Imperial College London


Faculty of MedicineNational Heart & Lung Institute

Honorary Research Fellow







Sydney StreetRoyal Brompton Campus





I am a Senior Image Processing and Data Scientist based at the Cardiovascular Magnetic Resonance unit, Royal Brompton Hospital. I have a background in Physics and Mathematics, a PhD in MRI Physics (Imperial College :: London), and an extensive experience in MRI research.

Since 2012, I have been working closely with a team of physicists and clinicians in diffusion tensor cardiovascular magnetic resonance (DT-CMR). It is a novel technique for the investigation of the 3-dimensional microstructure of the heart in-vivo. We aim to improve the understanding of how the healthy heart functions as well as how microstructural dysfunction contributes to disease.

I am focused on creating specialised tools to perform data analysis, image processing, and data visualisation, with a recent interest in finding solutions with AI.

Left ventricle tractography


We developed and validated a fully automated postprocessing framework for in vivo diffusion tensor cardiac magnetic resonance (DT-CMR) data powered by deep learning semantic segmentation (TensorFlow):

  • U-Net architecture
  • Proposed fully automated post-processing pipeline

We published in vivo studies showing that secondary directions of diffusion align with the laminar organisation of cardiomyocytes in the heart, and that cardiomyopathy hearts present impaired mobility:

sheetlet rotation

 Previous academic education:

  • 2005 - 2009: PhD in Myocardial Perfusion Imaging with MR, Imperial College, London, UK.
  • 2004 - 2005: MSc in Medical Engineering and Physics, King’s College, University of London, UK.
  • 1998 - 2004: First Degree in Physics / Applied Mathematics (Astrophysics), Faculty of Sciences, University of Porto, Portugal.



Huo Z, Wen K, Luo Y, et al., 2024, Referenceless Nyquist ghost correction outperforms standard navigator-based method and improves efficiency of in vivo diffusion tensor cardiovascular magnetic resonance, Magnetic Resonance in Medicine, Vol:91, ISSN:0740-3194, Pages:2403-2416

Huang J, Ferreira P, Wang L, et al., 2024, Deep learning-based diffusion tensor cardiac magnetic resonance reconstruction: a comparison study, Scientific Reports, Vol:14, ISSN:2045-2322

Roehl M, Conway M, Ghonim S, et al., 2024, STEAM-SASHA: A novel approach for blood and fat suppressed native T1 measurement in the right ventricular myocardium, Magnetic Resonance Materials in Physics, Biology and Medicine, ISSN:0968-5243

Zheng Y, Chan WX, Nielles-Vallespin S, et al., 2023, Effects of myocardial sheetlet sliding on left ventricular function, Biomechanics and Modeling in Mechanobiology, Vol:22, ISSN:1617-7940, Pages:1313-1332

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