Marc Vornehm

Marc Vornehm, M. Sc.

PhD Candidate

Department Artificial Intelligence in Biomedical Engineering (AIBE)
W3-Professur für Computational Imaging

Room: Room 2.02
Werner-von-Siemens Str. 61
91052 Erlangen

Marc’s research focuses on Deep Learning-based reconstruction of accelerated dynamic MRI acquisitions in the cardiac domain, for instance time-resolved cine imaging of the heart. Other research interests include segmentation and registration tasks in cardiac MRI. Previously, he has worked on 4D-CT for radiation therapy planning.

  • 10/2021 – present
    Ph.D. Candidate at Computational Imaging Lab, FAU Erlangen-Nürnberg
    in collaboration with Siemens Healthineers, Erlangen
  • 05/2024 – 08/2024
    Visiting Fellow at Cardiovascular MRI Lab, The Ohio State University, Columbus, Ohio
  • 10/2018 – 08/2021
    M.Sc. in Medical Engineering at FAU Erlangen-Nürnberg
    Master’s Thesis written at Siemens Healthineers, Erlangen
  • 10/2014 – 10/2018
    B.Sc. in Medical Engineering at FAU Erlangen-Nürnberg
    Bachelor’s Thesis written at Fraunhofer IIS, Erlangen

  • 10/2024 – present
    Application Developer for Cardiovascular MRI Predevelopment at Siemens Healthineers, Erlangen
  • 10/2021 – 09/2024
    Researcher at Computational Imaging Lab, FAU Erlangen-Nürnberg
  • 05/2020 – 07/2021
    Student Tutor in Deep Learning at Pattern Recognition Lab, FAU Erlangen-Nürnberg
  • 11/2018 – 10/2020
    Working Student in CT Predevelopment at Siemens Healthineers, Forchheim
  • 02/2018 – 04/2018
    Internship at Fraunhofer IIS, Erlangen
  • 10/2015 – 07/2017
    Student Tutor in Algorithms and Data Structures at Pattern Recognition Lab, FAU Erlangen-Nürnberg

2024

Journal Articles

Conference Contributions

2023

Journal Articles

Conference Contributions

2022

Conference Contributions

2021

Journal Articles

2020

Journal Articles

Student Title Type Status
Utkarsha Shukla GAN-based cardiac cine MRI reconstruction Project Ongoing
Chethana Neelakanta Robustness of Deep Learning-Based MRI Reconstruction to Resolution Variability Project Ongoing
Dhrutiv Bhavsar Spatiotemporal MoDL for cardiac cine MRI reconstruction Project Ongoing
Erik Stein Inline Implementation of Low-Latency Reconstruction for Real-Time Cardiac Cine MRI Using FIRE Project Finished
Maximilian Riehl Model-based Deep Learning Reconstruction of Cardiac T1 and T2 mapping Master’s Thesis Finished
Yannik Ott Deep Learning-Based Reconstruction of Radial Real-time Cine MRI Using Image-Based and k-Space-Based Methods Master’s Thesis Finished
Florian Fürnrohr Zero-shot Learning Reconstruction for 3D MR Angiography Master’s Thesis Finished
Yannik Ott Domain-Adaptive Heatmap Regression for Landmark Detection in Cardiac MRI Project Finished
Navaneeth Narayanan Low-field MRI Reconstruction Using the M4Raw Dataset and Investigating the Domain Gap Between fastMRI and M4Raw Datasets Project Finished
Maximilian Riehl Time-Resolved Coil Profiles for Dynamic MRI Reconstruction Project Finished
Karlo Fonseca Test-Time Training for Reconstruction of Low-field MRI Project Finished
Arpita Halder Self-supervised Learning for MRI Reconstruction of Cardiac Cine MRI Using VORTEX Project Finished
Daniel Amsel End-to-End Deep Learning-Based Reconstruction of Accelerated T1 Mapping for Cardiac MRI Master’s Thesis Finished
Lucas Lemos Franco & Lukas Bolay Score-Based Diffusion Models for Accelerated MRI Reconstruction Project Finished
Daniel Amsel Motion-Informed Cardiac Cine MRI Reconstruction Using a Variational Network and Voxelmorph Project Finished
Ute Spiske Deep Learning-Based Scar Quantification on Contrast-Enhanced Cardiac MRI Master’s Thesis Finished
Ute Spiske Evaluation of U-Net for Scar Segmentation on Late Gadolinium Enhancement Cardiac MR Images Research Laboratory Finished