@inproceedings{EisenmannReinkeWeruetal.2023, author = {Eisenmann, Matthias and Reinke, Annika and Weru, Vivienn and Tizabi, Minu Dietlinde and Isensee, Fabian and Adler, Tim J. and Ali, Sharib and Andrearczyk, Vincent and Aubreville, Marc and Baid, Ujjwal and Bakas, Spyridon and Balu, Niranjan and Bano, Sophia and Bernal, Jorge and Bodenstedt, Sebastian and Casella, Alessandro and Cheplygina, Veronika and Daum, Marie and De Bruijne, Marleen and Depeursinge, Adrien and Dorent, Reuben and Egger, Jan and Ellis, David G. and Engelhardt, Sandy and Ganz, Melanie and Ghatwary, Noha M. and Girard, Gabriel and Godau, Patrick and Gupta, Anubha and Hansen, Lasse and Harada, Kanako and Heinrich, Mattias and Heller, Nicholas and Hering, Alessa and Huaulm{\´e}, Arnoud and Jannin, Pierre and Kavur, A. Emre and Kodym, Oldrich and Kozubek, Michal and Li, Jianning and Li, Hongwei and Ma, Jun and Mart{\´i}n-Isla, Carlos and Menze, Bjoern H. and Noble, Alison and Oreiller, Valentin and Padoy, Nicolas and Pati, Sarthak and Payette, Kelly and R{\"a}dsch, Tim and Rafael-Pati{\~n}o, Jonathan and Bawa, Vivek Singh and Speidel, Stefanie and Sudre, Carole H. and Van Wijnen, Kimberlin M. H. and Wagner, M. and Wei, D. and Yamlahi, Amine and Yap, Moi Hoon and Yuan, C. and Zenk, Maximilian and Zia, A. and Zimmerer, David and Aydogan, Dogu Baran and Bhattarai, B. and Bloch, Louise and Br{\"u}ngel, Raphael and Cho, J. and Choi, C. and Dou, Q. and Ezhov, Ivan and Friedrich, Christoph M. and Fuller, C. and Gaire, Rebati Raman and Galdran, Adrian and Garc{\´i}a-Faura, {\´A}lvaro and Grammatikopoulou, Maria and Hong, S. and Jahanifar, Mostafa and Jang, I. and Kadkhodamohammadi, Abdolrahim and Kang, I. and Kofler, Florian and Kondo, Satoshi and Kuijf, Hugo Jaco and Li, M. and Luu, M. and Martinčič, Tomaz and Morais, P. and Naser, M. A. and Oliveira, B. and Owen, D. and Pang, S. and Park, Jinah and Park, S. and Płotka, S. and Puybareau, {\´E}lodie and Rajpoot, Nasir M. and Ryu, K. and Saeed, N. and Shephard, Adam and Shi, P. and Štepec, Dejan and Subedi, Ronast and Tochon, Guillaume and Torres, Helena R. and Urien, H{\´e}l{\`e}ne and Vila{\c{c}}a, Jo{\~a}o L. and Wahid, Kareem A. and Wang, H. and Wang, J. and Wang, L. and Wang, Xiyue and Wiestler, Benedikt and Wodzinski, Marek and Xia, F. and Xie, J. and Xiong, Z. and Yang, Sen and Yang, Y. and Zhao, Z. and Maier-Hein, Klaus H. and J{\"a}ger, Paul F. and Kopp-Schneider, Annette and Maier-Hein, Lena}, title = {Why is the Winner the Best?}, booktitle = {Proceedings: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, publisher = {IEEE}, address = {Los Alamitos}, isbn = {979-8-3503-0129-8}, issn = {2575-7075}, doi = {https://doi.org/10.1109/CVPR52729.2023.01911}, pages = {19955 -- 19967}, year = {2023}, language = {en} } @article{PirklCenciniKurzawskietal.2022, author = {Pirkl, Carolin and Cencini, Matteo and Kurzawski, Jan W. and Waldmannstetter, Diana and Li, Hongwei and Menzel, Marion Irene}, title = {Learning residual motion correction for fast and robust 3D multiparametric MRI}, volume = {2022}, pages = {102387}, journal = {Medical Image Analysis}, number = {77}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1361-8423}, doi = {https://doi.org/10.1016/j.media.2022.102387}, year = {2022}, abstract = {Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable.}, language = {en} } @inproceedings{PirklCenciniKurzawskietal.2021, author = {Pirkl, Carolin and Cencini, Matteo and Kurzawski, Jan W. and Waldmannstetter, Diana and Li, Hongwei and Sekuboyina, Anjany and Endt, Sebastian and Peretti, Luca and Donatelli, Graziella and Pasquariello, Rosa and Costagli, Mauro and Buonincontri, Guido and Tosetti, Michela and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Residual learning for 3D motion corrected quantitative MRI}, booktitle = {Medical Imaging with Deep Learning MIDL 2021}, subtitle = {Robust clinical T1, T2 and proton density mapping}, publisher = {OpenReview}, url = {https://openreview.net/forum?id=hxgQM71AuRA}, year = {2021}, language = {en} } @inproceedings{PirklCenciniKurzawskietal.2021, author = {Pirkl, Carolin and Cencini, Matteo and Kurzawski, Jan W. and Waldmannstetter, Diana and Li, Hongwei and Sekuboyina, Anjany and Endt, Sebastian and Peretti, Luca and Donatelli, Graziella and Pasquariello, Rosa and Costagli, Mauro and Buonincontri, Guido and Tosetti, Michela and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping}, booktitle = {Proceedings of Machine Learning Research}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v143/pirkl21a.html}, pages = {618 -- 632}, year = {2021}, language = {en} }