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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/2509.09004 |
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| _version_ | 1866909805667418112 |
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| author | Bell, Andrew Choi, Yan Kit Petersen, Steffen E King, Andrew Nazir, Muhummad Sohaib Young, Alistair A |
| author_facet | Bell, Andrew Choi, Yan Kit Petersen, Steffen E King, Andrew Nazir, Muhummad Sohaib Young, Alistair A |
| contents | Automatic quantification of intramyocardial motion and strain from tagging MRI remains an important but challenging task. We propose a method using implicit neural representations (INRs), conditioned on learned latent codes, to predict continuous left ventricular (LV) displacement -- without requiring inference-time optimisation. Evaluated on 452 UK Biobank test cases, our method achieved the best tracking accuracy (2.14 mm RMSE) and the lowest combined error in global circumferential (2.86%) and radial (6.42%) strain compared to three deep learning baselines. In addition, our method is $\sim$380$\times$ faster than the most accurate baseline. These results highlight the suitability of INR-based models for accurate and scalable analysis of myocardial strain in large CMR datasets. The code can be found at https://github.com/andrewjackbell/Displacement-INR |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09004 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Implicit Neural Representations of Intramyocardial Motion and Strain Bell, Andrew Choi, Yan Kit Petersen, Steffen E King, Andrew Nazir, Muhummad Sohaib Young, Alistair A Computer Vision and Pattern Recognition Artificial Intelligence Automatic quantification of intramyocardial motion and strain from tagging MRI remains an important but challenging task. We propose a method using implicit neural representations (INRs), conditioned on learned latent codes, to predict continuous left ventricular (LV) displacement -- without requiring inference-time optimisation. Evaluated on 452 UK Biobank test cases, our method achieved the best tracking accuracy (2.14 mm RMSE) and the lowest combined error in global circumferential (2.86%) and radial (6.42%) strain compared to three deep learning baselines. In addition, our method is $\sim$380$\times$ faster than the most accurate baseline. These results highlight the suitability of INR-based models for accurate and scalable analysis of myocardial strain in large CMR datasets. The code can be found at https://github.com/andrewjackbell/Displacement-INR |
| title | Implicit Neural Representations of Intramyocardial Motion and Strain |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.09004 |