Welcome to the Computational Imaging Lab!

Researchers at the Computational Imaging Lab work on advancing medical imaging through the development of novel MRI techniques and machine learning methods. Our focus lies in improving image quality, accelerating scan times, and enabling new clinical applications in areas such as musculoskeletal, cardiac, and abdominal imaging.

22SEP 2025
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Joint Lab Retreat with the Smart Imaging Lab
Earlier this month, we held our very first joint retreat together with the Smart Imaging Lab led by Prof. Dr. Jana Hutter. The goal of this two-day retreat in Ansbach was to explore opportunities for collaboration between our groups and to strengthen connections across labs. The program combined both scientific and social activities: from inspiring […]
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27MAR 2025
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New DFG grant
We are very excited to announce that our lab just received funding from DFG for a new 3-year research project. The goal of this joint project with the Department of Radiology (Armin Nagel and Rafael Heiß) it to develop an approach for MR imaging of orthopedic implants with reduced metal artifacts at 0.55T (Siemens Healthineers […]
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05FEB 2025
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New publication in NMR in Biomedicine
We are excited to share that our latest research, “Deep Learning‐Based Accelerated MR Cholangiopancreatography Without Fully‐Sampled Data”, has been published in NMR in Biomedicine! (http://DOI: 10.1002/nbm.70002) This study demonstrates how deep learning(DL)-based reconstructions can significantly accelerate MR cholangiopancreatography (MRCP) without full-sampled ground truth. We explored both supervised and self-supervised deep learning approaches. DL reconstructions could […]We are excited to share that our latest research, “Deep Learning‐Based Accelerated MR Cholangiopancreatography Without Fully‐Sampled Data”, has been published in NMR in Biomedicine! (http://DOI: 10.1002/nbm.70002) This study demonstrates how deep learning(DL)-based reconstructions can significantly accelerate MR cholangiopancreatography (MRCP) without full-sampled ground truth. We explored both supervised and self-supervised deep learning approaches. DL reconstructions could […]
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19DEC 2024
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New publication selected as MRM editor’s pick for January 2025
We are very happy to announce that Marc Vornehm‘s paper “CineVN: Variational network reconstruction for rapid functional cardiac cine MRI” was chosen as an MRM editor’s pick for January 2025. We developed a machine learning reconstruction for cardiac cine MRI and validated it with the estimation of ventricular volumes and myocardial strain in healthy subjects […]
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29AUG 2024
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New publication in Magnetic Resonance in Medicine
We are happy to announce that our latest publication “CineVN: Variational network reconstruction for rapid functional cardiac cine MRI” (https://onlinelibrary.wiley.com/doi/10.1002/mrm.30260) has been published in Magnetic Resonance in Medicine! We demonstrate fast inline reconstructions of high-resolution real-time cine MRI with better image quality and more accurate evaluation of cardiac function and myocardial strain compared to state-of-the-art […]We are happy to announce that our latest publication “CineVN: Variational network reconstruction for rapid functional cardiac cine MRI” (https://onlinelibrary.wiley.com/doi/10.1002/mrm.30260) has been published in Magnetic Resonance in Medicine! We demonstrate fast inline reconstructions of high-resolution real-time cine MRI with better image quality and more accurate evaluation of cardiac function and myocardial strain compared to state-of-the-art […]
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29JUL 2024
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Exciting News: Prof. Florian Named ISMRM Fellow for Innovative MRI Research!
We are happy to announce that Prof. Florian has been honored as a Fellow of the International Society for Magnetic Resonance in Medicine (ISMRM) at their annual meeting in Singapore. The recognition marks Prof. Knoll’s significant contributions to the field of magnetic resonance image reconstruction, including pivotal work on machine-learning-based image reconstruction. For more details, […]
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Contact Information

Contacts

Prof. Dr. Florian Knoll

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

Email: florian.knoll@fau.de