Medical Engineering

M1.1a | Model-based reconstruction methods for CT perfusion imaging (Sebastian Bannasch, Georg Rose et al.)

M1.1b | Dynamic C-arm CT perfusion of the liver (Hana Haseljić, Georg Rose et al.)

M1.2 | Under-sampled MRI for percutaneous intervention (Mario Breitkopf, Oliver Speck et al.)

M1.3 | Use of prior knowledge in CT reconstruction (Suhita Ghosh, Domenico Iuso, Sebastian Stober, Georg Rose et al.)

M1.4 | Use of prior knowledge for interventional MRI (Soumick Chatterjee, Andreas Nürnberger, Oliver Speck et al.)

M1.5 | Volume-of-interest imaging in C-arm CT (Daniel Punzet, Georg Rose et al.)

M1.6 | Stent detection and enhancement (Negar Chabi, Samuel Manthey, Bernhard Preim et al.)

M1.7 | Model-based reconstruction MRI (Chompunuch Sarasaen, Oliver Speck et al.)

M1.8 | Augmented 4D flow (Franziska Gaidzik, Gábor Janiga et al.)

M1.9 | Current visualisation during radiofrequency ablation (RFA) with MR coils (Thomas Gerlach, Ralf Vick et al.)

M1.10 | Deep learning for interventional C-arm CT (Philipp Ernst, Andreas Nürnberger et al.)

M1.11 | C-arm imaging with few arbitrary projections (Fatima Saad, Georg Rose et al.)

Materials Science

M2.1 | Optimisation of novel vanadium based high temperature materials (Christopher Müller, Manja Krüger et al.)

M2.2 | Characterisation and simulation-based development of engineering materials (Rostyslav Nizinkovskyi, Manja Krüger et al.)

M2.3 | Evaluation of force contributions to the damage evolution and failure analysis of metallic arthroplasty components (Maria Herbster, Jessica Bertrand, Thorsten Halle et al.)

M2.4 | In-situ SEM methods to improve implant materials (Karsten Harnisch, Thorsten Halle et al.)

M2.5 | Preparation and characterisation of ceramic foams (Kathleen Dammler, Michael Scheffler et al.)

M2.6 | Preparation and characterisation of cellular metals (Alina Sutygina, Michael Scheffler et al.)

M2.7 | Mechanical simulations of fiber-reinforced plastics based on parameters of the injection molding process (Stefan Bergmann, Holm Altenbach et al.)

M2.8 | Analysis of curved photovoltaic panels with a novel shell theory and a global-local approach (Moharam Haghi Choobar, Holm Altenbach et al.)

M2.10 | Preparation and testing of thermoelectric materials (Christian Künzel, Franziska Scheffler)

Master Sub-Projects

Journal Articles

Chatterjee S, Yassin H, Dubost F, Nürnberger A, and Speck O (tba): Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification. Manuscript to be submitted to Nature Communications (in preparation): arXiv:2206.05148 (1-14). DOI: 10.48550/arXiv.2206.05148. URL: https://arxiv.org/abs/2206.05148

Chatterjee S, Das A, Mandal C, Mukhopadhyay B, Vipinraj M, Shukla A, Rao R N, Sarasaen C, Speck O, and Nürnberger A (2022): TorchEsegeta: Framework for interpretability and explainability of image-based Deep Learning models. Applied Sciences, 12(4): 1834 (1-20). DOI: 10.3390/app12041834. URL: https://www.mdpi.com/2076-3417/12/4/1834

Chatterjee S, Breitkopf M, Sarasaen C, Yassin H, Rose G, Nürnberger A, and Speck O (2022): ReconResNet: Regularised residual learning for MR image reconstruction of Undersampled Cartesian and Radial data. Computers in Biology and Medicine, 143: 105321 (1-17). DOI: 10.1016/j.compbiomed.2022.105321. URL: https://www.sciencedirect.com/science/article/abs/pii/S0010482522001135

Chatterjee S, Sciarra A, Dünnwald M, Tummala P, Agrawal S K, Jauhari A, Kalra A, Oeltze-Jafra S, Speck O, and Nürnberger A (2022): StRegA: Unsupervised anomaly detection in brain MRIs using a compact context-encoding variational autoencoder. Computers in Biology and Medicine: 106093 (1-15). DOI: 10.1016/j.compbiomed.2022.106093. URL: https://www.sciencedirect.com/science/article/abs/pii/S0010482522008010

