Towards Quantifying Neurovascular Resilience

Stefano Moriconi; Rafael Rehwald; Maria A. Zuluaga; H. Rolf Jäger; Parashkev Nachev; Sébastien Ourselin; M. Jorge Cardoso

doi:10.1007/978-3-030-33327-0_18

Towards Quantifying Neurovascular Resilience

Stefano Moriconi^*, Rafael Rehwald, Maria A. Zuluaga, H. Rolf Jäger, Parashkev Nachev, Sébastien Ourselin, M. Jorge Cardoso

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings
Editors	Hongen Liao, Guijin Wang, Yongpan Liu, Zijian Ding, Simone Balocco, Feng Zhang, Luc Duong, Renzo Phellan, Guillaume Zahnd, Shadi Albarqouni, Stefanie Demirci, Katharina Breininger, Stefano Moriconi, Su-Lin Lee
Publisher	SPRINGER
Pages	149-157
Number of pages	9
ISBN (Print)	9783030333263
DOIs	https://doi.org/10.1007/978-3-030-33327-0_18
Publication status	Published - 1 Jan 2019
Event	1st International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China Duration: 13 Oct 2019 → 13 Oct 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11794 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	1st International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Country/Territory	China
City	Shenzhen
Period	13/10/2019 → 13/10/2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-33327-0_18

Cite this

Moriconi, S., Rehwald, R., Zuluaga, M. A., Jäger, H. R., Nachev, P., Ourselin, S., & Cardoso, M. J. (2019). Towards Quantifying Neurovascular Resilience. In H. Liao, G. Wang, Y. Liu, Z. Ding, S. Balocco, F. Zhang, L. Duong, R. Phellan, G. Zahnd, S. Albarqouni, S. Demirci, K. Breininger, S. Moriconi, & S.-L. Lee (Eds.), Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings (pp. 149-157). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11794 LNCS). SPRINGER. https://doi.org/10.1007/978-3-030-33327-0_18

Moriconi, Stefano ; Rehwald, Rafael ; Zuluaga, Maria A. et al. / Towards Quantifying Neurovascular Resilience. Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings. editor / Hongen Liao ; Guijin Wang ; Yongpan Liu ; Zijian Ding ; Simone Balocco ; Feng Zhang ; Luc Duong ; Renzo Phellan ; Guillaume Zahnd ; Shadi Albarqouni ; Stefanie Demirci ; Katharina Breininger ; Stefano Moriconi ; Su-Lin Lee. SPRINGER, 2019. pp. 149-157 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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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.",

author = "Stefano Moriconi and Rafael Rehwald and Zuluaga, {Maria A.} and J{\"a}ger, {H. Rolf} and Parashkev Nachev and S{\'e}bastien Ourselin and Cardoso, {M. Jorge}",

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doi = "10.1007/978-3-030-33327-0_18",

language = "English",

isbn = "9783030333263",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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pages = "149--157",

editor = "Hongen Liao and Guijin Wang and Yongpan Liu and Zijian Ding and Simone Balocco and Feng Zhang and Luc Duong and Renzo Phellan and Guillaume Zahnd and Shadi Albarqouni and Stefanie Demirci and Katharina Breininger and Stefano Moriconi and Su-Lin Lee",

booktitle = "Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings",

note = "1st International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 13-10-2019",

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Moriconi, S, Rehwald, R, Zuluaga, MA, Jäger, HR, Nachev, P, Ourselin, S & Cardoso, MJ 2019, Towards Quantifying Neurovascular Resilience. in H Liao, G Wang, Y Liu, Z Ding, S Balocco, F Zhang, L Duong, R Phellan, G Zahnd, S Albarqouni, S Demirci, K Breininger, S Moriconi & S-L Lee (eds), Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11794 LNCS, SPRINGER, pp. 149-157, 1st International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, China, 13/10/2019. https://doi.org/10.1007/978-3-030-33327-0_18

Towards Quantifying Neurovascular Resilience. / Moriconi, Stefano; Rehwald, Rafael; Zuluaga, Maria A. et al.
Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings. ed. / Hongen Liao; Guijin Wang; Yongpan Liu; Zijian Ding; Simone Balocco; Feng Zhang; Luc Duong; Renzo Phellan; Guillaume Zahnd; Shadi Albarqouni; Stefanie Demirci; Katharina Breininger; Stefano Moriconi; Su-Lin Lee. SPRINGER, 2019. p. 149-157 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11794 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review

TY - CHAP

T1 - Towards Quantifying Neurovascular Resilience

AU - Moriconi, Stefano

AU - Rehwald, Rafael

AU - Zuluaga, Maria A.

AU - Jäger, H. Rolf

AU - Nachev, Parashkev

AU - Ourselin, Sébastien

AU - Cardoso, M. Jorge

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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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U2 - 10.1007/978-3-030-33327-0_18

DO - 10.1007/978-3-030-33327-0_18

M3 - Conference paper

AN - SCOPUS:85075755393

SN - 9783030333263

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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EP - 157

BT - Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings

A2 - Liao, Hongen

A2 - Wang, Guijin

A2 - Liu, Yongpan

A2 - Ding, Zijian

A2 - Balocco, Simone

A2 - Zhang, Feng

A2 - Duong, Luc

A2 - Phellan, Renzo

A2 - Zahnd, Guillaume

A2 - Albarqouni, Shadi

A2 - Demirci, Stefanie

A2 - Breininger, Katharina

A2 - Moriconi, Stefano

A2 - Lee, Su-Lin

PB - SPRINGER

T2 - 1st International Workshop on Machine Learning and Medical Engineering for Cardiovasvular Healthcare, MLMECH 2019, and the 8th International Joint Workshops on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019

Y2 - 13 October 2019 through 13 October 2019

ER -

Moriconi S, Rehwald R, Zuluaga MA, Jäger HR, Nachev P, Ourselin S et al. Towards Quantifying Neurovascular Resilience. In Liao H, Wang G, Liu Y, Ding Z, Balocco S, Zhang F, Duong L, Phellan R, Zahnd G, Albarqouni S, Demirci S, Breininger K, Moriconi S, Lee SL, editors, Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting - 1st International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI 2019, Proceedings. SPRINGER. 2019. p. 149-157. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-33327-0_18

Towards Quantifying Neurovascular Resilience

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