Carolin Pirkl

Carolin Pirkl PhD candidate Image-Based Biomedical Modeling Group (IBBM) Fakultät für Informatik Technische Universität München Contact: carolin.pirkl@tum.de Linkedin

Research Interests

• Magnetic Resonance Imaging (MRI) with a focus on quantitative/multi-parametric techniques
• Physics-informed Deep Learning

Background

• since 2017: PhD candidate | IBBM, Computer Science, TUM, Munich, Germany & GE Healthcare, Munich, Germany
  Deep Learning for fast and robust multiparametric MRI
  
• 2017: Research Assistant | Department of Radiology, University Hospital Munich, Germany
  Simultaneous EEG and fMRI of neurofeedback
  
• 2017: MSc Physics | University of Munich, Germany
  Water exchange in cerebral perfusion MRI – a Bayesian approach
  
• 2016-2017: Research Assistant | Department of Radiology, University Hospital Munich, Germany
  Dynamic contrast-enhanced MRI of the brain
  
• 2015: Intern | KPMG AG, Consulting Finance, Munich, Germany
  Credit risk modeling
  
• 2015: Visiting graduate student | Karolinska University Hospital & Stockholm University, Sweden
  M.Sc. Medical Physics
  
• 2014: Student research intern | Ryerson University, Toronto, Canada
  Optoacoustic microscopy for cell characterization
  
• 2014: BSc Physics | University of Munich, Germany
  Clinical IMRT quality assurance: Evaluation of a rotating phantom for measurement of 3D dose distributions
  

Publications

• CM. Pirkl*, PA. Gómez*, I. Lipp, G. Buonincontri, M. Molina-Romero, A. Sekuboyina, D. Waldmannstetter, J. Dannenberg, S. Endt, A. Merola, JR. Whittaker, V. Tomassini, M. Tosetti, DK. Jones, BH. Menze†, MI. Menzel†. Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting. In: Third Conference on Medical Imaging with Deep Learning. PMLR Vol. 121 (2020).
• PA. Gómez, M. Cencini, M. Golbabaee, RF. Schulte, C. Pirkl, I. Horvath, G. Fallo, L. Peretti, M. Tosetti, BH. Menze, G. Buonincontri. Rapid three-dimensional multiparametric MRI with quantitative transient-state imaging. In: Scientific Reports 10:13769 (2020).
• M. Golbabaee, G. Buonincontri, CM. Pirkl, MI. Menzel, BH. Menze, M. Davis, PA. Gómez. Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks. In: Medical Image Analysis 69:101945(2021)
• CM. Pirkl, L. Nunez-Gonzalez, F. Kofler, S. Endt, L. Grundl, M. Golbabaee, PA. Gómez, M. Cencini, G. Buonincontri, RF. Schulte, M. Smits, B. Wiestler, BH. Menze†, MI. Menzel†, JA. Hernandez-Tamames†. Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imaging. Neuroradiology (2021).
• CM. Pirkl, M. Cencini, JW. Kurzawski, D. Waldmannstetter, H. Li, A. Sekuboyina, S. Endt, L. Peretti, G. Donatelli, R. Pasquariello, M. Costagli, G. Buonincontri, M. Tosetti, MI. Menzel†, BH. Menze†. Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping. In press: Forth Conference on Medical Imaging with Deep Learning. PMLR (2021).

• CM. Pirkl, I. Lipp, G. Buonincontri, M. Molina-Romero, A. Sekuboyina, D. Waldmannstetter, J. Dannenberg, V. Tomassini, M. Tosetti, DK. Jones, MI. Menzel, BH. Menze, PA. Gómez. Deep learning-enabled diffusion tensor MR Fingerprinting. In: Proceedings of the 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) (2019).
• M. Golbabaee, CM. Pirkl, MI. Menzel, G. Buonincontri, PA. Gómez. Deep MR Fingerprinting with total-variation and low-rank subspace priors. In: Proceedings of the 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) (2019).
• C. Hecking-Veltman, CM. Pirkl, J. Dannenberg, RF. Schulte, T.Sprenger, BH. Menze, MI. Menzel. Estimation and Correction of Image Phase in diffusion weighted MRI using Deep Learning. In: Proceedings of the 27th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) (2019)
• N. Andriamanga, CM. Pirkl, A. Sekuboyina, G. Buonincontri, MI. Menzel, PA. Gómez, M. Piraud, BH. Menze. Long Short Term Memory Recurrent Neural Network for MR Fingerprint-ing parameter estimation. In: Proceedings of the 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) (2019).
• I. Horvath, CM. Pirkl, G. Buonincontri, MI. Menzel, PA. Gómez, BH. Menze. Deep learning for 3D MR fingerprinting: a dual pathway parameter mapping and reconstruction approach. In: Proceedings of the 36th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) (2019).
• CM. Pirkl, L. Nuñez-Gonzalez, S. Endt, G. Buonincontri, RF. Schulte, PA. Gómez, M. Smits, BH. Menze, MI. Menzel, JA. Hernandez-Tamames. Accelerated 3D multiparametric MRI in glioma patients - Initial clinical experience. In: Proceedings of the 28th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) (2020).
• CM. Pirkl, I. Horvath, S. Endt, G. Buonincontri, MI. Menzel, PA. Gómez, BH. Menze. Synchronizing dimension reduction and parameter inference in 3D multiparametric MRI: A hybrid neural network approach. In: Proceedings of the 28th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) (2020).
• S. Endt, CM. Pirkl, CM. Verdun, BH. Menze, MI. Menzel. A deep-learning approach to accelerated T1-T2-relaxation-correlation imaging. In: Proceedings of the 37th Annual Scientific Meeting of the European Society for Magnetic Resonance in Medicine and Biology (ESMRMB) (2020).
• F. Wiesinger, G. McKinnon, S. Kaushik, AB. Solana, E. Ljungberg, M. Vogel, N. Takei, R. Schulte, C. Pirkl, C. Cozzini, L.Nuñez-Gonzalez, JA. Hernandez Tamames, M. Engström. 3D Silent Parameter Mapping: Further refinements & quantitative assessment. In: Proceedings of the 29th Annual Meeting of the International Society for Magnetic Resonance in Medicine (ISMRM) (2021).

* contributed equally
† contributed equally