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Principle Investigator
Postdocs
Dr. Francisco J. Fritz
Dr. Francisco J. Fritz
"My research lies in pushing the boundaries of MRI, particularly for ex vivo human imaging. I specialize in tailoring MR sequences to capture the intricate details of human anatomy, both in vivo and ex vivo. Delving into the realm of diffusion and relaxometry MR images, my research focuses on sophisticated analysis and modeling. This approach allows us to unravel the complexities of tissue microstructure, providing a detailed understanding of the composition and behavior of biological tissues at a microscopic level. Bridging the gap between research and practical applications, my efforts extend to clinical realms. I leverage optimized MR sequences and microstructure analysis techniques to contribute to the characterization of microstructures associated with epilepsy and tumors. This has significant implications for improving diagnostic precision and tailoring treatment strategies for better outcomes in these critical medical areas. Committed to the growth of the scientific community, I actively engage in the supervision of Master's and PhD students. Guiding the next generation of researchers, I aim to instill a passion for innovation, critical thinking, and excellence in scientific exploration."
f.lagosfritz@uke.de
Dr. Björn Fricke
Dr. Björn Fricke
"My project aims at helping to develop a user-friendly open-source toolbox denoted ACID for more reproducible and standardized usage of diffusion MRI in the spinal cord, for post-mortem samples, and the brain in neuroscience and clinical research studies.
To achieve this, the following aspects of the existing prototype of the ACID toolbox will be enhanced:
Improving the functionality and making it more user-friendly (e.g., by adding BIDS support).
Addition of new features such as optimizing all ACID modules for applications into the spinal cord and post-mortem.
Better understanding of noise which is important for the correct performance of denoising and rician bias correction."
bjoern.fricke@uni-luebeck.de
Dr. Laurin Mordhorst
Dr. Laurin Mordhorst
"My goal is to make MRI-based characterization of axons, the telephone cables of the human brain, accessible for clinical and neuroscience research. In particular, I quantify the radii of axons, which are related to the speed of information transfer; hence, axon radii quantification using non-invasive MRI-based methods may help quantify brain structure, function and health.
On one hand, I am improving the histological gold standard for MRI-based axon radii estimation by analzing large microscopy images including of millions axons using deep-learning based segmentation approaches. On the other hand, I am validating current models for MRI-based axon radii estimation against our new histological gold standard to assess feasibility and caveats of current modeling approaches, thereby contributing towards establishing the MRI-based axon radius estimates as a biomarker for the human brain."
laurin.mordhorst@uni-luebeck.de
Dr. Jan Malte Oeschger
Dr. Jan Malte Oeschger
"My work focuses on numerical methods for parameter estimation, bias correction, and model comparison in biophysical systems using diffusion MRI.
In my current postdoc, I lead the data analysis for a multi-site clinical imaging study across five European research centers. The goal is to optimize a 1-hour brain imaging protocol for epilepsy diagnostics, balancing acquisition speed, spatial resolution, signal quality, and cross-site reproducibility across different MRI hardware systems.
During my PhD, I built Monte Carlo simulation frameworks to stress-test estimators under noisy, real-world conditions against controlled ground truths and ran systematic model comparisons across 100k+ data points to identify the root causes of estimation errors. This allowed me to compare competing DKI models, including the standard and axisymmetric variations, and to characterize when and why they break. I implemented a bias correction algorithm based on a Gauss-Newton optimizer that reduced the data quality requirement for accurate estimation by 3x, enabling reliable results in time-constrained clinical settings."
jan.oeschger@uni-luebeck.de
PhD Students
Nina Lüthi
Nina Lüthi
"My project aims to explore discrepancies in ex-vivo diffusion-weighted images of entire human brains compared to in-vivo human brains. Ex-vivo samples hold significant value for obtaining high-resolution images that can be compared either with living brains or histological counterparts. Ideally, these samples would mirror one another, but factors such as fixation and post-mortem interval can introduce variations.
