Elisabeth Preuhs (née Hoppe)
Dr.-Ing. Elisabeth Preuhs
Department of Computer Science
Chair of Computer Science 5 (Pattern Recognition)
91058 Erlangen
- Phone number: +49 9131 85-27874
- Fax number: +49 9131 85-27270
- Email: elisabeth.hoppe@fau.de
- Website: https://lme.tf.fau.de/person/hoppe/
Office hours
Academic CV
Education
- 10/2017 – 12/2020:
Researcher/PhD Candidate at Pattern Recognition Lab - 04/2015 – 08/2017:
Student at Friedrich-Alexander-Universität Erlangen-Nürnberg, Computer Science (Master of Science) - 10/2011 – 03/2015:
Student at Ostbayerische Technische Hochschule Regensburg, Medical Computer Science (Bachelor of Science)
Professional Employment
- 01/2021 – present:
Scientist/Innovation Project Lead, Siemens Heathineers (Advanced Therapies), Forchheim - 10/2017 – 12/2020:
Researcher, Siemens Healthineers (Magnetic Resonance), Erlangen - 12/2016 – 06/2017:
Master student, Siemens Healthineers (Magnetic Resonance), Erlangen - 09/2016 – 11/2016:
Internship, Audi Electronics Venture, Gaimersheim - 06/2015 – 06/2016:
Working student, Fraunhofer Institute for Integrated Circuits IIS, Erlangen - 01/2014 – 03/2014:
Working student, Institut für Vorsorge und Finanzplanung, Altenstadt a.d.Waldnaab - 08/2013 – 12/2013:
Internship, Fraunhofer Institute for Integrated Circuits IIS, Erlangen - 10/2012 – 01/2013:
Teaching assistant, Ostbayerische Technische Hochschule, Regensburg
Projects
Gender Equality
My ongoing project is about inspiring young girls for technical and computer science and gender equality. Get yourself informed, why this is so important.
There are plenty events supported by us: Schnupperuni, Schülerinfotag, Mädchen-und-Technik Tag, Girls’ Day, Forscherinnencamp. Please contact me, if you need further information.
3-D Multi-contrast Cardiac CINE Magnetic Resonance Imaging
Research project in cooperation with Siemens Healthineers, Erlangen
Magnetic resonance imaging (MRI) is a non-invasive imaging technique which is well suited for the diagnosis and monitoring of cardiovascular diseases because of its ability of visualizing the anatomy and the functional information of the heart. Additionally, with this technique a diversity of image contrasts is provided. However, cardiovascular MRI is challenging due to e.g. myocardial contraction and respiratory motion and thus not well-established for the clinical practice yet.
With iterative reconstruction methods, the acquisition time can be clearly reduced and the artifacts minimized at the same time. With the help of these methods a representation of the heart with a well spatial and temporal resolution (4-D representation) can be created.
Additionally, quantitative representation of physical relaxation times can be generated with so-called mapping techniques based on these different image contrasts. The aim of this PhD project is the extension of the temporal 3-D representation imaging technique for the heart with such a multi-contrast dimension. This extra dimension can lead to an enhanced separation between pathological and healthy myocardial tissues.
Deep Learning-based Cardiac Navigation for Continuous Cardiac Magnetic Resonance Imaging
Research project in cooperation with Siemens Healthineers, Erlangen
In order to resolve the imaged heart into multiple dynamic dimensions, e.g., respiration and cardiac phases, the data is acquired continuously and afterwards synchronized with the underlying motion within the reconstruction framework. For the cardiac motion, often an external device, e.g., an electrocardiogram (ECG) has to be placed on the subject and monitors the cardiac cycles. However, this additional device is error-prone due to the application within an MR scanner. Further, the workflow is more complicated and the overall scan time is prolonged. We aim at developing a deep learning-based cardiac navigation, which directly derive a specific timepoint during a cardiac cycle. Acquired imaging data is fed into a deep neural network classifier, which outputs the probabilities for an R-wave at every timepoint within the measurement. Using the detected R-waves, data can be binned into different cardiac phases for the dynamic reconstruction. This way, we eliminate the need for an external ECG-device and can simplify the overall workflow.
Deep Learning-based Magnetic Resonance Fingerprinting Reconstruction
Research project in cooperation with Siemens Healthineers, Erlangen
Magnetic Resonance Fingerprinting is a recently proposed quantitative imaging technique. By altering sequence parameters at every time point, different imaging constrasts can be acquired resulting in so-called fingerprints for every image position. These fingerprints are characteristic for the underlying tissue states and can be used to determine quantitative parameters, e.g. T1 and T2 relaxation times. Convential reconstruction methods use pattern matching methods and compare the measured fingerprints with a simulated base of possible fingerprints. However, these methods are inefficient in terms of time and storage and furthermore, can yield errors if the simulated base is too small. To overcome these limitations, we are working on deep learning based MRF reconstruction. Acquired fingerprints are used as inputs for a deep neural network, which directly predicts the quantitative parameters without the need for a comparison with a simulated data base.
