@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{LiuGomezSolanaetal.2020, author = {Liu, Xin and G{\´o}mez, Pedro A. and Solana, Ana Beatriz and Wiesinger, Florian and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Silent 3D MR sequence for quantitative and multicontrast T1 and proton density imaging}, volume = {65}, pages = {185010}, journal = {Physics in Medicine \& Biology}, number = {18}, publisher = {IOP Publishing}, address = {Bristol}, issn = {1361-6560}, doi = {https://doi.org/10.1088/1361-6560/aba5e8}, year = {2020}, abstract = {This study aims to develop a silent, fast and 3D method for T1 and proton density (PD) mapping, while generating time series of T1-weighted (T1w) images with bias-field correction. Undersampled T1w images at different effective inversion times (TIs) were acquired using the inversion recovery prepared RUFIS sequence with an interleaved k-space trajectory. Unaliased images were reconstructed by constraining the signal evolution to a temporal subspace which was learned from the signal model. Parameter maps were obtained by fitting the data to the signal model, and bias-field correction was conducted on T1w images. Accuracy and repeatability of the method was accessed in repeated experiments with phantom and volunteers. For the phantom study, T1 values obtained by the proposed method were highly consistent with values from the gold standard method, R2 = 0.9976. Coefficients of variation (CVs) ranged from 0.09\% to 0.83\%. For the volunteer study, T1 values from gray and white matter regions were consistent with literature values, and peaks of gray and white matter can be clearly delineated on whole-brain T1 histograms. CVs ranged from 0.01\% to 2.30\%. The acoustic noise measured at the scanner isocenter was 2.6 dBA higher compared to the in-bore background. Rapid and with low acoustic noise, the proposed method is shown to produce accurate T1 and PD maps with high repeatability by reconstructing sparsely sampled T1w images at different TIs using temporal subspace. Our approach can greatly enhance patient comfort during examination and therefore increase the acceptance of the procedure.}, language = {en} } @article{CoelloHafalirNoeskeetal.2019, author = {Coello, Eduardo and Hafalir, Fatih S. and Noeske, Ralph and Menzel, Marion Irene and Haase, Axel and Menze, Bjoern H. and Schulte, Rolf F.}, title = {Overdiscrete echo-planar spectroscopic imaging with correlated higher-order phase correction}, volume = {84}, journal = {Magnetic Resonance in Medicine}, number = {1}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.28105}, pages = {11 -- 24}, year = {2019}, language = {en} } @article{GomezMolinaRomeroBuonincontrietal.2019, author = {G{\´o}mez, Pedro A. and Molina-Romero, Miguel and Buonincontri, Guido and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging}, volume = {9}, pages = {8468}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-019-44832-w}, year = {2019}, abstract = {Magnetic resonance imaging (MRI) has evolved into an outstandingly versatile diagnostic modality, as it has the ability to non-invasively produce detailed information on a tissue's structure and function. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as quantitative parametric maps, which offer an absolute quantification of underlying biophysical effects, such as relaxation times or flow. Here, we demonstrate how to acquire and reconstruct data in a transient-state with a dual purpose: 1 - to generate contrast-weighted images that can be adjusted to emphasise clinically relevant image biomarkers; exemplified with signal modulation according to flow to obtain angiography information, and 2 - to simultaneously infer multiple quantitative parameters with a single, highly accelerated acquisition. This is achieved by introducing three novel elements: a model that accounts for flowing blood, a method for sequence design using smooth flip angle excitation patterns that incorporates both parameter encoding and signal contrast, and the reconstruction of temporally resolved contrast-weighted images. From these images we simultaneously obtain angiography projections and multiple quantitative maps. By doing so, we increase the amount of clinically relevant data without adding measurement time, creating new dimensions for biomarker exploration and adding value to MR examinations for patients and clinicians alike.}, language = {en} } @article{GolbabaeeBuonincontriPirkletal.2020, author = {Golbabaee, Mohammad and Buonincontri, Guido and Pirkl, Carolin and Menzel, Marion Irene and Menze, Bjoern H. and Davies, Mike E. and G{\´o}mez, Pedro A.