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INTRODUCTION

          • Introduction

          • Current status of bioimaging in the AVS
            (Valencia Health Agency)

          • CEIB Objectives

          • Project Overview

          • System parts

          • R&D

          • Conclusions
EVOLUTION OF BIOIMAGING
CURRENT STATUS OF AVS BIOIMAGE PROJECT
Proof of Concept of Centralized Medical Imaging Management (GIMC)
CURRENT STATUS OF AVS BIOIMAGE PROJECT
               PACS DEPARTAMENTALES




                                      Servicios DICOM Q/R
                                      Acceso WADO


  Bus SOA




                                            GIMC
CURRENT STATUS OF AVS BIOIMAGING PROJECT
CEIB
Reference Centre to provide excellence in
bioimaging from a common repository




  Cardiology
CEIB
Reference Centre to provide excellence in
bioimaging from a common repository




  Neurology
CEIB
Reference Centre to provide excellence in
bioimaging from a common repository




  Angiography
CEIB WORKING LINES

Use of an image biobank for R & D and
transferring specialized knowledge to the patient's
electronic medical/health record (EHR)
   o   Development of an outstanding “search engine” through
       cataloged and indexed biobank for information management.

   o   Information validation and quality control

   o   Image Post-processing

   o   Systems Decision Support Imaging
CEIB GOALS

Development of an outstanding “search engine” through
cataloged and indexed biobank for information management

   o   Search for images, control cohort, discoveries, clinical trials, etc.

   o   Knowledge of the population by means of image quantitative
       markers

   o   Structuring radiological examinations from EHR through DICOM
       SR standard.
CEIB GOALS

Information validation and quality control


oValidation   at the level of studies (protocols, alerts)

oValidation   at the level of data (standardization)

oQuality  control regarding to radiation doses by considering both
patients and studies
CEIB GOALS

Image Post-processing

   o   Centralizing image post-processing in clinical trials

   o   Validation of new techniques of processing and obtaining
       quantitative marks

   o   Development of tools for image processing

   o   Development, standardization and validation of Image
       Biomarkers
CEIB GOALS

Computing Supported / Aided Decision through Bioimage

   o   Creation of virtual net-communities for users with common
       interests

   o   Participation in international projects (Connectome)

   o   Design and implementation of tools for the development of
       Computing Aided Decision of medical systems
SYSTEM OVERVIEW. CURRENT INFRASTRUCTURE

SYSTEM AREA: ALSIS PLATFORM, HITACHI
COMMUNICATION AREA: ARTERIAS
CEIB LAB
HOSPITAL PACS
                                           HITACHI
                                 Bus SOA




                                Arterias




                Lab CEIB                    ALSIS
SYSTEM COMPONENTS

GIMC                Search Engine (SE)




                      Anonymizer (ANOM)


                                 DICOM
                                 Morphing
                                  BET
SYSTEM OVERVIEW
                       EHR



   CLOUD CEIB R & D



                      GIMC

               BIKE


    GEBID
SYSTEM COMPONENTS
R & D BIOIMAGE CLINICAL TRIAL MANAGER SYSTEM (GEBID)
SYSTEM COMPONENTS
R & D BIOIMAGE CLINICAL TRIAL MANAGER SYSTEM (GEBID)
SYSTEM COMPONENTS
R & D BIOIMAGE CLINICAL TRIAL MANAGER SYSTEM (GEBID)
SYSTEM COMPONENTS
R & D BIOIMAGE CLINICAL TRIAL MANAGER SYSTEM (GEBID)
SYSTEM COMPONENTS
BIOIMAGING KNOWLEDGE ENGINE (BIKE) Techniques
BIKE-OLOGIES




                                                Bike-                 Bike-                 Bike-
  Bike-Cardio         Bike-Vascular
                                            Orthopaedics          NeuroImaging           Datamining
                        Morphological
  Quantification of                            Diagnosis of            Detection of
                         analysis of                                                     Radiation Exposure
  cardiac function                             osteoporosis        epileptogenic focus
                         aneurysms

                                                Spine disease          Dopamine
  Electrophysiology
                       Virtual treatment        diagnosis and        transmission         Quality Control
      planning
                                             treatment planning      quantification




                                           BIKE-OLOGIAS
                                      BioImage Knowledge Engine
SYSTEM COMPONENTS
BIKE-Postprocessing




