NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation
Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, it is often difficult for the cardiologists to interpret the...
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MDPI AG
2024-12-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/11/12/1227 |
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| author | Yiying Wang Abhirup Banerjee Vicente Grau |
| author_facet | Yiying Wang Abhirup Banerjee Vicente Grau |
| author_sort | Yiying Wang |
| collection | DOAJ |
| description | Cardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, 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, often 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 neural implicit representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer, in order to achieve 3D coronary artery tree reconstruction from two 2D projections. We validate our method using six different metrics on a dataset generated from coronary computed tomography angiography of right coronary artery and left anterior descending artery. The evaluation results demonstrate that our NeCA method, without requiring 3D ground truth for supervision or large datasets for training, achieves promising performance in both vessel topology and branch-connectivity preservation compared to the supervised deep learning model. |
| format | Article |
| id | doaj-art-b32f9a0011fd44469daf831c1f208b52 |
| institution | Kabale University |
| issn | 2306-5354 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj-art-b32f9a0011fd44469daf831c1f208b522024-12-27T14:11:33ZengMDPI AGBioengineering2306-53542024-12-011112122710.3390/bioengineering11121227NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit RepresentationYiying Wang0Abhirup Banerjee1Vicente Grau2Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UKInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UKInstitute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UKCardiovascular diseases (CVDs) are the most common health threats worldwide. 2D X-ray invasive coronary angiography (ICA) remains the most widely adopted imaging modality for CVD assessment during real-time cardiac interventions. However, 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, often 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 neural implicit representation using the multiresolution hash encoder and differentiable cone-beam forward projector layer, in order to achieve 3D coronary artery tree reconstruction from two 2D projections. We validate our method using six different metrics on a dataset generated from coronary computed tomography angiography of right coronary artery and left anterior descending artery. The evaluation results demonstrate that our NeCA method, without requiring 3D ground truth for supervision or large datasets for training, achieves promising performance in both vessel topology and branch-connectivity preservation compared to the supervised deep learning model.https://www.mdpi.com/2306-5354/11/12/12273D coronary artery tree reconstructioninvasive coronary angiographylimited-projection reconstructionneural implicit representationself-supervised learningdeep learning |
| spellingShingle | Yiying Wang Abhirup Banerjee Vicente Grau NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation Bioengineering 3D coronary artery tree reconstruction invasive coronary angiography limited-projection reconstruction neural implicit representation self-supervised learning deep learning |
| title | NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation |
| title_full | NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation |
| title_fullStr | NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation |
| title_full_unstemmed | NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation |
| title_short | NeCA: 3D Coronary Artery Tree Reconstruction from Two 2D Projections via Neural Implicit Representation |
| title_sort | neca 3d coronary artery tree reconstruction from two 2d projections via neural implicit representation |
| topic | 3D coronary artery tree reconstruction invasive coronary angiography limited-projection reconstruction neural implicit representation self-supervised learning deep learning |
| url | https://www.mdpi.com/2306-5354/11/12/1227 |
| work_keys_str_mv | AT yiyingwang neca3dcoronaryarterytreereconstructionfromtwo2dprojectionsvianeuralimplicitrepresentation AT abhirupbanerjee neca3dcoronaryarterytreereconstructionfromtwo2dprojectionsvianeuralimplicitrepresentation AT vicentegrau neca3dcoronaryarterytreereconstructionfromtwo2dprojectionsvianeuralimplicitrepresentation |