Publications
2023
Journal Articles
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Automated Cardiac Resting Phase Detection Targeted on the Right Coronary Artery
In: Journal Machine Learning for Biomedical Imaging (2023)
ISSN: 2766-905X
DOI: 10.59275/j.melba.2023-afe2
BibTeX: Download
Conference Contributions
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Fully Automatic Scar Segmentation on LGE Images with Reduced Contrast Agent Dose
SCMR 26th Annual Scientific Sessions (San Diego, CA, January 25, 2023 - January 28, 2023)
In: Proceedings of the SCMR 26th Annual Scientific Sessions 2023
BibTeX: Download - , , , , , , :
Quantitative evaluation of denoising algorithms without noise-free ground-truth data
ISMRM
BibTeX: Download - , , , , , , , , , :
Robust multi-contrast MRI denoising using trainable bilateral filters without noise-free targets
International Symposium on Biomedical Imaging (ISBI)
BibTeX: Download - , , , , , , , , :
Unsupervised denoising of prostate DWI
ISMRM
BibTeX: Download
2022
Conference Contributions
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Improving the Sensitivity to Myocardial Scar in Automatic Segmentations of Left Ventricular Myocardium on LGE Images
Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (London, May 7, 2022 - May 12, 2022)
In: Proceedings of the International Society for Magnetic Resonance in Medicine 2022
URL: https://index.mirasmart.com/ISMRM2022/PDFfiles/1014.html
BibTeX: Download - , , , , , , :
Myocardial scar segmentation on cardiac LGE images with reduced contrast agent dose using deep learning
24. Jahrestagung der Deutschen Sektion der ISMRM (Aachen, September 21, 2022 - September 24, 2022)
In: Abstractband der 24. Jahrestagung der Deutschen Sektion der ISMRM 2022
BibTeX: Download - , , , , , , :
Training a tunable, spatially-adaptive denoiser without clean targets
ISMRM
BibTeX: Download
Thesis
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Acquisition and Reconstruction Methods for Multidimensional and Quantitative Magnetic Resonance Imaging (Thesis, 2022)
URL: https://opus4.kobv.de/opus4-fau/frontdoor/index/index/docId/19067
BibTeX: Download
2021
Journal Articles
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Deep learning-based ECG-free Cardiac Navigation for Multi-Dimensional and Motion-Resolved Continuous Magnetic Resonance Imaging
In: IEEE Transactions on Medical Imaging (2021)
ISSN: 0278-0062
DOI: 10.1109/tmi.2021.3073091
BibTeX: Download - , , , , , , , , , :
X-ray Scatter Estimation Using Deep Splines
In: IEEE Transactions on Medical Imaging (2021)
ISSN: 0278-0062
DOI: 10.1109/TMI.2021.3074712
BibTeX: Download
Conference Contributions
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2D Respiration Navigation Framework for 3D Continuous Cardiac Magnetic Resonance Imaging
German Workshop on Medical Image Computing, 2021 (Regensburg, March 7, 2021 - March 9, 2021)
In: Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (ed.): Informatik aktuell 2021
DOI: 10.1007/978-3-658-33198-6_38
BibTeX: Download - , , , , , , , , , :
Abstract: Simultaneous Estimation of X-ray Back-scatter and Forward-scatter using Multi-task Learning
German Workshop on Medical Image Computing, 2021 (Regensburg, March 7, 2021 - March 9, 2021)
In: Christoph Palm, Heinz Handels, Klaus Maier-Hein, Thomas M. Deserno, Andreas Maier, Thomas Tolxdorff (ed.): Informatik aktuell 2021
DOI: 10.1007/978-3-658-33198-6_62
BibTeX: Download
2020
Journal Articles
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Appearance Learning for Image-based Motion Estimation in Tomography
In: IEEE Transactions on Medical Imaging (2020)
ISSN: 0278-0062
DOI: 10.1109/TMI.2020.3002695
BibTeX: Download
Book Contributions
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Simultaneous Estimation of X-Ray Back-Scatter and Forward-Scatter Using Multi-task Learning
In: Anne L. Marte, lPurang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (ed.): Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 2020, p. 199-208
ISBN: 9783030597122
DOI: 10.1007/978-3-030-59713-9_20
BibTeX: Download
Conference Contributions
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DeepECG: Towards 3-D Continuous Cardiac MRI without ECG-Gating - Deep Learning based R-Wave Classification for Automated Cardiac Phase Binning
