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\n  \n 2024\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Role of Physics-Informed Constraints In Deep Learning Real-Time Accessment of 3D Vascular Fluid Dynamics.\n \n \n \n \n\n\n \n Chan, W. X.; Ding, W. H.; Li, B. H.; Wong, H. S.; and Yap, C. H.\n\n\n \n\n\n\n June 2024.\n 29th Congress of the European Society of Biomechanics (ESBiomech 2024)\n\n\n\n
\n\n\n\n \n \n \"RolePaper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@conference{ESBconference24,\n    title        = {Role of Physics-Informed Constraints In Deep Learning Real-Time Accessment of 3D Vascular Fluid Dynamics},\n    author       = {W. X. Chan \n\t\t\t\t\tand W. H. Ding \n\t\t\t\t\tand B. H. Li\n\t\t\t\t\tand H. S. Wong\n\t\t\t\t\tand C. H. Yap},\n    year         = 2024,\n    month        = {June},\n    note         = {29th Congress of the European Society of Biomechanics (ESBiomech 2024)},\n    organization = {29th Congress of the European Society of Biomechanics (ESBiomech 2024)},\n\turl={https://www.conftool.com/esb2024/index.php/ESB2024_6.1_3_Ding.pdf?page=downloadPaper&filename=ESB2024_6.1_3_Ding.pdf&form_id=325}\n}\n\n
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\n \n\n \n \n \n \n \n \n Strategies for multi-case physics-informed neural networks for tube flows: a study using 2D flow scenarios.\n \n \n \n \n\n\n \n Wong, H. S.; Chan, W. X.; Li, B. H.; and Yap, C. H.\n\n\n \n\n\n\n Scientific Reports, 14(1): 11577. May 2024.\n \n\n\n\n
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@Article{Wong2024,\nauthor={Wong, Hong Shen\nand Chan, Wei Xuan\nand Li, Bing Huan\nand Yap, Choon Hwai},\ntitle={Strategies for multi-case physics-informed neural networks for tube flows: a study using 2D flow scenarios},\njournal={Scientific Reports},\nyear={2024},\nmonth={May},\nday={21},\nvolume={14},\nnumber={1},\npages={11577},\nissn={2045-2322},\ndoi={10.1038/s41598-024-62117-9},\nurl={https://doi.org/10.1038/s41598-024-62117-9}\n}\n\n\n
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\n  \n 2023\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Pre-Training Varied Vascular Geometries with a Deep Learning Side Network in Physics-Informed Neural Networks Simulations of Vascular Fluid Dynamics.\n \n \n \n \n\n\n \n Wong, H. S.; Li, B.; Chan, W. X.; and Yap, C. H.\n\n\n \n\n\n\n June 2023.\n 28th Congress of the European Society of Biomechanics (ESBiomech23)\n\n\n\n
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@conference{ESBconference23,\ntitle        = {Pre-Training Varied Vascular Geometries with a Deep Learning Side Network in Physics-Informed Neural Networks Simulations of Vascular Fluid Dynamics},\nauthor       = {Wong, Hong Shen\n\t\t\t\tand Li, Binghuan\n\t\t\t\tand Chan, Wei Xuan\n\t\t\t\tand Yap, Choon Hwai},\nyear         = 2023,\nmonth        = {June},\nnote         = {28th Congress of the European Society of Biomechanics (ESBiomech23)},\norganization = {28th Congress of the European Society of Biomechanics (ESBiomech23)},\nurl={https://esbiomech.org/conference/archive/2023maastricht/355.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Fluid Mechanical Effects of Fetal Aortic Valvuloplasty for Cases of Critical Aortic Stenosis with Evolving Hypoplastic Left Heart Syndrome.\n \n \n \n \n\n\n \n Wong, H. S.; Li, B.; Tulzer, A.; Tulzer, G.; and Yap, C. H.\n\n\n \n\n\n\n Annals of Biomedical Engineering. Feb 2023.\n \n\n\n\n
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@Article{Wong-and-Li2023,\nauthor={Wong, Hong Shen\nand Li, Binghuan\nand Tulzer, Andreas\nand Tulzer, Gerald\nand Yap, Choon Hwai},\ntitle={Fluid Mechanical Effects of Fetal Aortic Valvuloplasty for Cases of Critical Aortic Stenosis with Evolving Hypoplastic Left Heart Syndrome},\njournal={Annals of Biomedical Engineering},\nyear={2023},\nmonth={Feb},\nday={13},\nissn={1573-9686},\ndoi={10.1007/s10439-023-03152-x},\nurl={https://doi.org/10.1007/s10439-023-03152-x}\n}\n\n
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\n  \n 2021\n \n \n (2)\n \n \n
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\n \n\n \n \n \n \n \n \n Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping.\n \n \n \n \n\n\n \n Wu, Y.; Hatipoglu, S.; Alonso-Álvarez, D.; Gatehouse, P.; Li, B.; Gao, Y.; Firmin, D.; Keegan, J.; and Yang, G.\n\n\n \n\n\n\n Diagnostics, 11(2). 2021.\n \n\n\n\n
