Abstracts

Rationale: 3D printing, or additive manufacturing, is a common term used to describe the generation of a physical model derived from digital information.1 In recent years, there has been an emerging interest in healthcare related applications, specifically related to the creation of anatomic models based on patient specific MRI and CT scans.2 In particular, this technology may be very helpful for cerebral modeling in Neurosurgical interventions, especially when considering epilepsy surgery given the limitations of 2D projection alone in representing resectable regions, understanding spatial relationships between electrodes and defining surgical approaches.3 Currently, there exist a multitude of methods and machines to generate these models but there is limited information comparing them directly.4

Methods: We used the lens of epilepsy surgery to compare several of the most commonly used 3D printing methods to better understand their clinical application. The imaging data was derived from 3 Tesla MRI and CT scans from patients with epilepsy at Duke University Hospital. These DICOM (digital imaging in communications in medicine) files were converted into 3D objects using software including 3D Slicer® and MeshMixer® to define anatomic regions by varying the intensity thresholds of the source images. The 3D objects contained both cerebral structures and implanted depth electrodes.

We tested three common printing methods in terms of reproducibility, resolution, practical limitations, and cost: Polyjet (using Stratasys J750), which uses an inkjet-photopolymer technology; Low-force Stereolithography (SLA), which focuses visible light onto a liquid resin basin to cure the object under gravity; and Filament Deposition Modelling (FDM), which layers heated thermoplastic materials.

Results: We found that although the Polyjet provided the highest quality and most design flexibility with multi-material printing, it was also the most expensive. SLA was far less expensive but was limited to one material per model and the larger models exceeded the print volume. FDM was the least expensive and shared dual-material capacities but led to the greatest number of printing errors and failures.

Conclusions: An improved understanding of these benefits and constraints may guide further clinical applications of 3D modeling both within and beyond the scope evaluated here for epilepsy surgery. Physicians and researchers may therefore be better suited to choose the appropriate printing method for their needs which ultimately may improve communication in epilepsy conferences, peer-to-peer surgical planning, and patient education.

1. Shahrubudin N et al. An Overview on 3D Printing Technology: Technological, Materials, and Applications. Procedia Manufacturing. 2019;35:1286–96.
2. Awad A et al. 3D printed medicines: A new branch of digital healthcare. International Journal of Pharmaceutics. 2018;548(1):586–96.
3. Thawani JP et al. 3D printing in neurosurgery: A systematic review. Surgical Neurology International. 2016;7(34):801.
4. Dodziuk H. Applications of 3D printing in healthcare. Polish Journal of Cardio-Thoracic Surgery. 2016;3:283–93.

Funding: Please list any funding that was received in support of this abstract.: We received funding from the Duke University School of Medicine and the Duke Innovation Co-Lab.