Therefore, my research focuses on investigating the longitudinal impact of formaldehyde fixation on diffusion-weighted images of whole human brains. Potential effects of fixation may alter the microstructure, posing challenges in comparing ex-vivo and in-vivo diffusion MRI data. Consequently, it is crucial for my work to assess and account for these influences in ex-vivo models."
nina.luethi@uni-luebeck.de
Antonia Bortolazzi
Antonia Bortolazzi
"My project involves the adaptation and optimization of MRI pulse sequences to improve data quality, acquisition efficiency, and motion robustness in quantitative brain imaging. I conduct my PhD in collaboration with Siemens Healthineers, which enables the direct translation of methodological innovations into advanced scanner implementations and provides a strong link between academic research and clinical technology advancements. By combining methodological development with the analysis of anatomical and microstructural brain properties, my research contributes to the advancement of time-efficient, high-resolution quantitative MRI for both neuroscience research and future clinical applications.
Currently, my research topic focuses on the quantitative characterization of brain microstructure using advanced MRI techniques, with a particular emphasis on myelin water imaging (MWI). I investigate parameters such as the myelin water fraction, which serves as a proxy for myelin content and therefore provides insight into white-matter integrity, brain development, and possible neurological disease processes. A central aim of my work is to determine how these microstructural parameters can be reliably estimated from alternative MRI acquisitions, enabling higher spatial resolution and reduced scan times while maintaining quantitative accuracy."
antonia.bortolazzi@uni-luebeck.de
Laura Bogs
Laura Bogs
"My work focuses on the longitudinal analysis of qMRI parameters across different tissue states, following the transition from in-vivo measurements to ex-vivo conditions, including the process of tissue fixation. By systematically comparing these stages, I investigate how preparation steps and environmental changes influence qMRI-derived metrics over time.
In addition, I work on developing automated approaches to streamline data processing. This includes implementing deep learning methods for automatic tissue segmentation in ex-vivo datasets, enabling faster and more consistent analysis. A further objective is the development of automated registration techniques to align ex-vivo and in-vivo data, facilitating direct comparison across conditions.
Through these efforts, my work aims to improve the efficiency, scalability, and comparability of qMRI analyses across different experimental settings."
l.bogs@uni-luebeck.de
Jan Meyer
Jan Meyer
"My project tries to combine different MRI contrasts and sequences to better identify epileptogenic regions in the brain. For this I try to train deep learning networks on MRI data acquired from drug-resistant temporal lobe epilepsy patients in order to combine different contrasts to enhance imaging leading to potentially better diagnosis and thus treatment outcomes.
Additionally, I help with the processing of the µCT data acquired at the DESY using neural networks to automatically segment cells, blood vessels and other small structures in these images. Labeling these huge datasets manually is practically impossible and thus automatic analyses using deep learning offer new ways to process this kind of data."
jan01.meyer@uni-luebeck.de
Master Students
Sophie Otte
Sophie Otte
"My master thesis focuses on investigating temperature-dependent effects in quantitative MRI (qMRI) using ex-vivo tissue samples. I design and conduct controlled experiments in which temperature is systematically varied, allowing me to isolate its influence on specific MRI-derived parameters. This includes setting up measurement protocols, acquiring imaging data across defined temperature ranges, and ensuring experimental stability throughout the process.
A central part of my work is the analysis and interpretation of qMRI metrics, with the aim of identifying how and to what extent they change with temperature. By combining experimental data with quantitative evaluation methods, I work to characterize these dependencies and assess their implications for measurement accuracy.
Through this approach, I contribute to improving the understanding of how temperature affects qMRI measurements and to refining methodologies that enable more consistent and reliable parameter estimation under controlled conditions."
sophie.otte@studium.uni-hamburg.de
Student Assistants
Ali Aghajan
Ali Aghajan
ali.aghajan-2@studium.uni-hamburg.de
Alumnis
- Mohammad Ashtarayeh
- Dr. Nils Bodammer
- Alexander Crabbe
- Dr. Isabel Ellerbrock
- Joshua Engels
- Sara Holm
- Hatice-Kübra Ögreten
- Dr. Sebastian Papazoglou
- Dr. Davide Santoro
- Prof. Dr. Mohammad Hassan Farsbaf Shaker
- Tobias Streubel
- Dr. Thibault Tabarin
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