\n\n\n\n \n \n \"FastPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@Article{Wu2021-2,\nAUTHOR = {Wu, Yinzhe and Hatipoglu, Suzan and Alonso-Álvarez, Diego and Gatehouse, Peter and Li, Binghuan and Gao, Yikai and Firmin, David and Keegan, Jennifer and Yang, Guang},\nTITLE = {Fast and Automated Segmentation for the Three-Directional Multi-Slice Cine Myocardial Velocity Mapping},\nJOURNAL = {Diagnostics},\nVOLUME = {11},\nYEAR = {2021},\nNUMBER = {2},\nARTICLE-NUMBER = {346},\nURL = {https://www.mdpi.com/2075-4418/11/2/346},\nPubMedID = {33669747},\nISSN = {2075-4418},\nABSTRACT = {Three-directional cine multi-slice left ventricular myocardial velocity mapping (3Dir MVM) is a cardiac magnetic resonance (CMR) technique that allows the assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 3Dir MVM also provides three orthogonal phase velocity mapping datasets, which are used to generate velocity maps. These velocity maps may also be used to facilitate and improve the myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel fast and automated framework that improves the standard U-Net-based methods on these CMR multi-channel data (magnitude and phase velocity mapping) by cross-channel fusion with an attention module and the shape information-based post-processing to achieve accurate delineation of both epicardial and endocardial contours. To evaluate the results, we employ the widely used Dice Scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows superior performance compared to standard U-Net-based networks trained on single-channel data. The obtained results are promising and provide compelling evidence for the design and application of our multi-channel image analysis of the 3Dir MVM CMR data.},\nDOI = {10.3390/diagnostics11020346}\n}\n\n
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\n Three-directional cine multi-slice left ventricular myocardial velocity mapping (3Dir MVM) is a cardiac magnetic resonance (CMR) technique that allows the assessment of cardiac motion in three orthogonal directions. Accurate and reproducible delineation of the myocardium is crucial for accurate analysis of peak systolic and diastolic myocardial velocities. In addition to the conventionally available magnitude CMR data, 3Dir MVM also provides three orthogonal phase velocity mapping datasets, which are used to generate velocity maps. These velocity maps may also be used to facilitate and improve the myocardial delineation. Based on the success of deep learning in medical image processing, we propose a novel fast and automated framework that improves the standard U-Net-based methods on these CMR multi-channel data (magnitude and phase velocity mapping) by cross-channel fusion with an attention module and the shape information-based post-processing to achieve accurate delineation of both epicardial and endocardial contours. To evaluate the results, we employ the widely used Dice Scores and the quantification of myocardial longitudinal peak velocities. Our proposed network trained with multi-channel data shows superior performance compared to standard U-Net-based networks trained on single-channel data. The obtained results are promising and provide compelling evidence for the design and application of our multi-channel image analysis of the 3Dir MVM CMR data.\n
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\n \n\n \n \n \n \n \n \n Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives.\n \n \n \n \n\n\n \n Wu, Y.; Tang, Z.; Li, B.; Firmin, D.; and Yang, G.\n\n\n \n\n\n\n Frontiers in Physiology, 12: 1111. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"RecentPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@ARTICLE{Wu2021-1,\nAUTHOR={Wu, Yinzhe and Tang, Zeyu and Li, Binghuan and Firmin, David and Yang, Guang},   \nTITLE={Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives},      \nJOURNAL={Frontiers in Physiology},      \nVOLUME={12},\nPAGES={1111},\nYEAR={2021},\nURL={https://www.frontiersin.org/article/10.3389/fphys.2021.709230},\nDOI={10.3389/fphys.2021.709230},      \nISSN={1664-042X},\n}\n\n
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