Framework Development for Patient-specific Compliant Aortic Dissection Phantom Model Fabrication: Magnetic Resonance Imaging Validation and Deep-learning Segmentation.

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Abstract

Type B aortic dissection is a life-threatening medical emergency that can result in rupture of the aorta. Due to the complexity of patient-specific characteristics, only limited information on flow patterns in dissected aortas has been reported in the literature. Leveraging the medical imaging data for patient-specific in-vitro modeling can complement the hemodynamic understanding of aortic dissections. We propose a new approach toward fully automated patient-specific type B aortic dissection model fabrication. Our framework uses a novel deep-learning-based segmentation for negative mold manufacturing. Deep-learning architectures were trained on a dataset of 15 unique computed tomography scans of dissection subjects and were blind-tested on 4 sets of scans, which were targeted for fabrication. Following segmentation, the 3D models were created and printed using polyvinyl alcohol. These models were then coated with latex to create compliant patient-specific phantom models. The Magnetic resonance imaging (MRI) structural images demonstrate the ability of the introduced manufacturing technique for creating intimal septum wall and tears based on patient-specific anatomy. The in-vitro experiments show the fabricated phantoms generate physiologically-accurate pressure results. The deep-learning models also show high similarity metrics between manual segmentation and auto-segmentation where Dice metric is as high as 0.86. The proposed deep-learning-based negative mold manufacturing method facilitates an inexpensive, reproducible, and physiologically-accurate patient-specific phantom model fabrication suitable for aortic dissection flow modeling.Copyright © 2023 by ASME.