@article{PirklNunezGonzalezKofleretal.2021, author = {Pirkl, Carolin and Nunez-Gonzalez, Laura and Kofler, Florian and Endt, Sebastian and Grundl, Lioba and Golbabaee, Mohammad and G{\´o}mez, Pedro A. and Cencini, Matteo and Buonincontri, Guido and Schulte, Rolf F. and Smits, Marion and Wiestler, Benedikt and Menze, Bjoern H. and Menzel, Marion Irene and Hernandez-Tamames, Juan A.}, title = {Accelerated 3D whole-brain T1, T2, and proton density mapping}, volume = {63}, journal = {Neuroradiology}, subtitle = {feasibility for clinical glioma MR imaging}, number = {11}, publisher = {Springer}, address = {Berlin}, doi = {https://doi.org/10.1007/s00234-021-02703-0}, pages = {1831 -- 1851}, year = {2021}, abstract = {Purpose: Advanced MRI-based biomarkers offer comprehensive and quantitative information for the evaluation and characterization of brain tumors. In this study, we report initial clinical experience in routine glioma imaging with a novel, fully 3D multiparametric quantitative transient-state imaging (QTI) method for tissue characterization based on T1 and T2 values. Methods: To demonstrate the viability of the proposed 3D QTI technique, nine glioma patients (grade II-IV), with a variety of disease states and treatment histories, were included in this study. First, we investigated the feasibility of 3D QTI (6:25 min scan time) for its use in clinical routine imaging, focusing on image reconstruction, parameter estimation, and contrast-weighted image synthesis. Second, for an initial assessment of 3D QTI-based quantitative MR biomarkers, we performed a ROI-based analysis to characterize T1 and T2 components in tumor and peritumoral tissue. Results: The 3D acquisition combined with a compressed sensing reconstruction and neural network-based parameter inference produced parametric maps with high isotropic resolution (1.125 × 1.125 × 1.125 mm3 voxel size) and whole-brain coverage (22.5 × 22.5 × 22.5 cm3 FOV), enabling the synthesis of clinically relevant T1-weighted, T2-weighted, and FLAIR contrasts without any extra scan time. Our study revealed increased T1 and T2 values in tumor and peritumoral regions compared to contralateral white matter, good agreement with healthy volunteer data, and high inter-subject consistency. Conclusion: 3D QTI demonstrated comprehensive tissue assessment of tumor substructures captured in T1 and T2 parameters. Aiming for fast acquisition of quantitative MR biomarkers, 3D QTI has potential to improve disease characterization in brain tumor patients under tight clinical time-constraints.}, language = {en} } @inproceedings{BertramDonovanTecillaetal.2021, author = {Bertram, Christof and Donovan, Taryn and Tecilla, Marco and Bartenschlager, Florian and Fragoso-Garcia, Marco and Wilm, Frauke and Marzahl, Christian and Breininger, Katharina and Maier, Andreas and Klopfleisch, Robert and Aubreville, Marc}, title = {Dataset on bi- and multi-nucleated tumor cells in canine cutaneous mast cell tumors}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-33197-9}, issn = {1431-472X}, doi = {https://doi.org/10.1007/978-3-658-33198-6_33}, pages = {134 -- 139}, year = {2021}, language = {en} } @inproceedings{SchroeterRosenkranzEscalanteBetal.2020, author = {Schr{\"o}ter, H. and Rosenkranz, Tobias and Escalante-B, A. N. and Aubreville, Marc and Maier, Andreas}, title = {CLCNET: deep learning-based noise reduction for hearing aids using complex linear coding}, booktitle = {ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5090-6631-5}, doi = {https://doi.org/10.1109/ICASSP40776.2020.9053563}, pages = {6949 -- 6953}, year = {2020}, language = {en} } @inproceedings{MarzahlBertramWilmetal.2021, author = {Marzahl, Christian and Bertram, Christof and Wilm, Frauke and Voigt, J{\"o}rn and Barton, Ann K. and Klopfleisch, Robert and Breininger, Katharina and Maier, Andreas and Aubreville, Marc}, title = {Cell detection for asthma on partially annotated whole slide images}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2021: Proceedings, German Workshop on Medical Image Computing, Regensburg, March 7-9, 2021}, subtitle = {learning to be EXACT}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-33197-9}, issn = {1431-472X}, doi = {https://doi.org/10.1007/978-3-658-33198-6_36}, pages = {147 -- 152}, year = {2021}, language = {en} } @article{SchwarzWach2022, author = {Schwarz, Jan Oliver and Wach, Bernhard}, title = {The usage of cultural products in design thinking: An assessment of an underestimated approach}, volume = {25}, journal = {The Design Journal}, number = {1}, publisher = {Taylor \& Francis}, address = {London}, issn = {1460-6925}, doi = {https://doi.org/10.1080/14606925.2021.2005879}, pages = {4 -- 24}, year = {2022}, language = {en} } @inproceedings{deCandidoKollerGallitzetal.2020, author = {de Candido, Oliver and Koller, Michael and Gallitz, Oliver and Melz, Ron and Botsch, Michael and Utschick, Wolfgang}, title = {Towards feature validation in time to lane change classification using deep neural networks}, booktitle = {2020 IEEE 23rd International Conference on Intelligent Transportation Systems (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-4149-7}, doi = {https://doi.org/10.1109/ITSC45102.2020.9294555}, year = {2020}, language = {en} } @book{BotschUtschick2020, author = {Botsch, Michael and Utschick, Wolfgang}, title = {Fahrzeugsicherheit und automatisiertes Fahren}, subtitle = {Methoden der Signalverarbeitung und des maschinellen Lernens}, publisher = {Hanser}, address = {M{\"u}nchen}, isbn = {978-3-446-46804-7}, doi = {https://doi.org/10.3139/9783446468047}, pages = {448}, year = {2020}, language = {de} } @inproceedings{GallitzdeCandidoBotschetal.2019, author = {Gallitz, Oliver and de Candido, Oliver and Botsch, Michael and Utschick, Wolfgang}, title = {Interpretable feature generation using deep neural networks and its application to lane change detection}, booktitle = {2019 IEEE Intelligent Transportation Systems Conference (ITSC)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-7024-8}, doi = {https://doi.org/10.1109/ITSC.2019.8917524}, pages = {3405 -- 3411}, year = {2019}, language = {en} } @inproceedings{SanchezMoralesBotschHuberetal.2019, author = {S{\´a}nchez Morales, Eduardo and Botsch, Michael and Huber, Bertold and Garc{\´i}a Higuera, Andr{\´e}s}, title = {High precision indoor navigation for autonomous vehicles}, booktitle = {2019 International Conference on Indoor Positioning and Indoor Navigation}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-1788-1}, doi = {https://doi.org/10.1109/IPIN.2019.8911780}, year = {2019}, language = {en} } @inproceedings{ChaulwarAlHashimiBotschetal.2019, author = {Chaulwar, Amit and Al-Hashimi, Hussein and Botsch, Michael and Utschick, Wolfgang}, title = {Efficient hybrid machine learning algorithm for trajectory planning in critical traffic-scenarios}, booktitle = {The 4th International Conference on Intelligent Transportation Engineering, ICITE 2019}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-4553-2}, doi = {https://doi.org/10.1109/ICITE.2019.8880266}, pages = {196 -- 202}, year = {2019}, language = {en} }