                  Angel Alberic. Cuantificación Quirón
SYSTEM COMPONENTS
BIKE-Postprocessing: Computational Models




Martí Bonmatí L, et al. Biomarcadores de imagen, imagen cuantitativa y bioingeniería. Radiología. 2011
SYSTEM COMPONENTS
BIKE-Imaging: Quantification and Biomarkers
Martí Bonmatí L, et al. Biomarcadores de
SYSTEM COMPONENTS                  imagen, imagen cuantitativa y
                                   bioingeniería. Radiología. 2011
BIKE-Imaging: Feature Extraction
SYSTEM COMPONENTS
BIKE-Datamining
R&D. WE HAVE A DREAM
Return the R & D through the EHR by the transmission of knowledge to
the patient
R&D
Return on R & D to the EHR by the transmission of knowledge to the
patient
R&D
Return on R & D to the EHR by the transmission of knowledge to the
patient
Title:
CEIB Experience: A R&D cloud services in bioimaging oriented to integration of environments with EHR
Abstract:
Purpose:
The EHR of the Valencia Health Service (Spain) offers highly specialized solutions. The purpose of this work is to present the
centralization of “Valencia Biobank of Medical Imaging” (GIMC). This project will allow R&D support for the scientific community
through the implementation of logical services based in Open Source technologies.
Methods and Materials:
Centralized storage image systems are a milestone in the strategic framework of the AVS. That is why obtained images from the entire
population, are a sample of great value that will form the basis of knowledge for the future science. The defined architecture includes a
Search Engine, a platform to support the management of clinical trials with biomedical imaging (GEBID) and a Bioimaging
Knowledge Engine (BIKE).
Results:
Both CEIB and GIMC provide an ideal data source for analyzing the acquired image data. In particular, the following actions are
prepared: development and validation of an Image Bank, a Decision Support Systems, an Imaging Biomarkers platform, Virtual
Communities as users’ networks and Image Processing Tools libraries.
Conclusion:
As a result of the data management from the Biobank and the development of Cloud services-CEIB, digital medical imaging and
computer processing, several biomarker parameters and quality innovations will be develop and shown. This initiative will allow the
“Valencia Biobank of Medical Imaging” to achieve excellence in care and experimental medicine in the field of Bioimaging. The main
final objective of this process is that the results of R&D will be transferred to the patient, providing value-added elements to their
EHR.in bioimaging oriented to integration of environments with EHR



                          THANKS FOR YOUR ATTENTION

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Maria de la Iglesia - CEIB: a R&D services in bioimaging oriented to integration of environments with her on cloud computing

