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

Office hours

by appointment

My 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, I have worked on 4D-CT for radiation therapy planning.

  • 10/2021 – present
    Ph.D. Candidate at the Computational Imaging Lab, FAU Erlangen-Nürnberg
    in collaboration with Siemens Healthineers, Erlangen
  • 05/2024 – 08/2024
    Visiting Fellow at the 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 in Cardiovascular MRI Predevelopment at Siemens Healthineers, Erlangen
  • 05/2020 – 07/2021
    Student Tutor in Deep Learning at the Pattern Recognition Lab, FAU Erlangen-Nürnberg
  • 11/2018 – 10/2020
    Working Student in CT Predevelopment at Siemens Healthineers, Erlangen
  • 02/2018 – 04/2018
    Internship at Fraunhofer IIS, Erlangen
  • 10/2015 – 07/2017
    Student Tutor in Algorithms and Data Structures at the 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
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 Ongoing
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