Gabriel Ramos Llorden

Gabriel Ramos Llorden's picture


Ramos Llordén

Campus Drie Eiken, Gebouw N, N.115
Universiteitsplein 1,
2610 Wilrijk, Belgium



Gabriel Ramos Llordén (1988) was born in Benavente, Spain. He received the "Ingeniero de Telecomunicación" degree (MSc) as well as an MSc in Biomedical Engineering and Signal Processing from the University of Valladolid, Valladolid, Spain, in 2012 and 2013 respectively. He carried out an internship at Laboratory of Image Processing (LPI), Valladolid, under the supervision of dr. Gonzalo Vegas Sánchez Ferrero and prof. Santiago Aja Fernández. There, he conducted research on ultrasound medical denoising, with the outcome of a publication in a high impact factor international journal, and also a national award.

In September 2013, he moved to Antwerp, Belgium, to pursue a PhD degree at IMEC-Vision Lab, with prof. Arnold Jan den Dekker and prof. Jan Sijbers as supervisors. He has been focused on improving Magnetic Resonance (MR) relaxometry through signal processing and mathematical methods. In particular, he has developed efficient, statistically optimal schemes for joint motion correction and parameter estimation as well as accurate/precise, fast relaxometry estimators.
Furthermore, he developed new techniques for MR image reconstruction, namely, new mathematical priors to accelerate the relatively slow acquisition protocol of MR images. Both research lines have led to international publications in high impact factor medical journals, and awards in international conferences.

In January 2017, he was invited as a research scholar at Laboratory of Mathematics in Imaging (LMI), Harvard Medical School, Boston, USA, for three months, where he worked on diffusion MRI reconstruction. He was supervised by prof. Carl-Fredrik Westin and prof. Yogesh Rathi.

Gabriel Ramos Llordén has been involved in the supervision of several MSc students as well as he has given courses on signal and image processing to MSc physics students. He has also been a reviewer for top journals in the medical imaging field.


Unified Maximum Likelihood approaches for T1 map restoration in the presence of patient motion