NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections by Neural Implicit Representation

• URL: http://arxiv.org/abs/2409.04596v1
• Date: Fri, 6 Sep 2024 20:08:21 GMT
• Title: NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections by Neural Implicit Representation
• Authors: Yiying Wang, Abhirup Banerjee, Vicente Grau,
• Abstract summary: 2D x-ray invasive coronary angiography remains as the most widely adopted imaging modality for CVDs diagnosis. Due to the radiation limit, in general only two angiographic projections are acquired, providing limited information of the vessel geometry. We propose a self-supervised deep learning method called NeCA, which is based on implicit neural representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer.
• Score: 2.1771042711033997
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D x-ray invasive coronary angiography (ICA) remains as the most widely adopted imaging modality for CVDs diagnosis. However, in current clinical practice, it is often difficult for the cardiologists to interpret the 3D geometry of coronary vessels based on 2D planes. Moreover, due to the radiation limit, in general only two angiographic projections are acquired, providing limited information of the vessel geometry and necessitating 3D coronary tree reconstruction based only on two ICA projections. In this paper, we propose a self-supervised deep learning method called NeCA, which is based on implicit neural representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer in order to achieve 3D coronary artery tree reconstruction from two projections. We validate our method using six different metrics on coronary computed tomography angiography data in terms of right coronary artery and left anterior descending respectively. The evaluation results demonstrate that our NeCA method, without 3D ground truth for supervision and large datasets for training, achieves promising performance in both vessel topology preservation and branch-connectivity maintaining compared to the supervised deep learning model.

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