| Title: | Multi-attention-aware motion estimation for cardiac MR imaging based on a feature pyramid network |
| Journal: | Biomedical Signal Processing and Control |
| Published: | 1 Jun 2026 |
| DOI: | https://doi.org/10.1016/j.bspc.2026.109714 |
| URL: | https://research.manchester.ac.uk/en/publications/8e9093e4-ab25-4343-b5a1-4bae8718d6d0 |
Publication 19820
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Abstract
Objective: Unsupervised cardiac motion estimation often confronts complex scenarios, which include a lack of explicit reference for the deformation fields, and intramodal anatomical gaps. These factors introduce substantial obstacles in the effective representation of both smooth and accurate cardiac motion, thereby hindering the prediction of intricate structural details after registration. However, existing approaches have not sufficiently explored the explicit spatial correlations encompassed in multi-range displacements. Methods: To overcome the challenges, we propose a novel multi-attention-guided network, MAPC-Net, a pyramidal network with spatial correlation normalisation compensated in the mechanism for high-quality cardiac motion estimation. Results: The extensive experimental results from quantitative and qualitative aspects indicate that MAPC-Net achieves exceptional performance in the generalisation of the effective deformation field on the private dataset UKBiobank and publicly available ACDC in terms of cardiac cine-MRI datasets. Our model achieves an average Dice score over 75 % (77.2 % ), a 95 % - Hausdorff Distance less than 4.50 mm and a Negative Jacobian Determinant value of 0.20 % without segmentation label guided over UKBioBank dataset. We highlighted the significant potential of the proposed framework in clinical relevance by demonstrating the downstream analysis in terms of cardiac peak strain signal. We improve the estimated peak radial strain value from 41.72 % to 43.28 % . Conclusion: A novel framework was proposed for the refinement of motion estimation by introducing attention-guided correlation between warped and fixed frames. Significance: The architecture of our proposed model offers a new solution for predicting high-quality cardiac deformation fields, leveraging an attention-aware cost volume calculation embedded in a pyramidal network for motion estimation.</p>
5 Authors
- Kun Wu
- Xiang Chen
- Nina Cheng
- Nishant Ravikumar
- Alejandro F. Frangi
1 Application
| Application ID | Title |
|---|---|
| 11350 | CARDIOMARKER- Computational imaging phenomics in population cardiac MRI with automatic image quality assessment: benchmarking, scalability and inference with state-of-the-art algorithms. |
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