Research Article: Advanced 3D printed model of middle cerebral artery aneurysms for neurosurgery simulation

Date Published: August 1, 2019

Publisher: Springer International Publishing

Author(s): Ruth G. Nagassa, Paul G. McMenamin, Justin W. Adams, Michelle R. Quayle, Jeffrey V. Rosenfeld.


Neurosurgical residents are finding it more difficult to obtain experience as the primary operator in aneurysm surgery. The present study aimed to replicate patient-derived cranial anatomy, pathology and human tissue properties relevant to cerebral aneurysm intervention through 3D printing and 3D print-driven casting techniques. The final simulator was designed to provide accurate simulation of a human head with a middle cerebral artery (MCA) aneurysm.

This study utilized living human and cadaver-derived medical imaging data including CT angiography and MRI scans. Computer-aided design (CAD) models and pre-existing computational 3D models were also incorporated in the development of the simulator. The design was based on including anatomical components vital to the surgery of MCA aneurysms while focusing on reproducibility, adaptability and functionality of the simulator. Various methods of 3D printing were utilized for the direct development of anatomical replicas and moulds for casting components that optimized the bio-mimicry and mechanical properties of human tissues. Synthetic materials including various types of silicone and ballistics gelatin were cast in these moulds. A novel technique utilizing water-soluble wax and silicone was used to establish hollow patient-derived cerebrovascular models.

A patient-derived 3D aneurysm model was constructed for a MCA aneurysm. Multiple cerebral aneurysm models, patient-derived and CAD, were replicated as hollow high-fidelity models. The final assembled simulator integrated six anatomical components relevant to the treatment of cerebral aneurysms of the Circle of Willis in the left cerebral hemisphere. These included models of the cerebral vasculature, cranial nerves, brain, meninges, skull and skin. The cerebral circulation was modeled through the patient-derived vasculature within the brain model. Linear and volumetric measurements of specific physical modular components were repeated, averaged and compared to the original 3D meshes generated from the medical imaging data. Calculation of the concordance correlation coefficient (ρc: 90.2%–99.0%) and percentage difference (≤0.4%) confirmed the accuracy of the models.

A multi-disciplinary approach involving 3D printing and casting techniques was used to successfully construct a multi-component cerebral aneurysm surgery simulator. Further study is planned to demonstrate the educational value of the proposed simulator for neurosurgery residents.

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Neurosurgery trainees are finding it increasingly difficult to obtain operative experience as the primary operator in aneurysm surgery [1]. Good quality cadaver dissection opportunities are also not readily available for neurosurgery residents. Simulation is emerging as a useful training aid for neurosurgery [2]. The treatment of cerebral aneurysms requires specialized skill development and proficient use of micro-instruments. Furthermore, any advance in neurosurgical training methods is of potential value to both neurosurgeons and patients [3]. Operative caseload of neurosurgical trainees forms an extensive role in training, alongside surgical simulation in the development of primary neurosurgical techniques [4]. Current simulation methods include the use of human cadavers, large animal models, medical manikins and virtual simulation with haptic feedback [5, 6]. However, these models rarely simulate the entire procedure or provide realistic haptic feedback [7, 8]. A 3D model mimicking human tissue would allow trainees to go through the basic operative steps of specific procedures and navigate through anatomical landmarks enabling effective training with the supervision of superiors in a safe environment [9]. Simulation-based training has been shown to improve non-technical skills including cognitive and interpersonal skills that can be overlooked in surgical training [10, 11]. Such improvements in the context of neurosurgical trainees would decrease medical error and potentially improve patient outcomes [12]. 3D printed models have been demonstrated to accurately replicate patient-specific vascular structures [11]. Such models have been shown to be valuable in endovascular coiling simulation where anatomical complexities are detected through medical imaging and therefore require the determination of a preoperative tactile approach [13]. They can assist in improving the understanding of spatial anatomy configuration, particularly in cases of challenging vasculature [13].

To improve the reproducibility and cost efficacy, the left side of the brain was designed with the surgical approach and realistic pathology of a middle cerebral artery (MCA) aneurysm at the M1/M2 junction. Due to the inability to obtain all necessary components of a comprehensive simulator from a singular patient dataset, each component was developed from various patient medical imaging and computational 3D models that obtained optimal clarity of the desired anatomy.

Advances in additive manufacturing technology enable the production of full color, dimensionally accurate, low-cost 3D prints. This technology is being implemented in a range of medical fields [22]. Whilst a variety of substrates are available in 3D printing [23], the haptic properties are currently limited. In addition, limitations in 3D printing technology complicate the manufacture of hollow flexible models and the replication of soft tissues [24]. The present study overcame these limitations through 3D print-driven moulds which allowed us to cast materials that mimicked real tissues. Material selection was based on producing anatomically accurate items with optimal haptic properties.

The applications of 3D printing in medicine are enhanced when integrated with real patient medical imaging data. In this study 3D printing was complemented with the casting of synthetic materials to achieve bio-mimicry properties. The design adaptability we have shown is a major advantage as it allows the development of modular customized simulators to meet a range of teaching and training scenarios.




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