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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.21019 |
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| _version_ | 1866916761823084544 |
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| author | Ugurlu, Devran Qian, Shuang Fairweather, Elliot Mauger, Charlene Ruijsink, Bram Toso, Laura Dal Deng, Yu Strocchi, Marina Razavi, Reza Young, Alistair Lamata, Pablo Niederer, Steven Bishop, Martin |
| author_facet | Ugurlu, Devran Qian, Shuang Fairweather, Elliot Mauger, Charlene Ruijsink, Bram Toso, Laura Dal Deng, Yu Strocchi, Marina Razavi, Reza Young, Alistair Lamata, Pablo Niederer, Steven Bishop, Martin |
| contents | A cardiac digital twin is a virtual replica of a patient's heart for screening, diagnosis, prognosis, risk assessment, and treatment planning of cardiovascular diseases. This requires an anatomically accurate patient-specific 3D structural representation of the heart, suitable for electro-mechanical simulations or study of disease mechanisms. However, generation of cardiac digital twins at scale is demanding and there are no public repositories of models across demographic groups. We describe an automatic open-source pipeline for creating patient-specific left and right ventricular meshes from cardiovascular magnetic resonance images, its application to a large cohort of ~55000 participants from UK Biobank, and the construction of the most comprehensive cohort of adult heart models to date, comprising 1423 representative meshes across sex (male, female), body mass index (range: 16 - 42 kg/m$^2$) and age (range: 49 - 80 years). Our code is available at https://github.com/cdttk/biv-volumetric-meshing/tree/plos2025 , and pre-trained networks, representative volumetric meshes with fibers and UVCs will be made available soon. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_21019 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants Ugurlu, Devran Qian, Shuang Fairweather, Elliot Mauger, Charlene Ruijsink, Bram Toso, Laura Dal Deng, Yu Strocchi, Marina Razavi, Reza Young, Alistair Lamata, Pablo Niederer, Steven Bishop, Martin Image and Video Processing Machine Learning A cardiac digital twin is a virtual replica of a patient's heart for screening, diagnosis, prognosis, risk assessment, and treatment planning of cardiovascular diseases. This requires an anatomically accurate patient-specific 3D structural representation of the heart, suitable for electro-mechanical simulations or study of disease mechanisms. However, generation of cardiac digital twins at scale is demanding and there are no public repositories of models across demographic groups. We describe an automatic open-source pipeline for creating patient-specific left and right ventricular meshes from cardiovascular magnetic resonance images, its application to a large cohort of ~55000 participants from UK Biobank, and the construction of the most comprehensive cohort of adult heart models to date, comprising 1423 representative meshes across sex (male, female), body mass index (range: 16 - 42 kg/m$^2$) and age (range: 49 - 80 years). Our code is available at https://github.com/cdttk/biv-volumetric-meshing/tree/plos2025 , and pre-trained networks, representative volumetric meshes with fibers and UVCs will be made available soon. |
| title | Cardiac Digital Twins at Scale from MRI: Open Tools and Representative Models from ~55000 UK Biobank Participants |
| topic | Image and Video Processing Machine Learning |
| url | https://arxiv.org/abs/2505.21019 |