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arXiv · 2509.09004

Implicit Neural Representations of Intramyocardial Motion and Strain

Andrew Bell·Yan Kit Choi·Steffen E Petersen·Andrew King·Muhummad Sohaib Nazir·Alistair A Young
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Abstract

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

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Andrew Bell, Yan Kit Choi, Steffen E Petersen, Andrew King, Muhummad Sohaib Nazir, Alistair A Young. 2025-09-10. Implicit Neural Representations of Intramyocardial Motion and Strain. https://arxiv.org/abs/2509.09004

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