• 09/2017 – 08/2021:
  Researcher/PhD Candidate at Pattern Recognition Lab
• 04/2015 – 08/2017:
  Student at Friedrich-Alexander-Universität Erlangen-Nürnberg, Medical Engineering (Master of Science)
• 05/2011 – 04/2015:
  Student at Friedrich-Alexander-Universität Erlangen-Nürnberg, Medical Engineering (Bachelor of Science)

Magnetic Resonance Imaging Contrast Synthesis

Research project in cooperation with Siemens Healthineers, Erlangen

A Magnetic Resonance Imaging (MRI) exam typically consists of several MR pulse sequences that yield different image contrasts. Each pulse sequence is parameterized through multiple acquisition parameters that influence MR image contrast, signal-to-noise ratio, acquisition time, and/or resolution.

Depending on the clinical indication, different contrasts are required by the radiologist to make a reliable diagnosis. This complexity leads to high variations of sequence parameterizations across different sites and scanners, impacting MR protocoling, AI training, and image acquisition.

MR Image Synthesis

The aim of this project is to develop a deep learning-based approach to generate synthetic MR images conditioned on various acquisition parameters (repetition time, echo time, image orientation). This work can support radiologists and technologists during the parameterization of MR sequences by previewing the yielded MR contrast, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training.

MR Image-to-Image Translations

As MR acquisition time is expensive, and re-scans due to motion corruption or a premature scan end for claustrophobic patients may be necessary, a method to synthesize missing or corrupted MR image contrasts from existing MR images is required. Thus, this project aims to develop an MR contrast-aware image-to-image translation approach, enabling the synthesis of missing or corrupted MR images with adjustable image contrast. Additionally, this work can be used as an advanced data augmentation technique to synthesize different contrasts from a single MR image to enhance the training of AI applications in MRI.

Machine Learning Applications in Magnetic Resonance Imaging beyond Image Acquisition and Interpretation

Research project in cooperation with Siemens Healthineers, Erlangen

Magnetic Resonance Imaging (MRI) is an important but complex imaging modality in current radiology. Artificial intelligence (AI) can play an important role for acclerating MR sequence acquisition as well as supporting image interpretation and diagnosis. However, there are also opportunities besides image acquisition and interpretation for which AI can play a vital role to optimze the clinical workflow and decrease costs.

Automated Protocoling

One critical workflow step for an MRI exam is protocoling, i.e., selecting an adequate imaging protocol under consideration of the ordered procedure, clinical indication, and medical history. Due to the complexity of MRI exams and the heterogeneity of MR protocols, this is a nontrivial task. The aim of this project is to analyze and quantify challenges complicating a robust approach for automated protocoling, and propose solutions to these challenges.

Automated Billing

Moreover, reporting and documentation is a crucial step in the radiology workflow. We have therefore automated the selection of billing codes from modality log data for an MRI exam. Integrated into the clinical environment, this work has the potential to free the technologist from a non-value adding administrative task, enhance the MRI workflow, and prevent coding errors.

2021

• ISMRM Magna Cum Laude Merit Award (top 15%)
  “Fat-Saturated MR Image Synthesis with Acquistion Parameter-Conditioned Image-to-Image Generative Adversarial Network”

2020

• ISMRM Summa Cum Laude Merit Award (top 5%)
  “Acquisition Parameter Conditioned Generative Adversarial Network for Enhanced MR Image Synthesis”