Publications — Radcliffe Department of Medicine

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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

Gonzales RA. et al, (2024)

Enhancing marine debris identification with convolutional neural networks

Wahlig V. and Gonzales RA., (2024), Journal of Emerging Investigators

Quality control-driven framework for reliable automated segmentation of cardiac magnetic resonance LGE and VNE images

Gonzales RA. et al, (2023)

TVnet: a deep-learning approach for enhanced right ventricular function analysis through tricuspid valve motion tracking

Gonzales RA. et al, (2023)

2.5D Flow MRI: 2D phase-contrast of the tricuspid valvular flow with automated valve-tracking

Lamy J. et al, (2023)

Deep learning for automated insertion point annotation of CMR late gadolinium enhancement and virtual native enhancement images

Gonzales RA. et al, (2023)

Quality control-driven deep ensemble for accountable automated segmentation of cardiac magnetic resonance LGE and VNE images.

Gonzales RA. et al, (2023), Front Cardiovasc Med, 10

Artificial Intelligence for Contrast-Free MRI: Scar Assessment in Myocardial Infarction Using Deep Learning-Based Virtual Native Enhancement.

Zhang Q. et al, (2022), Circulation, 146, 1492 - 1503

2.5D flow MRI of tricuspid valvular flow: An accurate automated valve-following phase-contrast approach

Lamy J. et al, (2022)

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), European Heart Journal - Cardiovascular Imaging, 23

Towards 2D phase-contrast of tricuspid valve flow

Lamy J. et al, (2022)

Development of Deep Learning Virtual Native Enhancement for Gadolinium-Free Myocardial Infarction and Viability Assessment

ZHANG Q. et al, (2022)

Fast and robust motion correction of cardiovascular magnetic resonance T1-mapping using data-driven convolutional neural networks for generalisability

Gonzales RA. et al, (2022)

Quality control-driven artificial intelligence for reliable automatic segmentation of LGE images in clinical practice

Gonzales RA. et al, (2022)

Automated Measurements of Mitral and Tricuspid Annular Dimensions in Cardiovascular Magnetic Resonance

Gonzales RA. et al, (2022), Proceedings - International Symposium on Biomedical Imaging, 2022-March

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

Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours

Gonzales RA. et al, (2021), BMC Medical Imaging, 21

Deep neural network ensemble for on-the-fly quality control-driven segmentation of cardiac MRI T1 mapping.

Hann E. et al, (2021), Med Image Anal, 71

Isovolumic relaxation time and e' metrics evaluated by deep-learning analysis of long-axis cine: correlations to atrial pressure and fibrosis

Peters D. et al, (2021)

Automated Tracking of the Tricuspid Valve Plane in Long-axis Cine Images with a 2-step Deep Learning Pipeline

Gonzales RA. et al, (2021)

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