Cardiovascular Biomechanics and A.I. Laboratory
Imperial College London, Department of bioengineering

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Links to Our Codes and Datasets

B-Spline Fourier Motion Tracking Traditional pair-wise image registration regularized with cyclic motion consistency Reference: Wiputra et al., Scientific reports, 10(1), 1-14. Github Link Traditional Finite Element Model of the Heart FEniCS Code for traditional Finite Element modelling of the cardiac ventricle biomechanics Reference: Green et al., Biomech Model Mechanobiol. 2024 Oct;23(5):1433-48. Github Link PINN Fluid Mechanics Prediction in Vessels Physics-Informed Neural Network for predicting detailed fluid mechanics in a range of idealized vascular geometries. Reference: Chan et al., Comput Biol Med. 2025 May 1;190:110074. Github Link PINN Fluid Mechanics Prediction in the Left Ventricle Physics-Informed Neural Network for predicting detailed fluid mechanics in the cardiac left ventricle using 3D Doppler acquisitions as data guidance. Reference: Wong et al., Comput Methods Programs Biomed. 2025 Feb 13:108671. Github Link Deep Learning Generation of Cranial Aneurysm Shapes (AneuG) 2-stage Generative AI model for creating synthetic bifurcation cranial aneurysm shapes for augmenting flow prediction models. Generation can be conditioned to produce shapes with specific clinically relevant morphological parameters. Reference: Ding et al. "Two-Stage Generative Model for Intracranial Aneurysm Meshes with Morphological Marker Conditioning." MICCAI 2025 [accepted June 2025] Github Link Large CFD Dataset of Bifurcation Cranial Aneurysm Large dataset of steady flow and pulsatile simulations, based on synthetic aneurysm shapes generated via AneuG Reference: TBD Hugging Face Link PINN Finite Element Model of the Left Ventricle (IMC-PINN-FE) Fast Physics Informed Neural Network Algorithm for Finite Element Modelling of ventricular biomechanics, which constraints to adhere to imaged cardiac motions. Reference: TBD Github Link Deep Learning Prediction of Unloaded State of the Left Ventricle from Image Reconstruction (Load2UnNet) Graph Attention Network to rapidly calculate the unloaded state 3D mesh of the left ventricle, from the 3D mesh at end-diastole and inputs of the end-diastolic pressure, cardiac stiffness scale, and helix angle configurations. Reference: TBD Github Link Graph Hamornic Deformation Encoder for Cardiac Shapes Guided by Segmented Image Slices via Differential Slicing and Voxelization Reconstruction of the 3D mesh of the heart’s shape using the graph harmonic mesh morphic approach, guided by slices of segmented image via the differentiable slicing and voxelization technique, the first truly differentiable direct supervision by image. Reference: Luo et al., arXiv preprint arXiv:2409.02070. 2024 Sep 3. Github Link Feedback Attention Image Registration for Echo/MRI Images 2D or 3D image registration with a special feedback attention mechanism that enhances performance. Reference: Hasan et al. Feedback Attention to Enhance Unsupervised Deep Learning Image Registration in 3D Echocardiography. IEEE Transactions on Medical Imaging. 2025 Jan 16. Github Link Temporal Attention Module for Enhancing Segmentation 2D or 3D image segmentation from medical images with temporal attention enhancement that is achieved with a small, light-weight, yet highly effective temporal attention module that can be flexibly added to a range of network architecture. Reference: Hasan et al., arXiv preprint arXiv:2501.14929. 2025 Jan 24. Link TB added