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.
MOCOnet: Robust Motion Correction of Cardiovascular Magnetic Resonance T1 Mapping Using Convolutional Neural Networks.
Gonzales RA. et al, (2021), Front Cardiovasc Med, 8
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.
Gonzales RA. et al, (2021), J Cardiovasc Magn Reson, 23
TVnet: Automated Time-Resolved Tracking of the Tricuspid Valve Plane in MRI Long-Axis Cine Images with a Dual-Stage Deep Learning Pipeline
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
Artificial Intelligence for Contrast-free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-based Virtual Native Enhancement (VNE)
Zhang Q. et al, (2022), Circulation
TVnet: automated global analysis of tricuspid valve plane motion in CMR long-axis cines with residual neural networks for assessment of right ventricular function
Gonzales RA. et al, (2022), 36 - 37
Development of Deep Learning Virtual Native Enhancement for Gadolinium-Free Myocardial Infarction and Viability Assessment
ZHANG Q. et al, (2022)