Application Information
We always have open topics for Bachelor and Master Theses, and Projects. We encourage you to submit your own topic, related to our lab research. General topics and fields of research can typically be found on the personal pages of the individual group members. Formulate a research question and provide an outline of your chosen approach. Please include 1-2 paper references as supporting literature for your approach. For most topics, students should complete the Computational MRI course (lecture and exercise) beforehand, as it provides essential skills to work on your topic.
Please use the application form below to apply at our lab!
To ensure a fair and organised process, we kindly ask that all supervision requests go through this form. We will then contact you within the next two weeks!
Topics and Guidelines
We encourage students to propose their own research topics based on their interests. A great starting point is exploring the research fields of our lab members to find inspiration. If you do not yet have a specific topic in mind, you may request one of our topics. However, we strongly prefer students to take the initiative in shaping their own research questions. We look forward to your ideas and enthusiasm!
Research Field #1 – Multi-Spectral MRI Reconstruction for Metal Artifact Reduction
This field focuses on exploring conventional MRI reconstruction methods, including parallel imaging, compressed sensing, and iterative reconstruction, etc. A major goal is to develop efficient reconstruction strategies that reduce acquisition time while maintaining high image quality. A particular focus is multi-spectral imaging (MSI) which acquires additional spectral components to provide complimentary information. MSI is especially relevant in the presence of metal implants, where strong off-resonance effects can lead to severe metal-induced artifacts. By incorporating information from multiple spectral components, conventional reconstruction methods can guarantee image quality and reduce these artifacts.
This work involves implementation and evaluation of different conventional reconstruction approaches, as well as methods for artifact correction. In addition, we investigate how different coil sensitivity estimation methods influence reconstruction quality and robustness.
Research Field #2 – Breast DCE-MRI Reconstruction using DL
This work develops deep learning (DL) models to make breast MRI faster and more accurate. Breast MRI is the most sensitive imaging tool for detecting breast cancer. Its core technique, dynamic contrast-enhanced MRI (DCE-MRI), tracks contrast agent uptake over time to help distinguish malignant from benign lesions. However, slow acquisition forces a trade-off between spatial and temporal resolution. Deep learning addresses this by reconstructing high-quality images from highly undersampled data. We focus on unrolled models such as MoDL and VarNet, as well as self-supervised methods such as SSDU which do not require fully sampled ground truth. We are also interested in breast MRI segmentation to support tumor detection and quantitative analysis.
Research Field #3 – Learning-Based Cardiac Cine MRI Reconstruction
This field develops deep learning (DL) models for fast, accurate reconstruction of cardiac cine MRI, which captures the motion of the beating heart. Conventional cine MRI relies on cardiac gating and breath-holds, which are difficult for many patients. Real-time imaging without gating requires very high acceleration rates while preserving image quality. Deep learning makes this possible by reconstructing high-quality images from highly undersampled data. We focus on unrolled models such as MoDL and VarNet, as well as self-supervised methods such as SSDU and Zero-Shot SSDU, which do not require fully sampled ground truth. We also explore transformers, dual-domain networks, and coordinate-based networks. Additionally, we are interested in cardiac segmentation to support functional assessment.
Department Thesis Guidelines
There is a [general workflow] for theses at the Department AIBE, which should be read carefully. The following information does not replace this workflow, but rather supplements it with lab-specific information.
Thesis Registration
After a successful application, a supervisor will be assigned to the student. The student submits an abstract (with additional data and photo, if desired) using the [AIBE template] and a certificate of enrollment digitally to the supervisor. The student completes the Registration & Agreement form from the [AIBE website] (note special cases such as the need for a medical supervisor in Medical Engineering), signs it, and sends it to the supervising professor and supervisor.
MRI Colloquium
The MRI colloquium is hosted together with other labs working on MRI and takes place on Thursdays at 11am during the lecture period. Invitations are sent out regularly and we would like to invite you to attend.
Presentation
The results of your thesis or project should be presented in a final talk during the MRI colloquium or the lab meeting: thesis presentations are 20 minutes, and project presentations are 15 minutes. This presentation should be given 2-4 weeks before your deadline. This gives you the chance to include additional aspects from the discussion in your thesis or report. Please discuss the date of your presentation with your supervisor as early as possible to make sure that your favoured date is still available. The contribution of the presentation to your final grade depends on your study program.
Code
All of your code is required to be archived in a Git repository in our group on the [GitLab server of the RRZE]. Your supervisor will create a repository for you and invite you to it. We recommend to get familiar with Git as early as possible and to use it right from the beginning of your implementation work. Please find more information about the GitLab server [here]. Access to the GitLab server needs to be requested via the [IdM-Portal]. Note that Git is intended for text-based files (i.e., source code), and not for large files like PDFs or images. For this reason, projects on [gitlab.rrze.fau.de] have a quota of 2GB.
Language
The common language at the lab is English. Hence, all oral presentations should be given in English. Master’s theses should be written in English. Bachelor’s theses may be written in German, if your supervisor agrees. However, we highly recommend to write it in English as well.
Writing your Thesis / Research Project
The thesis or report needs to be written with LaTeX using the CIL thesis [template]. Your thesis should follow the common structure of scientific documents. Please also consider style, syntax, grammar, and punctuation as this highly influences the readability of your work and will also be taken into account for your grade. Please also refer to the guidelines of the Pattern Recognition Lab [PR Thesis Guidelines] when preparing your thesis.
Thesis Submission
Your thesis needs to be submitted as one printout version and as a PDF file to your supervisor before your deadline. Your implementations have to be handed over to your supervisor in the form of a Git repository on the [GitLab server of the RRZE]. This includes all code that is required to reproduce your results. All data that you have used or generated for your thesis needs to be accessible to your supervisor after your submission. This includes all your results, scripts for processing the data (if not already included in your Git repository), figures, etc. Details on the submission format of this data may vary and should be discussed individually with your supervisor. In any way, it is your responsibility to provide your supervisor with everything that would be required to reproduce your results.