International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition (Sydney, April 18, 2020 - April 23, 2020)
BibTeX: Download - , , , , , , :
Learning how to Clean Fingerprints -- Deep Learning based Separated Artefact Reduction and Regression for MR Fingerprinting
International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition (Sydney, April 18, 2020 - April 23, 2020)
BibTeX: Download - , , , , , , , :
RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting
Bilderverarbeitung für die Medizin Algorithmen - Systeme - Anwendungen (Berlin, March 15, 2020 - March 17, 2020)
DOI: 10.1007/978-3-030-32248-9_11
BibTeX: Download - , , , , , , , , :
A Robust Deep-Learning-based Automated Cardiac Resting Phase Detection: Validation in a Prospective Study
International Society for Magnetic Resonance in Medicine (ISMRM) 28th Annual Meeting & Exhibition (Sydney, April 18, 2020 - April 23, 2020)
BibTeX: Download - , , , , , , :
Learning how to Clean Fingerprints -- Deep Learning based Separated Artefact Reduction and Regression for MR Fingerprinting
ISMRM Workshop on Data Sampling & Image Reconstruction (Sedona, January 26, 2020 - January 29, 2020)
BibTeX: Download
2019
Journal Articles
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Accurate fatty acid composition estimation of adipose tissue in the abdomen based on bipolar multi-echo MRI
In: Magnetic Resonance in Medicine 81 (2019), p. 2330-2346
ISSN: 0740-3194
DOI: 10.1002/mrm.27557
BibTeX: Download - , , , , , , , :
Symmetry prior for epipolar consistency
In: International Journal of Computer Assisted Radiology and Surgery (2019)
ISSN: 1861-6410
DOI: 10.1007/s11548-019-02027-8
BibTeX: Download
Book Contributions
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Magnetic Resonance Fingerprinting Reconstruction Using Recurrent Neural Networks
In: Rainer Röhrig, Harald Binder, Hans-Ulrich Prokosch, Ulrich Sax, Irene Schmidtmann, Susanne Stolpe, Antonia Zapf (ed.): German Medical Data Sciences: Shaping Change – Creative Solutions for Innovative Medicine, IOS Press, 2019, p. 126-133 (Studies in Health Technology and Informatics, Vol.267)
DOI: 10.3233/SHTI190816
BibTeX: Download
Conference Contributions
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RinQ Fingerprinting: Recurrence-Informed Quantile Networks for Magnetic Resonance Fingerprinting
Medical Image Computing and Computer Assisted Intervention (Shenzhen, October 13, 2019 - October 17, 2019)
In: Proceedings of MICCAI 2019, Cham: 2019
DOI: 10.1007/978-3-030-32248-9_11
BibTeX: Download - , , , , :
Motion Gradients for Epipolar Consistency
15th International Meeting on Fully Three-Dimensional Image Reconstruction (Philadelphia, PA, June 2, 2019 - June 6, 2019)
DOI: 10.1117/12.2532319
BibTeX: Download - , , , , , , , :
Automatic Cardiac Resting Phase Detection for Static Cardiac Imaging Using Deep Neural Networks
Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition (Montreal, May 11, 2019 - May 16, 2019)
In: Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition) 2019
URL: https://index.mirasmart.com/ISMRM2019/PDFfiles/2131.html
BibTeX: Download - , , , , , , , :
Free-Breathing, Self-Navigated and Dynamic 3-D Multi-Contrast Cardiac CINE Imaging Using Cartesian Sampling and Compressed Sensing
Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition) (Montreal, May 11, 2019 - May 16, 2019)
In: Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition) 2019
BibTeX: Download - , , , , , , , :
Maximum Likelihood Estimation of Head Motion Using Epipolar Consistency
Workshop on Bildverarbeitung fur die Medizin, 2019 (Lübeck, March 17, 2019 - March 19, 2019)
In: Thomas M. Deserno, Andreas Maier, Christoph Palm, Heinz Handels, Klaus H. Maier-Hein, Thomas Tolxdorff (ed.): Informatik aktuell 2019
DOI: 10.1007/978-3-658-25326-4_29
BibTeX: Download
2018
Conference Contributions
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Accuracy of Fatty Acid Quantification using Bipolar Multi-Echo MRI for Varying Numbers of Echoes
26th Annual Meeting & Exhibition (Paris, France)
In: Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2018
