@neurIST complex information processing toolchain for the integrated management of cerebral aneurysms

Author:

Villa-Uriol M. C.12,Berti G.3,Hose D. R.4,Marzo A.4,Chiarini A.5,Penrose J.6,Pozo J.12,Schmidt J. G.7,Singh P.4,Lycett R.4,Larrabide I.12,Frangi A. F.128

Affiliation:

1. Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), Information and Communication Technologies Department, Universitat Pompeu Fabra, c/ Tanger 122–140, E08018 Barcelona, Spain

2. Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), c/ Tanger 122–140, E08018 Barcelona, Spain

3. Consulting in mathematical methods, Bonn, Germany

4. Medical Physics Group, Faculty of Medicine, University of Sheffield, Sheffield, UK

5. BioComputing Competence Centre SCS s.r.l., Casalecchio di Reno, Italy

6. ANSYS UK, Ltd, Abingdon, UK

7. University of Applied Science Koblenz, Germany

8. Institució Catalana de Recerca i Estudis Avançats (ICREA), c/ Tanger 122–140, E08018 Barcelona, Spain

Abstract

Cerebral aneurysms are a multi-factorial disease with severe consequences. A core part of the European project @neurIST was the physical characterization of aneurysms to find candidate risk factors associated with aneurysm rupture. The project investigated measures based on morphological, haemodynamic and aneurysm wall structure analyses for more than 300 cases of ruptured and unruptured aneurysms, extracting descriptors suitable for statistical studies. This paper deals with the unique challenges associated with this task, and the implemented solutions. The consistency of results required by the subsequent statistical analyses, given the heterogeneous image data sources and multiple human operators, was met by a highly automated toolchain combined with training. A testimonial of the successful automation is the positive evaluation of the toolchain by over 260 clinicians during various hands-on workshops. The specification of the analyses required thorough investigations of modelling and processing choices, discussed in a detailed analysis protocol. Finally, an abstract data model governing the management of the simulation-related data provides a framework for data provenance and supports future use of data and toolchain. This is achieved by enabling the easy modification of the modelling approaches and solution details through abstract problem descriptions, removing the need of repetition of manual processing work.

Publisher

The Royal Society

Subject

Biomedical Engineering,Biomaterials,Biochemistry,Bioengineering,Biophysics,Biotechnology

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2. Assessment of intracranial aneurysm rupture risk using a point cloud-based deep learning model;Frontiers in Physiology;2024-02-15

3. Exploring the Variations in Angles Around Basilar Bifurcation Categorized by Aneurysm Locations;2023 15th Biomedical Engineering International Conference (BMEiCON);2023-10-28

4. Variations of Middle Cerebral Artery Hemodynamics Due to Aneurysm Clipping Surgery;Journal of Engineering and Science in Medical Diagnostics and Therapy;2023-09-26

5. Hemodynamics of thrombus formation in intracranial aneurysms: An in silico observational study;APL Bioengineering;2023-07-07

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