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Towards Quantifying Neurovascular Resilience

  • Stefano MoriconiEmail author
  • Rafael Rehwald
  • Maria A. Zuluaga
  • H. Rolf Jäger
  • Parashkev Nachev
  • Sébastien Ourselin
  • M. Jorge Cardoso
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11794)

Abstract

Whilst grading neurovascular abnormalities is critical for prompt surgical repair, no statistical markers are currently available for predicting the risk of adverse events, such as stroke, and the overall resilience of a network to vascular complications. The lack of compact, fast, and scalable simulations with network perturbations impedes the analysis of the vascular resilience to life-threatening conditions, surgical interventions and long-term follow-up. We introduce a graph-based approach for efficient simulations, which statistically estimates biomarkers from a series of perturbations on the patient-specific vascular network. Analog-equivalent circuits are derived from clinical angiographies. Vascular graphs embed mechanical attributes modelling the impedance of a tubular structure with stenosis, tortuosity and complete occlusions. We evaluate pressure and flow distributions, simulating healthy topologies and abnormal variants with perturbations in key pathological scenarios. These describe the intrinsic network resilience to pathology, and delineate the underlying cerebrovascular autoregulation mechanisms. Lastly, a putative graph sampling strategy is devised on the same formulation, to support the topological inference of uncertain neurovascular graphs.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Stefano Moriconi
    • 1
    Email author
  • Rafael Rehwald
    • 2
  • Maria A. Zuluaga
    • 3
  • H. Rolf Jäger
    • 2
  • Parashkev Nachev
    • 2
  • Sébastien Ourselin
    • 1
  • M. Jorge Cardoso
    • 1
  1. 1.School of Biomedical Engineering and Imaging SciencesKing’s College LondonLondonUK
  2. 2.Institute of NeurologyUniversity College LondonLondonUK
  3. 3.Universidad Nacional de ColombiaBogotáColombia

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