Abstract
Anatomical heart mesh models created from cine cardiac images are useful for the evaluation and monitoring of cardiovascular diseases, but require challenging and time-consuming reconstruction processes. Errors due to reduced spatial resolution and motion artefacts limit the accuracy of 3D models. We proposed ModusGraph to produce a higher quality 3D and 4D (3D+time) heart models automatically, employing i) a voxel processing module with Modality Handles and a super-resolution decoder to define low-resolution and high-resolution segmentations and correct motion artefacts with multi-modal unpaired data, ii) a Residual Spatial-temporal Graph Convolution Network to generate mesh models by controlled and progressive spatial-temporal deformation to better capture the cardiac motion, and iii) a Signed Distance Sampling process to bridge those two parts for end-to-end training. ModusGraph was trained and evaluated on CT angiograms and cardiovascular MRI cines, showing superior performance compared to other mesh reconstruction methods. It creates well-defined meshes from sparse MRI cines, enabling vertex tracking across cardiac cycle frames. This process aids in analyzing myocardium function and conducting biomechanical analyses from imaging data https://github.com/MalikTeng/ModusGraph.
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Acknowledgements
YD was funded by the Kings-China Scholarship Council PhD Scholarship Program. HX was funded by Innovate UK (104691) London Medical Imaging & Artificial Intelligence Centre for Value Based Healthcare. SCOT-HEART was funded by The Chief Scientist Office of the Scottish Government Health and Social Care Directorates (CZH/4/588), with supplementary awards from Edinburgh and Lothian’s Health Foundation Trust and the Heart Diseases Research Fund. AAY and KP acknowledge funding from the National Institutes of Health R01HL121754 and Welcome ESPCR Centre for Medical Engineering at King’s College London WT203148/Z/16/Z.
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Deng, Y. et al. (2023). ModusGraph: Automated 3D and 4D Mesh Model Reconstruction from Cine CMR with Improved Accuracy and Efficiency. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_17
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