I studied Biomedical Engineering in St. Louis, MO (USA) at Saint Louis University (B.S. 2014) where I focused on tissue engineering. I then completed the Erasmus Mundus' Common European Master's Course in Biomedical Engineering (CEMACUBE) at RWTH Aachen University (RWTH Aachen, Germany) and the Czech Technical University in Prague (ČVUT Prague, Czech Republic) where I specialized in Medical Imaging Instrumentation (M.Sc. 2016). My interests have evolved from regenerative medicine in my bachelor's to medical imaging in the master's.

My doctoral research, which was funded by the German Academic Exchange Service (DAAD), focuses on developing deep learning techniques for automating the processing and analysis of MRSI data. The goal has been to accelerate traditional methods to facilitate acquiring larger-scale data, with the end-goal being to develop tools to better characterize and study brain tumors. 

Throughout my doctoral research, I have become acutely interested in developing deep learning techniques for modeling physical phenomena especially in inverse problems. In my work, this has arisen as multivariate modeling of in vivo spectra, but is also applicable to other types of imaging modalities as well as tumor modeling. I'm optimistic to see what research opportunities lie ahead!

• 2018 - present Ph.D. Technical University of Munich, Munich, Germany - DAAD Fellow

• 2016 MSc. Biomedical Engineering - Czech Technical University in Prague, Prague Czech Republic

• 2014 - 2015 RWTH Aachen University, Aachen, Germany

• 2014 B.Sc. Biomedical Engineering - Saint Louis University, St. Louis, MO USA

• 2011 Study Abroad - Saint Louis University - Madrid, Madrid, Spain

• Studying the domain shift of DL models trained on synthetic MRS data

• Deep learning for automating the processing and quantification protocols of large-scale MRS(I) brain spectra

• Simulating in vivo-like synthetic MRS data

• Deep learning techniques for quantitative mathematical methods

  • I.e. Developing DL techniques to accelerate solving complex mathematical models that represent physical phenomena

• Uncertainty quantification for DL models

• MR(S)I reconstruction and super-resolution

MRS is a quantitative MR technique used to characterize the metabolism or metabolic composition of tissue in vivo. This is done by analyzing the specific frequencies present in the FID, instead of just considering the magnitude which is used for qualitative imaging. These frequencies are metabolite dependent, making them easy to identify with a priori knowledge. While all protons resonate at the same Larmor frequency, the precessions that occur when excited by the RF pulse cause small changes in the local magnetic field experienced by the nucleus. MR-visible molecules are visible because there are many of these small perturbations that cumulatively change the local effective magnetic field of the molecule. This change in the magnetic field changes the molecule's resonant frequency. In the frequency domain, this change in resonant frequency shifts the frequency peaks to the left or the right. This chemical shift is unique to each molecule and by comparing peak profiles of different metabolites, maps of relative metabolite concentrations can be generated. This type of information is useful for characterizing tissue, including pathologies such as tumors.

Deep Learning is an off-shoot of machine learning that focuses on using models with multiple hidden layers between the input and output layers. During training, data is fed into the model where many mathematical operations occur until the output is calculated. Then based on a chosen optimization scheme, a loss or penalty is calculated depending on how good or bad the output is. Some form of gradient descent is used to update the parameters of the model that were used in the mathematical operations mentioned above. The model continues to update the parameters until the model converges. This basic framework can be used for things such as object detection, image classification, multivariate regression, reinforcement learning, image and text translations, and many more. As different as these applications may seem, they all have at least one thing in common which is the fundamental task of all machine learning techniques: pattern recognition. Networks can easily become very complicated in order to achieve a specific goal. The way these networks are optimized can also vary greatly. But the underlying goal of every model is to learn patterns that lead to accurate predictions, classifications, or regressions.

• Li Lab (Dr. Yan Li), Center for Intelligent Imaging, University of California, San Francisco (UCSF)

• MRS Synthetic Data Working Group

• AI for MRS Working Group

  • Munich Institute of Biomedical Engineering, Technical University of Munich (TUM)

  • Technical University of Eindhoven (TUE)

  • Columbia University