  • 1.
  • 2. INTRODUCTION • Introduction • Current status of bioimaging in the AVS (Valencia Health Agency) • CEIB Objectives • Project Overview • System parts • R&D • Conclusions
  • 4. CURRENT STATUS OF AVS BIOIMAGE PROJECT Proof of Concept of Centralized Medical Imaging Management (GIMC)
  • 5. CURRENT STATUS OF AVS BIOIMAGE PROJECT PACS DEPARTAMENTALES Servicios DICOM Q/R Acceso WADO Bus SOA GIMC
  • 6. CURRENT STATUS OF AVS BIOIMAGING PROJECT
  • 7. CEIB Reference Centre to provide excellence in bioimaging from a common repository Cardiology
  • 8. CEIB Reference Centre to provide excellence in bioimaging from a common repository Neurology
  • 9. CEIB Reference Centre to provide excellence in bioimaging from a common repository Angiography
  • 10. CEIB WORKING LINES Use of an image biobank for R & D and transferring specialized knowledge to the patient's electronic medical/health record (EHR) o Development of an outstanding “search engine” through cataloged and indexed biobank for information management. o Information validation and quality control o Image Post-processing o Systems Decision Support Imaging
  • 11. CEIB GOALS Development of an outstanding “search engine” through cataloged and indexed biobank for information management o Search for images, control cohort, discoveries, clinical trials, etc. o Knowledge of the population by means of image quantitative markers o Structuring radiological examinations from EHR through DICOM SR standard.
  • 12. CEIB GOALS Information validation and quality control oValidation at the level of studies (protocols, alerts) oValidation at the level of data (standardization) oQuality control regarding to radiation doses by considering both patients and studies
  • 13. CEIB GOALS Image Post-processing o Centralizing image post-processing in clinical trials o Validation of new techniques of processing and obtaining quantitative marks o Development of tools for image processing o Development, standardization and validation of Image Biomarkers
  • 14. CEIB GOALS Computing Supported / Aided Decision through Bioimage o Creation of virtual net-communities for users with common interests o Participation in international projects (Connectome) o Design and implementation of tools for the development of Computing Aided Decision of medical systems
  • 15. SYSTEM OVERVIEW. CURRENT INFRASTRUCTURE SYSTEM AREA: ALSIS PLATFORM, HITACHI COMMUNICATION AREA: ARTERIAS CEIB LAB HOSPITAL PACS HITACHI Bus SOA Arterias Lab CEIB ALSIS
  • 16. SYSTEM COMPONENTS GIMC Search Engine (SE) Anonymizer (ANOM) DICOM Morphing BET
  • 17. SYSTEM OVERVIEW EHR CLOUD CEIB R & D GIMC BIKE GEBID
  • 18. SYSTEM COMPONENTS R & D BIOIMAGE CLINICAL TRIAL MANAGER SYSTEM (GEBID)
  • 19. SYSTEM COMPONENTS R & D BIOIMAGE CLINICAL TRIAL MANAGER SYSTEM (GEBID)
  • 20. SYSTEM COMPONENTS R & D BIOIMAGE CLINICAL TRIAL MANAGER SYSTEM (GEBID)
  • 21. SYSTEM COMPONENTS R & D BIOIMAGE CLINICAL TRIAL MANAGER SYSTEM (GEBID)
  • 22. SYSTEM COMPONENTS BIOIMAGING KNOWLEDGE ENGINE (BIKE) Techniques
  • 23. BIKE-OLOGIES Bike- Bike- Bike- Bike-Cardio Bike-Vascular Orthopaedics NeuroImaging Datamining Morphological Quantification of Diagnosis of Detection of analysis of Radiation Exposure cardiac function osteoporosis epileptogenic focus aneurysms Spine disease Dopamine Electrophysiology Virtual treatment diagnosis and transmission Quality Control planning treatment planning quantification BIKE-OLOGIAS BioImage Knowledge Engine
  • 24. SYSTEM COMPONENTS BIKE-Postprocessing Angel Alberic. Cuantificación Quirón
  • 25. SYSTEM COMPONENTS BIKE-Postprocessing: Computational Models Martí Bonmatí L, et al. Biomarcadores de imagen, imagen cuantitativa y bioingeniería. Radiología. 2011
  • 27. Martí Bonmatí L, et al. Biomarcadores de SYSTEM COMPONENTS imagen, imagen cuantitativa y bioingeniería. Radiología. 2011 BIKE-Imaging: Feature Extraction
  • 29. R&D. WE HAVE A DREAM Return the R & D through the EHR by the transmission of knowledge to the patient
  • 30. R&D Return on R & D to the EHR by the transmission of knowledge to the patient
  • 31. R&D Return on R & D to the EHR by the transmission of knowledge to the patient
  • 32. Title: CEIB Experience: A R&D cloud services in bioimaging oriented to integration of environments with EHR Abstract: Purpose: The EHR of the Valencia Health Service (Spain) offers highly specialized solutions. The purpose of this work is to present the centralization of “Valencia Biobank of Medical Imaging” (GIMC). This project will allow R&D support for the scientific community through the implementation of logical services based in Open Source technologies. Methods and Materials: Centralized storage image systems are a milestone in the strategic framework of the AVS. That is why obtained images from the entire population, are a sample of great value that will form the basis of knowledge for the future science. The defined architecture includes a Search Engine, a platform to support the management of clinical trials with biomedical imaging (GEBID) and a Bioimaging Knowledge Engine (BIKE). Results: Both CEIB and GIMC provide an ideal data source for analyzing the acquired image data. In particular, the following actions are prepared: development and validation of an Image Bank, a Decision Support Systems, an Imaging Biomarkers platform, Virtual Communities as users’ networks and Image Processing Tools libraries. Conclusion: As a result of the data management from the Biobank and the development of Cloud services-CEIB, digital medical imaging and computer processing, several biomarker parameters and quality innovations will be develop and shown. This initiative will allow the “Valencia Biobank of Medical Imaging” to achieve excellence in care and experimental medicine in the field of Bioimaging. The main final objective of this process is that the results of R&D will be transferred to the patient, providing value-added elements to their EHR.in bioimaging oriented to integration of environments with EHR THANKS FOR YOUR ATTENTION

