@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} } @inproceedings{EndtPirklVerdunetal.2021, author = {Endt, Sebastian and Pirkl, Carolin and Verdun, Claudio Mayrink and Menze, Bjoern H. and Menzel, Marion Irene}, title = {Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging}, booktitle = {17th International Symposium on Medical Information Processing and Analysis}, editor = {Romero, Eduardo and Costa, Eduardo Tavares and Brieva, Jorge and Rittner, Leticia and Linguraru, Marius George and Lepore, Natasha}, publisher = {SPIE}, address = {Bellingham}, isbn = {978-1-5106-5053-4}, doi = {https://doi.org/10.1117/12.2604737}, year = {2021}, language = {en} } @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{MayoCenciniPirkletal.2024, author = {Mayo, Perla and Cencini, Matteo and Pirkl, Carolin and Menzel, Marion Irene and Tosetti, Michela and Menze, Bjoern H. and Golbabaee, Mohammad}, title = {StoDIP: Efficient 3D MRF Image Reconstruction with Deep Image Priors and Stochastic Iterations}, booktitle = {Machine Learning in Medical Imaging: 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings, Part II}, editor = {XU, Xuanang and Cui, Zhiming and Rekik, Islem and Ouyang, Xi and Sun, Kaicong}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-73290-4}, doi = {https://doi.org/10.1007/978-3-031-73290-4_13}, pages = {128 -- 137}, year = {2024}, language = {en} } @inproceedings{MayoCenciniFataniaetal.2024, author = {Mayo, Perla and Cencini, Matteo and Fatania, Ketan and Pirkl, Carolin and Menzel, Marion Irene and Menze, Bjoern H. and Tosetti, Michela and Golbabaee, Mohammad}, title = {Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders}, booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI 2024): Conference Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-1333-8}, doi = {https://doi.org/10.1109/ISBI56570.2024.10635677}, year = {2024}, language = {en} }