A patient-specific multi-modality abdominal aortic aneurysm imaging phantom

Author:

Little Callum D.ORCID,Mackle Eleanor C.ORCID,Maneas Efthymios,Chong DebraORCID,Nikitichev DaniilORCID,Constantinou Jason,Tsui JaniceORCID,Hamilton GeorgeORCID,Rakhit Roby D.ORCID,Mastracci Tara M.ORCID,Desjardins Adrien E.ORCID

Abstract

Abstract Purpose Multimodality imaging of the vascular system is a rapidly growing area of innovation and research, which is increasing with awareness of the dangers of ionizing radiation. Phantom models that are applicable across multiple imaging modalities facilitate testing and comparisons in pre-clinical studies of new devices. Additionally, phantom models are of benefit to surgical trainees for gaining experience with new techniques. We propose a temperature-stable, high-fidelity method for creating complex abdominal aortic aneurysm phantoms that are compatible with both radiation-based, and ultrasound-based imaging modalities, using low cost materials. Methods Volumetric CT data of an abdominal aortic aneurysm were acquired. Regions of interest were segmented to form a model compatible with 3D printing. The novel phantom fabrication method comprised a hybrid approach of using 3D printing of water-soluble materials to create wall-less, patient-derived vascular structures embedded within tailored tissue-mimicking materials to create realistic surrounding tissues. A non-soluble 3-D printed spine was included to provide a radiological landmark. Results The phantom was found to provide realistic appearances with intravascular ultrasound, computed tomography and transcutaneous ultrasound. Furthermore, the utility of this phantom as a training model was demonstrated during a simulated endovascular aneurysm repair procedure with image fusion. Conclusion With the hybrid fabrication method demonstrated here, complex multimodality imaging patient-derived vascular phantoms can be successfully fabricated. These have potential roles in the benchtop development of emerging imaging technologies, refinement of novel minimally invasive surgical techniques and as clinical training tools.

Funder

Wellcome Trust

Publisher

Springer Science and Business Media LLC

Subject

Health Informatics,Radiology, Nuclear Medicine and imaging,General Medicine,Surgery,Computer Graphics and Computer-Aided Design,Computer Science Applications,Computer Vision and Pattern Recognition,Biomedical Engineering

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