Notes de l'éditeur

  1. C – E - I – B : CENTRE OF EXCELENCE FOR BIOMEDICAL IMAGING, IN SPANISH CEIB-AVS
  2. I WILL DIVIDE MY PRESENTATION IN THIS TOPICS I WILL TAK ABOUT
  3. IN THE PAST THE RADIOLGIST ANALIZED THE X-RAY IMAGE TAKING A LOOK NOWADAYS THE PACS AND DIAGNOSTIC STATION ARE THE TOOLS THAT THESE PROFESIONALS ARE USING AND OF COURSE THE EVOLUTION OF BIOIMAGING IS FOCUSED TO COMPUTATIONAL MODELS AND THE IMAGE BIOMARKERS.
  4. THE ARQUITECTURE OF THE SYSTEM CONSISTS OF….
  5. AS YOU CAN SEE
  6. THIS IMAGE REPOSITORY SUPORTS ALL IMAGES THAT ARE PRODUCED BY VALENCIAN HOSPITALS THIS REPOSITORY WILL OFFER SERVICES FOR DIFERENT APLICATIONS VIA STANDARD WADO (WEB ACCESS DICOM OBJECT)
  7. CEIB IS AIMED TO BE A RE…
  8. AND SO ON
  9. I SHOW YOU A BRIEF SUMMARY OF THE WORKING LINES
  10. IF WE FOCUS IN THE FIRST POINT OUR MAIN GOALS ARE 1 2 3
  11. THE SECOND GOAL LEAD AS TO THE NEXT TOPICS
  12. THE NEXT GOAL IS RELATED TO IMAGE POST-PORCESING THROUGH
  13. THE NEXT GOAL IS COMPUTING SUPPORTED…
  14. THE ARQUITECTURE OF OUR SYSTEM WILL CONSIS IN THE FUTURE OF A CLOUD CEIB R&D THAT IS COMPOSED BY GEBID AND BIKE
  15. IS BASED IN XNAT FRAMEWORK WHICH ALLOWS THE ORGANITATION AND SHARING OF BIOIMAGE DATA
  16. AS YOU CAN SEE THIS FRAMEWORK SUPPORS DIFERENT KINDS OF DATA RELATED TO CLINICAL TRIAL
  17. XNAT ORGANIZES THE WORK FLOW IN CLINICAL TRIALS
  18. XNAT FRAMEWORK IS DIVIDED IN THREE LEVELS
  19. THE SECOND ELEMENT OF CLOUD CEIB R&D IS BIKE THAT MEANS… THIS IS OUR CONCEPTUAL FRAMEWORK TO HIGH COMPUTATIONAL FOR BIOMEDICAL IMAGING WE HAVE TWO CRITERIA IN ORDER TO DEVELOP THIS …..
  20. I WANT TO REMARK THAT ALL OUR SYSTEM IS BASED BY OPEN SOURCE
  21. Figura 2 Proceso de extracción de información relevante mediante la creación de imágenes paramétricas
  22. Izquierda, reconstrucción 3D de hueso trabecular extraída a partir de imágenes de RM de alta resolución espacial. Centro, conversión de la geometría de la estructura trabecular a un modelo de elementos finitos basado en peque˜nos elementos hexaedro. En esta imagen tridimensional, cada peque˜no cubo corresponde a un elemento finito. Cada elemento tiene unas propiedades físicoquímicas, correspondientes a hueso en este ejemplo, y está unido al resto de elementos finitos formando una malla. Derecha, mapa paramétrico de las tensiones nodales resultado de la simulación de compresión de la estructura trabecular. Valores elevados (codificados en rojo) indican tensiones nodales elevadas, es decir, regiones que se corresponden con un mayor riesgo de rotura.
  23. Figura 1 Mapa de procesos para el establecimiento y validación de un biomarcador de imagen. En la parte izquierda de la figura se representan los ítems médico-asistenciales y en la derecha los metodológicos.
  24. Figura 2 Proceso de extracción de información relevante mediante la creación de imágenes paramétricas
  25. DICOM TAGS
  26. I WANT TO CONFESS YOU THAT WE HAVE A DREAM….. FOR EXAMPLE WIHT HIGH SPECIALIZAD REPORTS
  27. THIS IS OUR PROPOSAL AND THANS FOR YOUR ATTENTION