In Vitro Modeling of Human Brain Arteriovenous Malformation for Endovascular Simulation and Flow Analysis

Kaneko, Naoki; Ullman, Henrik; Ali, Fadil; Berg, Philipp; Ooi, Yinn Cher; Tateshima, Satoshi; Colby, Geoffrey P.; Komuro, Yutaro; Hu, Peng; Khatibi, Kasra; Mejia, Lucido Luciano Ponce; Szeder, Viktor; Nour, May; Guo, Lea; Chien, Aichi; Viñuela, Fernando; Nemoto, Shigeru; Mashiko, Toshihiro; Sehara, Yoshihide; Hinman, Jason D; Duckwiler, Gary; Jahan, Reza

doi:10.1016/j.wneu.2020.06.084

Cited by 18 publications

(16 citation statements)

References 19 publications

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“…The other major advantages of using 3D printed models for VMs assessment are those related to surgery planning and surgery training 96 . Some institutions are starting to model and print VMs integrating them into hydraulic circuits to create the most realistic representation of real VMs 97 . These printed models allow a more accurate planning of surgical and interventional procedures, and selecting the most appropriate approach 98 .…”

Section: Present and Future Insightsmentioning

confidence: 99%

Conventional and advanced MRI evaluation of brain vascular malformations

Martín-Noguerol

Concepción-Aramendía

Lim

et al. 2021

Journal of Neuroimaging

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Vascular malformations (VMs) of the central nervous system (CNS) include a wide range of pathological conditions related to intra and extracranial vessel abnormalities. Although some VMs show typical neuroimaging features, other VMs share and overlap pathological and neuroimaging features that hinder an accurate differentiation between them. Hence, it is not uncommon to misclassify different types of VMs under the general heading of arteriovenous malformations. Thorough knowledge of the imaging findings of each type of VM is mandatory to avoid these inaccuracies. Conventional MRI sequences, including MR angiography, have allowed the evaluation of CNS VMs without using ionizing radiation. Newer MRI techniques, such as susceptibility‐weighted imaging, black blood sequences, arterial spin labeling, and 4D flow imaging, have an added value of providing physiopathological data in real time regarding the hemodynamics of VMs. Beyond MR images, new insights using 3D printed models are being incorporated as part of the armamentarium for a noninvasive evaluation of VMs. In this paper, we briefly review the pathophysiology of CNS VMs, focusing on the MRI findings that may be helpful to differentiate them. We discuss the role of each conventional and advanced MRI sequence for VMs assessment and provide some insights about the value of structured reports of 3D printing to evaluate VMs.

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Section: Present and Future Insightsmentioning

confidence: 99%

Conventional and advanced MRI evaluation of brain vascular malformations

Martín-Noguerol

Concepción-Aramendía

Lim

et al. 2021

Journal of Neuroimaging

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“…Microfluidic chips have been widely used to study neural cells and NoN and to build various ex vivo organ-on-chip models ( Geraili et al, 2018 ; Holloway et al, 2021 ), including kidney ( Asif et al, 2020 ), bone ( George et al, 2018 ; Sheyn et al, 2019 ; Truesdell et al, 2020 ), heart ( Kofron and Mende, 2017 ), liver ( Maschmeyer et al, 2015 ), muscle ( Morimoto et al, 2013 ), and brain ( Kaneko et al, 2020 ). The advantages of microfluidics in high-throughput, high-efficiency integration, miniaturization, flexible architecture, and low costs in fabrication speed up the microfluidics-based ex vivo NoN studies ( Sharma et al, 2013 ; Kou et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

Lab-on-Chip Microsystems for Ex Vivo Network of Neurons Studies: A Review

Zhang

Rong

Bian

2022

Front. Bioeng. Biotechnol.

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Increasing population is suffering from neurological disorders nowadays, with no effective therapy available to treat them. Explicit knowledge of network of neurons (NoN) in the human brain is key to understanding the pathology of neurological diseases. Research in NoN developed slower than expected due to the complexity of the human brain and the ethical considerations for in vivo studies. However, advances in nanomaterials and micro-/nano-microfabrication have opened up the chances for a deeper understanding of NoN ex vivo, one step closer to in vivo studies. This review therefore summarizes the latest advances in lab-on-chip microsystems for ex vivo NoN studies by focusing on the advanced materials, techniques, and models for ex vivo NoN studies. The essential methods for constructing lab-on-chip models are microfluidics and microelectrode arrays. Through combination with functional biomaterials and biocompatible materials, the microfluidics and microelectrode arrays enable the development of various models for ex vivo NoN studies. This review also includes the state-of-the-art brain slide and organoid-on-chip models. The end of this review discusses the previous issues and future perspectives for NoN studies.

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“…17 Only recently, Kaneko et al manufactured a phantom of a brain AVM for interventional simulations by 3D printing, and were able to reproduce the vessels of the nidus up to the limit of spatial resolution of the 3D angiograms (< 150 lm). 18 Published computational and experimental works have successfully reproduced the patient-specific haemodynamics of AVMs, and simulated the embolisation procedure for treatment planning. However, a framework that can be generalised and implemented in near real-time to support AVM treatment planning has yet to be developed.…”

Section: Introductionmentioning

confidence: 99%

A Computational Framework for Pre-Interventional Planning of Peripheral Arteriovenous Malformations

Franzetti

Bonfanti

Tanade

et al. 2021

Cardiovasc Eng Tech

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Purpose Peripheral arteriovenous malformations (pAVMs) are congenital lesions characterised by abnormal high-flow, low-resistance vascular connections—the so-called nidus—between arteries and veins. The mainstay treatment typically involves the embolisation of the nidus, however the complexity of pAVMs often leads to uncertain outcomes. This study aims at developing a simple, yet effective computational framework to aid the clinical decision making around the treatment of pAVMs using routinely acquired clinical data. Methods A computational model was developed to simulate the pre-, intra-, and post-intervention haemodynamics of a patient-specific pAVM. A porous medium of varying permeability was employed to simulate the sclerosant effect on the nidus haemodynamics. Results were compared against clinical data (digital subtraction angiography, DSA, images) and experimental flow-visualization results in a 3D-printed phantom of the same pAVM. Results The computational model allowed the simulation of the pAVM haemodynamics and the sclerotherapy-induced changes at different interventional stages. The predicted inlet flow rates closely matched the DSA-derived data, although the post-intervention one was overestimated, probably due to vascular system adaptations not accounted for numerically. The nidus embolization was successfully captured by varying the nidus permeability and increasing its hydraulic resistance from 0.330 to 3970 mmHg s ml−1. The nidus flow rate decreased from 71% of the inlet flow rate pre-intervention to 1%: the flow completely bypassed the nidus post-intervention confirming the success of the procedure. Conclusion The study demonstrates that the haemodynamic effects of the embolisation procedure can be simulated from routinely acquired clinical data via a porous medium with varying permeability as evidenced by the good qualitative agreement between numerical predictions and both in vivo and in vitro data. It provides a fundamental building block towards a computational treatment-planning framework for AVM embolisation.

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In Vitro Modeling of Human Brain Arteriovenous Malformation for Endovascular Simulation and Flow Analysis

Cited by 18 publications

References 19 publications

Conventional and advanced MRI evaluation of brain vascular malformations

Conventional and advanced MRI evaluation of brain vascular malformations

Lab-on-Chip Microsystems for Ex Vivo Network of Neurons Studies: A Review

A Computational Framework for Pre-Interventional Planning of Peripheral Arteriovenous Malformations

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