Development of patient specific 3D printed liver model for preoperative planning

Madurska, M. J., Poyade, M., Eason, D., Rea, P. and Watson, A. J.M. (2017) Development of patient specific 3D printed liver model for preoperative planning. Surgical Innovation, 24(2), pp. 145-150. (doi:10.1177/1553350616689414) (PMID:28134003)

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Introduction. Liver surgery is widely used as a treatment modality for various liver pathologies. Despite significant improvement in clinical care, operative strategies, and technology over the past few decades, liver surgery is still risky, and optimal preoperative planning and anatomical assessment are necessary to minimize risks of serious complications. 3D printing technology is rapidly expanding, and whilst appliactions in medicine are growing, but its applications in liver surgery are still limited. This article describes the development of models of hepatic structures specific to a patient diagnosed with an operable hepatic malignancy. Methods. Anatomy data were segmented and extracted from computed tomography and magnetic resonance imaging of the liver of a single patient with a resectable liver tumor. The digital data of the extracted anatomical surfaces was then edited and smoothed, resulting in a set of digital 3D models of the hepatic vein, portal vein with tumor, biliary tree with gallbladder, and hepatic artery. These were then 3D printed. Results. The final models of the liver structures and tumor provided good anatomical detail and representation of the spatial relationships between the liver tumor and adjacent hepatic structures and could be easily manipulated and explored from different angles. Conclusions. A graspable, patient-specific, 3D printed model of liver structures could provide an improved understanding of the complex liver anatomy and better navigation in difficult areas and allow surgeons to anticipate anatomical issues that might arise during the operation. Further research into adequate imaging, liver-specific volumetric software, and segmentation algorithms are worth considering to optimize this application.

Item Type:Articles
Additional Information:This work was supported by NHS Highland (ID # 1028).
Glasgow Author(s) Enlighten ID:Rea, Professor Paul
Authors: Madurska, M. J., Poyade, M., Eason, D., Rea, P., and Watson, A. J.M.
College/School:College of Medical Veterinary and Life Sciences > School of Life Sciences
Journal Name:Surgical Innovation
Publisher:SAGE Publications
ISSN (Online):1553-3514
Published Online:28 January 2017

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