profiles with CTP-RSNA

Software are not aware of any tool aimed to develop all confidentiality profiles defined in Part 15 of the DICOM standard in paragraph E ‘Attribute Confidentiality Profiles’

(http://dicom.nema.org/medical/dicom/current/output/html/part15.html#chapter_E).

In order to avoid compromising the privacy, we consider crucial to implement secure and robust software modules in the personal data protection frameworkWithin the DICOM standard, ten profiles are defined as listed:

Population Imaging – AnonymizationImplementation of the 10 DICOM Confidentiality Profiles with CTP-RSNA

This profile is the most stringent on and removes all information concerning:

• The identity as well as identifying and demographic characteristics of the patient• The identity of the authors, persons responsible or family members• The identity of any personnel involved in the procedure• The identity of the organisations involved in ordering or carrying out the method• The information (not relative to the patient) that could be used to find out the identity of the original files not anonymized (eg UID, date and time)• The private attributes (which are not part of the standard). Some manufacturers keep important information for the image, e.g. gradients used in DTI.

Basic profile

Population Imaging – Anonymization

o Anonimización de las tags del estándar DICOM del nivel de aplicación básica del perfil de confidencialidad:

▪ DICOM PS3.6 2015a - Data Dictionary.

▪ DICOM PS3.15 2015a - Security and System Management Profiles.

● E Attribute Confidentiality Profiles (which attributes should be anonymized)

http://dicom.nema.org/medical/dicom/current/output/

Implementation of the 10 DICOM Confidentiality Profiles with CTP-RSNA

Face anonymization

Text Detection by:

• Digital image processing

• Feature comparison in the regions of interest

Anonymization of the text printed on the image

Population Imaging. Quality Control

Acquisition Time

SNRSpatial Resolution

vóxelSize

susceptibility Artifacts Effect

Population Imaging. QC

Manual stimation by contrast Modification

AutomaticStimation

Translational Movement Rotational Movement

The * indicate movements of more than 1 mm

Frawise Displacement (Power) DVARS

iiiiziyixi dddFd

ixxiix ddd )1(

i titi YYI

21,, )(1DVARS(t)

FD and DVARS

Test FD y DVARS

FD and DVARS

Structured QC Report

Example

Population ImagingUse cases with BIMCV

Use case #1.- 10k Project Big Data in Brain Imaging

Population Imaging

10 k is a collaborative project with the San Juan & Sagunto Hospitals. Maria de la Iglesia-Vayá, PhD. & Jose Maria Salinas, PhD.

Population Imaging

• Cortical thickness, area and volume structure compared with the reference values

Population Imaging

Imaging Biomarkers.

Blood Lab

Imaging Biomarkers

Imaging Lab Quibim_Quiron. Spin-off

Example

Use case #2.- BrainGIS (Brain Geografic Information System)

Population Imaging – Data Mining

Use case #2.- BrainGIS (Brain Geografic Information System)

Population Imaging – Data Mining

Use case #3.- NeuroBIM-MS (Multiple Sclerosis)

Population Imaging

Hospital Vega Baja de OrihuelaDr. Santiago Mola - Head of Neurology

Hospital General Universitario de AlicanteDr. Angel Pérez - Neurologist specialist

Hospital Universitario San Juan de AlicantePhD. Jose María Salinas - Head of Information

Technology and associate professor at the University of Alicante

Phd. Miguel Angel CazorlaRoVit research group manager

Use case #4.- MIDAS (Massive Image Data Anatomy Spine)

Population Imaging

MIDAS is a collaborative project with the Arnau de Villanova Hospital. Traumatology Service. Dr. Julio Domenech Fernandez

Population Imaging

https://sourceforge.net/projects/spinalcordtoolbox

Use case #5.- Augmented Reality for Visualization

Population Imaging. Visualization

Parametric image by Augmented Reality from Gonzalo M. Rojas. Download from Smartphone Play Store, the Prototype for Android: ARiBraiN3T.

http://www.aribrain.info

http://www.esf.org/activities/forward-looks/medical-sciences-emrc/current-forward-looks-in-medical-sciences/personalised-medicine-for-the-european-citizen/more-information.html.

Population Imaging as part of Big Data landscape

Data Sharing: Code, Manage & Collaborate

Data Sharing - Open minds

The Landscape of BIMCV - Open source

Acknowledgement

• Luis Martí-Bonmatí, Head of Biomedical Imaging Research Group (GIBI230) at La Fe Polytechnics and University Hospital – La Fe Health Research Institute.

• Oscar Zurriga Llorens, Director General of Research, Innovation Technolgy and Quality. Regional Ministry of Health in the Valencia Region.

• Carmen Ferrer Ripollés, Deputy Director General of Information Systems for Health. Regional Ministry of Health in the Valencia Region.

• Salvador Peiro Moreno, Deputy Director General of Research and Innovation. Regional Ministry of Health in the Valencia Region.

• Ignacio Blanquer, Institute of Instrumentation for Molecular Imaging – I3M. Universitat Politècnica de València.

• Jacobo Martínez, Director of FISABIO.• Carlos Martinez Riera, Coordinator European Research projects office, University of

Valencia.• Jose María Salinas, University Hospital San Juan de Alicante. Head of Information

Technology and associate professor at the University of Alicante. • GIMD team. Regional Ministry of Health in the Valencia Region & General Electric.• Rafael de Andrés and Timo Zimmerman, The Spanish representatives in the Euro-

BioImaging Interim Board.

Acknowledgements

Thank you so much to my team

Members CEIB-CS•María de la Iglesia Vayá, PhD. Team Leader.•Ángel Fernández-Cañada Vilata, MSc.•José Miguel Calderón Terol, MSc.•Jhon Jairo Sáenz Gamboa, MSc.

Past Member CEIB-CS•Jorge Isnardo Altamirano, MSc.

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