Colleges
Ricardo Gonzales
FSCMR, BEng
Clarendon Scholar & DPhil Student
Cardiovascular data science
My research focus is on developing robust deep learning approaches for accountable contrast-agent-free cardiac magnetic resonance (CMR) imaging in clinical applications. I design novel data-driven methods to automatically derive predictive biomarkers on clinical outcomes. My DPhil programme is funded by the Clarendon Fund Scholarship and Radcliffe Department of Medicine Scholar Programme.
Previously, I received my undergraduate degree in Electrical Engineering at UTEC (Peru) and my research training at Yale University (USA) and Lund University (Sweden), where I developed tools for the assessment of diastolic function in CMR, and its relationship to atrial remodeling. Outside of work, I serve as the Computer Science Head at REPU, a career progression program.
Key publications
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Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images.
Journal article
Gonzales RA. et al, (2023), Front Cardiovasc Med, 10
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MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks.
Journal article
Gonzales RA. et al, (2021), Front Cardiovasc Med, 8
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MVnet: automated time-resolved tracking of the mitral valve plane in CMR long-axis cine images with residual neural networks: a multi-center, multi-vendor study.
Journal article
Gonzales RA. et al, (2021), J Cardiovasc Magn Reson, 23
Recent publications
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Automated CMR index of left ventricular diastolic function (e'): a validation study against echocardiography in the large-scale Beta3-LVH trial
Conference paper
Gonzales RA. et al, (2024)
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Quality control-driven framework for reliable automated segmentation of cardiac magnetic resonance LGE and VNE images
Conference paper
Gonzales RA. et al, (2023)
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TVnet: a deep-learning approach for enhanced right ventricular function analysis through tricuspid valve motion tracking
Conference paper
Gonzales RA. et al, (2023)