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Schneider18-AOF.pdf
BibTeX: Download - , , , , , , , , :
Deep Learning for Magnetic Resonance Fingerprinting: Accelerating the Reconstruction of Quantitative Relaxation Maps
Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (26th Annual Meeting & Exhibition) (Paris, France)
In: Proceedings of the Joint Annual Meeting ISMRM-ESMRMB (26th Annual Meeting & Exhibition) 2018
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Hoppe18-DLF.pdf
BibTeX: Download - , , , , , , , :
Fat Content and Fatty Acid Composition Quantification Using a 3D Stack-of-Radial Trajectory With Adaptive Gradient Calibration
26th Annual Meeting & Exhibition (Paris, France)
In: Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2018
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Schneider18-FCA.pdf
BibTeX: Download - , , , , , , , :
Gradient-Delay-Compensated Radial MRI for Fat Content and Fatty Acid Composition Quantification
Workshop on Quantitative Body Imaging (New Delhi, India)
In: Proceedings of the ISMRM Workshop on Quantitative Body Imaging 2018
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2018/Schneider18-GRM.pdf
BibTeX: Download
2017
Conference Contributions
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Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series
62. Jahrestagung der GMDS (Oldenburg, September 17, 2017 - September 21, 2017)
In: German Medical Data Sciences: Visions and Bridges, IOS Press: 2017
DOI: 10.3233/978-1-61499-808-2-202
URL: https://www5.informatik.uni-erlangen.de/Forschung/Publikationen/2017/Hoppe17-DLF.pdf
BibTeX: Download
Patents
No patents found.
Activities and Talks
2020
- Deep Learning-based Magnetic Resonance Fingerprinting
(Speech / Talk)
May 20, 2020, Event: Data Science Seminat at DKFZ, Deutsches Krebsforschungszentrum (DKFZ), URL: https://www.youtube.com/watch?v=OTtVnFCGm6I&feature=youtu.be - Deep Learning basiertes Magnetresonanz-Fingerprinting
(Speech / Talk)
June 18, 2020, Event: Gesellschaft für Informatik - Regionalgruppe Mittelfranken, URL: https://rg-mittelfranken.gi.de/veranstaltung/deep-learning-basiertes-magnetresonanz-fingerprinting - Künstliche Intelligenz für Magnetresonanztomographie
(Speech / Talk)
September 1, 2020, Event: Mädchen und Technik Praktikum 2020 - Medizinische Bildgebung und Künstliche Intelligenz für Medizinische Bildverarbeitung
(Speech / Talk)
November 3, 2020, Event: Forscherinnencamp 2020, URL: https://www.tf.fau.de/2020/10/allgemein/forscherinnen-camp-2020/
2019
- Medizinische 3-D Bildgebung
(Speech / Talk)
March 21, 2019, Event: Schüler-Infotag - Free-Breathing, Self-Navigated and Dynamic 3-D Multi-Contrast Cardiac CINE Imaging Using Cartesian Sampling and Compressed Sensing
(Speech / Talk)
May 13, 2019, Event: Joint Annual Meeting ISMRM-ESMRMB (27th Annual Meeting & Exhibition) - RinQ Fingerprinting: Recurrence-informed Quantile Networks for Magnetic Resonance Fingerprinting
(Speech / Talk)
October 14, 2019, Event: International Conference on Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - Digitale Bildverarbeitung
(Speech / Talk)
October 29, 2019, Event: Forscherinnen-Camp
2018
- Medizinische 3-D Bildgebung
(Speech / Talk)
March 8, 2018, Event: Schüler-Infotag - Deep Learning for Magnetic Resonance Fingerprinting: Accelerating the Reconstruction of Quantitative Relaxation Maps
(Speech / Talk)
June 21, 2018, Event: Joint Annual Meeting ISMRM-ESMRMB (26th Annual Meeting & Exhibition) - AI: Physician of the Future
(Speech / Talk)
October 31, 2018, Event: Schnupperuni
2017
- Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series
(Speech / Talk)
September 20, 2017, Event: 62. Jahrestagung der Deutschen Gesellschaft für medizinische Informatik, Biometrie und Epidemiologie e.v. (GMDS) - AI: Physician of the Future
(Speech / Talk)
October 31, 2017, Event: Schnupperuni
Awards
2020
- : FAU Innovatorin (Friedrich-Alexander-Universität Erlangen-Nürnberg) – 2020
2019
- : KI Newcomerin im Bereich Lebenswissenschaften (Gesellschaft für Informatik e.V.) – 2019
2018
- : conhIT Nachwuchspreis für praxisorientierte Abschlussarbeiten (2. Platz Master Thesis) – 2018
2015
- : conhIT Nachwuchspreis für praxisorientierte Abschlussarbeiten (1. Platz Bachelor Thesis) – 2015