Master’s Thesis Project Proposals

Improving the spatial resolution of Cardiac Magnetic Resonance Quantitative T1-mapping using a super-resolution reconstruction

Advisership – IST: Rita Nunes and Andreia Gaspar
Hospital da Luz: Nuno André Silva

To treat atrial fibrillation, many patients require radiofrequency ablation (RFA), for which myocardial fibrosis is a predictor of success. T1 mapping is currently used in the clinic for tissue characterization (and fibrosis identification) in the ventricles, but its application to the left atrium remains challenging due to its smaller dimensions. To ensure that a sufficiently high spatial resolution is attained within acquisition times compatible with a clinical protocol, strategies for acquisition acceleration are required.
The aim of this project is to implement a reconstruction pipeline for performing super-resolution in the context of T1-mapping, exploring the robustness of different k-space sampling patterns to respiratory and cardiac motion.

Comparison of T1-maps and Late Gadolinium Enhancement in the detection of myocardial fibrosis

Advisership – IST: Rita Nunes and Andreia Gaspar
Hospital da Luz: Hugo Marques, António Miguel Ferreira

In many heart muscle pathologies, the characterization of myocardium fibrosis is a critical step for understanding disease progression and guide patient treatment. The gold standard technique for non-invasive imaging of this tissue is magnetic resonance imaging (MRI) with late gadolinium enhancement (LGE). In alternative, T1 mapping techniques have been shown to be advantageous compared to LGE as they allow an accurate, reproducible, and quantitative assessment of fibrosis without the need for contrast agent injection. However, the exact relationship between the tissue’ changes identified in LGE and T1-maps still remains under research.
The aim of this project is to investigate the value of T1 maps in the identification of fibrosis by comparison with the standard LGE images when using machine learning analysis. These approaches will be applied to previously collected datasets, which have already been qualitatively evaluated by our clinical collaborators at Hospital da Luz.

Optimizing T2 quantitative estimation of knee cartilage with Magnetic Resonance Imaging (MRI) applying dictionary-based methods

Advisership – IST: Rita Nunes and Andreia Freitas
Hospital de Santo António: José Manuel Coelho
Escola Superior de Saúde do Politécnico do Porto: Luísa Nogueira

Cartilage osteoarthritis causes severe disability, affecting 70 to 90% of patients over 65 years old. Knee osteoarthritis is commonly assessed with X-ray imaging. However, research studies suggest that cartilage degeneration is visible in those images, only when its function and mobility have already been compromised. Recent studies suggest T2 and T1p quantitative MRI may be adequate predictors of early osteoarthritis and could potentially provide valuable information on disease progression, useful for patient follow-up and therapeutic decision.
The aim of this project is to implement an estimation algorithm for T2 mapping where exact knowledge of the used multi spin-echo sequence – MSE is used to predict the observed signal and finding the best match out of a range of plausible T2 values, and apply it to the knee cartilage (Figure shows results from a pilot test). Resulting T2 maps of the knee will then be compared to those obtained with the gold-standard estimation methods (e.g. mono-exponential fit, disregarding the first echo).

Figure: MSE T2-weighted images of the knee joint with superimposed T2 map estimation of the selected knee cartilage. Comparison of the gold-standard method using a mono-exponential decay model (left) and the proposed dictionary-based method (right). Slight differences can be noticed in the T2 cartilage estimated values (colour scaling represents T2 times in ms per pixel). Data was acquired at Hospital de Santo António, in collaboration with José Manuel Coelho, António Oliveira and Luísa Nogueira.

Comparing free-water fraction estimation algorithms in the context of Traumatic Brain Injury

Advisership – IST: Rita Nunes
University of Cambridge: Marta Correia

Traumatic Brain Injury (TBI) is defined as an alteration of brain function or other evidence of brain pathology, caused by an external force. Worldwide, TBI is a leading cause of injury-related death and disability, with a devastating impact on patients and their families. Diffusion Weighted MRI (DWI) can be used to explore the microstructural architecture of brain tissues in vivo and it has been widely applied to the study of TBI pathology. However, the presence of extracellular free water from edema can affect the diffusion measures, potentially leading to wrong interpretations about the underlying microstructural changes. The free-water elimination (FWE) signal model is an alternative to more traditional approaches to modelling of DWI data, which considers also an isotropic extracellular compartment representing free water. A recent method for estimating free water fraction using multiple diffusion-weighting shells has been shown to reduce the bias in the parameter estimates. However, as clinical protocols often use a single diffusion-weighing (single-shell data) to reduce exam times, it becomes relevant to investigate if introducing prior knowledge in the estimation could enable reliable free water elimination when applied to single-shell data.
The goal of this project is to compare the performance of these two free water elimination algorithms when applied to the same TBI data. A large dataset of multi-shell DWI data has been acquired as part of a longitudinal TBI study led by the NTNU in Trondheim. This dataset includes 155 patients and 78 healthy controls scanned at multiple time points (622 scans in total). The data has already been pre-processed, and the multi-shell algorithm applied to eliminate free water contamination. In this project the same data will be processed applying an open-source implementation of the single-shell algorithm. The results will be compared to the output of the multi-shell algorithm (currently the state-of-the art in the field) for the same data, in order to assess the reliability of the diffusion metrics estimated by the single-shell approach.

