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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-Bonmatí[email protected], [email protected]
Other facilities
Single Technology Flagship Node EoI EuBI
BIMCV
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
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
1 PAS
center
x PAS
center
CommunityPACS - AD
Community 2
Arterias Network
1 PAS
center
y PAS
center
CommunityPACS - AD
Community 3
Arterias Network
1 PAS
center
z PAS
center
CommunityPACS - AD
... ... ...
Solution. Logical Scheme
Two levels of image storage
(Local and Regional)
BIMCV
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
Q/R
• 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
• 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
• 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
Population Imaging. Quality Control
Acquisition Time
SNRSpatial Resolution
vóxelSize
susceptibility Artifacts Effect
Population Imaging. QC
Manual stimation by contrast Modification
AutomaticStimation
Population Imaging. QC
Translational Movement Rotational Movement
The * indicate movements of more than 1 mm
3602
rL
Population Imaging. QC
Frawise Displacement (Power) DVARS
iiiiziyixi dddFd
ixxiix ddd )1(
i titi YYI
21,, )(1DVARS(t)
FD and DVARS
Population Imaging. QC
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
Use case #1.- 10k Project Big Data in Brain Imaging
Population Imaging
• Cortical thickness, area and volume structure compared with the reference values
Use case #1.- 10k Project Big Data in Brain Imaging
Population Imaging
Use case #1.- 10k Project Big Data in Brain 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
Use case #4.- MIDAS (Massive Image Data Anatomy Spine)
Population Imaging
Use case #4.- MIDAS (Massive Image Data Anatomy Spine)
Population Imaging
Use case #4.- MIDAS (Massive Image Data Anatomy Spine)
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.