Conference Papers

Chatterjee S, Tummala P, Speck O, and Nürnberger A (2022): Complex Network for Complex Problems: A comparative study of CNNs and Complex-valued CNNs. Paper presented at 5th IEEE International Conference on Image Processing, Applications and Systems (IEEE IPAS 2022), Genova, IT, Dec 5-7, 2022. p. tba. DOI: tba. URL: https://ipas.ieee.tn/

Chatterjee S, Breitkopf M, Sarasaen C, Yassin H, Rose G, Nürnberger A, and Speck O (2021): ReconResNet: Regularised residual learning for MR image reconstruction of undersampled cartesian and radial data. Paper presented at Medical Imaging with Deep Learning (MIDL) 2021, Lübeck, Germany, Jul 7-9, 2021. p. — (1-3; ID: 110). Oral presentation (online). DOI: —. URL:

Punzet D, Frysch R, Khosroshahi E, Beuing O, Speck O, and Rose G (2020): Epipolar-constrained optical flow triangulation for the interior problem in CBCT. Paper presented at Virtual 2020 IEEE Nuclear Science Symposium & Medical Imaging Conference – 27th International Symposium on Room-Temperature Semiconductor (IEEE NSS-MIC 2020), Boston, MA, USA, Oct 31-Nov 7, 2020. p. — (1-3; ID: 1620). Digital poster (ID: M-08-179). DOI: 10.1109/NSS/MIC42677.2020.9508107. URL: https://oa.mg/work/10.1109/nss/mic42677.2020.9508107

Further Contributions (Abstracts, Talks, Posters)

Chatterjee S, Sciarra A, Dünnwald M, Tummala P, Agrawal S K, Jauhari A, Kalra A, Oeltze-Jafra S, Speck O, and Nürnberger A (2022): StRegA: Unsupervised Anomaly Detection in Brain MRIs using Compact Context-encoding Variational Autoencoder. Abstract presented at 2022 Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM-ESMRMB 2022), London, UK, May 7-12, 2022. p. — (1-3; ID: 5531). Oral presentation. URL: https://www.researchgate.net/publication/358357400_StRegA_Unsupervised_Anomaly_Detection_in_Brain_MRIs_using_Compact_Context-encoding_Variational_Autoencoder

Chatterjee S, Yassin H, Dubost F, Nürnberger A, and Speck O (2022): Learning to segment brain tumours using an explainable classifier. Abstract presented at 2022 Joint Annual Meeting ISMRM-ESMRMB & ISMRT 31st Annual Meeting (ISMRM-ESMRMB 2022), London, UK, May 7-12, 2022. p. — (1-3; ID: 5592). Oral presentation. URL: https://www.researchgate.net/publication/358357555_Learning_to_segment_brain_tumours_using_an_explainable_classifier

Hubmann M J, Gerlach T, Pannicke E, Hensen B, Wacker F, Speck O, and Vick R (2021): Feasibility study of MRI-guided IRE. Abstract presented at 5th Conference on Image-Guided Interventions (IGIC 2021), Magdeburg, Germany, Oct 13-14, 2021. p. 21-22. Oral presentation. URL: http://www.igic.de/upload/2021/IGIC_2021_Abstracts.pdf

Chatterjee S, Das A, Mandal C, Mukhopadhyay B, Vipinraj M, Shukla A, Speck O, and Nürnberger A (2021): Interpretability techniques for deep learning based segmentation models. Abstract presented at The 29th Annual Meeting and Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM 2021), — (online), May 15-20, 2021. p. — (1-4; ID: 2400). Oral presentation (online). URL: https://www.researchgate.net/publication/349589153_Interpretability_Techniques_for_Deep_Learning_based_Segmentation_Models

Kowal R, Prier M, Pannicke E, Gerlach T, Speck O, and Rose G (2020): Specific absorption rate in a dedicated birdcage coil for neonatal MRI. Abstract presented at ESMRMB 2020 Online - 37th Annual Scientific Meeting, Sep 30-Oct 2, 2020. p. S40. Oral presentation (online). DOI: 10.1007/s10334-020-00874-0. URL: https://link.springer.com/article/10.1007/s10334-020-00874-0

Kowal R, Prier M, Pannicke E, Vick R, and Speck O (2020): From PCB to Simulation: a workflow instruction for designing birdcage models from production data. Abstract presented at ESMRMB 2020 Online - 137th Annual Scientific Meeting, — (online), Sep 30-Oct 2, 2020. p. S163-64. Digital poster. DOI: 10.1007/s10334-020-00876-y. URL: https://link.springer.com/article/10.1007/s10334-020-00876-y