Colleges
Ricardo Gonzales
FSCMR, BEng
Clarendon Scholar & DPhil Student
Artificial Intelligence in Cardiovascular Imaging
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. 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
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TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline
Conference paper
Gonzales RA. et al, (2021), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12906 LNCS, 567 - 576
Recent publications
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Deep learning-based measurement of isovolumic relaxation time from cardiovascular magnetic resonance long-axis cines: validation with pressure-derived IVRT
Conference paper
Barrientos L. et al, (2024)
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Advancements in glioma segmentation: comparing the U-Net and DeconvNet models
Journal article
Mohammed AA. and Gonzales RA., (2024), Journal of Emerging Investigators