Two open research assistant positions in neuroimaging: apply until December 18th

We are looking for two enthusiastic researchers to work on our project on Multimodal neuroimaging biomarkers throughout the migraine cycle: towards neurofeedback training for personalized anti-migraine treatment, at the Institute for Systems and Robotics – Lisboa, at Instituto Superior Técnico, Universidade de Lisboa, in collaboration with Hospital da Luz Learning Health and iMM – Instituto de Medicina Molecular João Lobo Antunes, and funded by Programa Operacional POR Lisboa 2020 with reference LISBOA-01-0145-FEDER-029675).


The project aims to: 1) uncover multimodal neuroimaging biomarkers of the the different phases of the migraine cycle using a combination of functional and stuctural MRI as well as EEG; and 2) develop a neuroimaging-based neurofeedback system for preventive therapy of migraine. The research assistants will contribute to the development of methods for the integration of simultaneous EEG and fMRI data.


The candidates should have: 1) an MSc degree completed by the date of the call in Biomedical Engineering, Medical Physics, Electrical Engineering, Computer Science, Physics, Neuroscience, or related areas; 2) experience with EEG and/or MRI, and/or machine learning, and preferably also with neurofeedback and/or BCI; 3) availability for an interview.


The researcher will receive a contract of 6 months, extendable up to 12 months, with a monthly stipend of 989,70€.

Hosting conditions:

The researchers will be hosted at LaSEEB – the Biomedical Engineering Lab of the Institute for Systems and Robotics – Lisboa ( LaSEEB offers a rich, interdisciplinary research environment wihtin the top engineering school in Lisbon, with strong collaborations with the Lisbon medical school and state-of-the-art medical imaging facilities. Besides the participation in the project activities, the researcher is also expected to be actively involved in the academic life of the institute, including the engagement in conferences and seminars.


The call is open until December 18th 2019.

To apply, please find the official notice here:

For further information, please contact Prof. Patrícia Figueiredo at:

Athanasios Vourvopoulos joins MIG_N2Treat team at LaSEEB

Thanos (Athanasios Vourvopoulos) has just joined the team of the FCT-funded project MIG_N2Treat: Multimodal neuroimaging biomarkers throughout the migraine cycle: towards neurofeedback training for personalized anti-migraine treatment. In this project, we aim to identify multimodal neuroimaging biomarkers of migraine, and to use these to develop a novel personalized therapeutic approach based on neurofeedback training. The project is led by ISR-Lisboa at Técnico and is carried out in collaboration with iMM and Hospital da Luz – Learning Health. During the next two years, Thanos will participate in the development of an EEG-neurofeedback system based on simultaneous EEG-fMRI models.

COST Action – Glioma MR Imaging 2.0

The COST Action proposal GliMR2.0, entitled Glioma MR Imaging 2.0, submitted to the Open Call of 2018 (OC-2018-2), has been approved for funding. Prof Patrícia Figueiredo from LaSEEB is one of the proponents of this European research network under the EU funded COST (The European Cooperation in Science and Technology) Programme. The LaSEEB contribution will essentially be in the context of  Working Group 1, on “Advanced MRI biomarkers for glioma characterisation”.|Name:overview

Keep in Touch 2019

LaSEEB participated in this year’s edition of Técnico’s Keep in Touch. The event is part of the celebrations of the 108th anniversary of Instituto Superior Técnico and aims to bring the IST community together.

Hundreds of children, youth, alumni, professors, researchers and staff participated in several activities organised by the several departments.



ISMRM 2019

Several researchers from LaSEEB presented their work during the 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM 2019).

Abstracts presented:

  • Breath-hold BOLD-fMRI cerebrovascular reactivity metrics predict cognitive impairment in cerebral small vessel disease; Pinto J et al.
  • Amplitude of low-frequency fluctuations in resting-state BOLD-fMRI is associated with cognitive decline in cerebral small vessel disease; Moreira J. et al.
  • Impact of processing options on histogram metrics extraction from DWI in cerebral small vessel disease; Fouto A. et al.
  • Classification of BOLD-fMRI dynamic functional connectivity states based on simultaneous EEG microstates; Abreu R. et al.
  • Classification of sleep stages from fMRI dynamic functional connectivity using deep learning; Carmona J. et al.
  • Accelerated Carotid 4D flow MRI with Multicontrast HD-PROST Reconstruction; Gaspar A. et al.
  • Improving T2 and B1 parametric estimation in the brain with multi spin-echo MR and fusion bootstrap moves solver (FBMS); Freitas A. et al.
  • Neuromelanin-sensitive Magnetic Resonance Imaging Study of the Substantia Nigra in Huntington’s Disease; Leitão R.  et al.
  • Optimizing Neuromelanin-sensitive Turbo Spin Echo sequences using the extended phase graph formalism including magnetization transfer effects; Nunes R. et al.