- Virtual angiography simulation from 3D and 3D+t brain vascular models
- Simulation d’angiographies virtuelles à partir de modèles vasculaires 3D et 3D+t
- 57 months (01/2013-09/2017)
- Project and partner 1: Nicolas Passat, CReSTIC, Université de Reims Champagne-Ardenne
- Partner 2: Jean-Paul Armspach, ICube, Université de Strasbourg
- Partner 3: Benoît Naegel, ICube, Université de Strasbourg
- Partner 4: Hugues Talbot, LIGM, ESIEE-Paris
- Partner 5: Christophe Prud'homme, IRMA, Université de Strasbourg
- Partner 6: Julien Jomier, Kitware-SAS
Context and purposes
In the last 20 years, progress in medical imaging has led to the development of modalities devoted to visualise vascular structures. These angiographic images progressively proved their usefulness in various clinical applications, in particular for cerebrovascular issues. However, these cerebral angiographic data are complex to analyse. This has motivated, since the mid 90’s, the proposal of several image processing tools for vessel analysis. Unfortunately, contrary to the morphological brain image analysis, for which synthetic (i.e., virtual) images and associated ground-truths (segmented data) are available, there are not such data in the case of cerebrovascular images. The interdisciplinary program starts from real MR angiographic data to finally lead to the generation of virtual MR angiographic data. During this process which leads to these simulated data, realistic 3D (anatomical) and 3D+t (hemodynamic) models –providing ground-truth for the virtual MRA images– are obtained. The purpose of this project is to develop a pipeline for the generation of virtual Magnetic Resonance Angiographies (MRA) of the human brain, associated to their anatomical (3D) and hemodynamic (3D+t) models (providing a ground-truth). The simulated data and ground-truths are currently not available for cerebral vascular networks. This results in a lack of common development, validation and comparison framework in the research fields related to vessel analysis.
The project needs to consider the following issues:
- The handling of inter-individual cerebrovascular variability, in the context of anatomical model generation.
- The numeric simulation of blood flows in complex geometries and in multiphysic and multiscale frameworks.
- The accurate simulation of the physical processes involved in MRA acquisition sequences in order to finally obtain realistic virtual angiographic images.
In order to do so, the successive steps are considered:
- Extraction of vascular volumes from real MRA images.
- Generation of 3D vascular models from these data.
- 3D+t simulation of blood flow in complex (arterial and venous) models.
- Simulation of MR acquisition of angiographic data from these 3D+t models.
The interaction between and within these steps requires strong interdisciplinary collaborations between computer science, applied mathematics, physics and medicine. The project combines all these expertises within the academia partners which are completed with the world industrial leader in open-science (open-source and open-data), Kitware, in bio-medical software and associated data infrastructures. In particular we develop upon the standard open frameworks developed by Kitware to efficiently deal with the issue of large data storage, handling, and computation to finally and effectively lead to operational data and software resources.
- Task 1: Data acquisition (coordinator: Jean-Paul Armspach)
- Task 2: Multimodal image processing (coordinator: Benoît Naegel)
- Task 3: 3D vascular complex model generation (coordinator: Hugues Talbot)
- Task 4: Blood flow simulation for 3D+t vascular model generation (coordinator: Stéphanie Salmon)
- Task 5: Virtual angiographic data simulation (coordinator: Jean-Paul Armspach)
Two major deliverables are set for this project:
- The obtained data (virtual images and associated 3D/3D+t models) will be finally available on the web, via a server accessible to the whole medical community.
- The methodological solutions developed at each step of the project will lead to software tools relying on standard (open source) software resources, then being also fully available to the medical image analysis community.
The common and reliable framework for the design, calibration, validation and comparison of angiographic image processing and analysis (filtering, segmentation, quantification, etc.) developed in this project will be an invaluable accelerator for the academic and industrial partners involved, their research and innovation works, thus leading to technological and medical improvements. The socio-economic importance of the research proposed is highlighted by the fact that vascular pathologies are one of the main causes of morbidity and mortality in the Western world, and thus constitute a crucial public health issue.