}, title = {Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks}, volume = {2021}, pages = {101945}, journal = {Medical Image Analysis}, number = {69}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1361-8415}, doi = {https://doi.org/10.1016/j.media.2020.101945}, year = {2020}, 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{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{PirklGomezLippetal.2020, author = {Pirkl, Carolin and G{\´o}mez, Pedro A. and Lipp, Ilona and Buonincontri, Guido and Molina-Romero, Miguel and Sekuboyina, Anjany and Waldmannstetter, Diana and Dannenberg, Jonathan and Endt, Sebastian and Merola, Alberto and Whittaker, Joseph R. and Tomassini, Valentina and Tosetti, Michela and Jones, Derek K. and Menze, Bjoern H. and Menzel, Marion Irene}, title = {Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting}, booktitle = {Proceedings of Machine Learning Research}, number = {121}, publisher = {PMLR}, address = {[s. l.]}, issn = {2640-3498}, url = {https://proceedings.mlr.press/v121/pirk20a.html}, pages = {639 -- 654}, year = {2020}, language = {en} } @article{MolinaRomeroGomezSperletal.2018, author = {Molina-Romero, Miguel and G{\´o}mez, Pedro A. and Sperl, Jonathan I. and Czisch, Michael and S{\"a}mann, Philipp G. and Jones, Derek K. and Menzel, Marion Irene and Menze, Bjoern H.}, title = {A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization}, volume = {80}, journal = {Magnetic Resonance in Medicine}, number = {5}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.27181}, pages = {2155 -- 2172}, year = {2018}, abstract = {Purpose The compartmental nature of brain tissue microstructure is typically studied by diffusion MRI, MR relaxometry or their correlation. Diffusion MRI relies on signal representations or biophysical models, while MR relaxometry and correlation studies are based on regularized inverse Laplace transforms (ILTs). Here we introduce a general framework for characterizing microstructure that does not depend on diffusion modeling and replaces ill-posed ILTs with blind source separation (BSS). This framework yields proton density, relaxation times, volume fractions, and signal disentanglement, allowing for separation of the free-water component. Theory and Methods Diffusion experiments repeated for several different echo times, contain entangled diffusion and relaxation compartmental information. These can be disentangled by BSS using a physically constrained nonnegative matrix factorization. Results Computer simulations, phantom studies, together with repeatability and reproducibility experiments demonstrated that BSS is capable of estimating proton density, compartmental volume fractions and transversal relaxations. In vivo results proved its potential to correct for free-water contamination and to estimate tissue parameters. Conclusion Formulation of the diffusion-relaxation dependence as a BSS problem introduces a new framework for studying microstructure compartmentalization, and a novel tool for free-water elimination.}, language = {en} } @inproceedings{MolinaRomeroWiestlerGomezetal.2018, author = {Molina-Romero, Miguel and Wiestler, Benedikt and G{\´o}mez, Pedro A. and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases}, booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2018}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-00931-1}, doi = {https://doi.org/10.1007/978-3-030-00931-1_12}, pages = {98 -- 106}, year = {2018}, language = {en} } @unpublished{KaushikBylundCozzinietal.2022, author = {Kaushik, Sandeep and Bylund, Mikael and Cozzini, Cristina and Shanbhag, Dattesh and Petit, Steven F. and Wyatt, Jonathan J. and Menzel, Marion Irene and Pirkl, Carolin and Mehta, Bhairav and Chauhan, Vikas and Chandrasekharan, Kesavadas and Jonsson, Joakim and Nyholm, Tufve and Wiesinger, Florian and Menze, Bjoern H.}, title = {Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2203.16288}, year = {2022}, language = {en} } @article{KaushikBylundCozzinietal.2023, author = {Kaushik, Sandeep and Bylund, Mikael and Cozzini, Cristina and Shanbhag, Dattesh and Petit, Steven F. and Wyatt, Jonathan J. and Menzel, Marion Irene and Pirkl, Carolin and Mehta, Bhairav and Chauhan, Vikas and Chandrasekharan, Kesavadas and Jonsson, Joakim and Nyholm, Tufve and Wiesinger, Florian and Menze, Bjoern H.}, title = {Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network}, volume = {68}, pages = {195003}, journal = {Physics in Medicine \& Biology}, number = {19}, publisher = {IOP Publishing}, address = {Bristol}, issn = {0031-9155}, doi = {https://doi.org/10.1088/1361-6560/acefa3}, year = {2023}, language = {en} } @inproceedings{GomezSperlSprengeretal.2015, author = {G{\´o}mez, Pedro A. and Sperl, Jonathan I. and Sprenger, Tim and Metzler-Baddeley, Claudia and Jones, Derek K. and Saemann, Philipp and Czisch, Michael and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Joint Reconstruction of Multi-Contrast MRI for Multiple Sclerosis Lesion Segmentation}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2015, Algorithmen - Systeme - Anwendungen, Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2015 in L{\"u}beck}, editor = {Handels, Heinz and Deserno, Thomas Martin and Meinzer, Hans-Peter and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Berlin}, isbn = {978-3-662-46224-9}, doi = {https://doi.org/10.1007/978-3-662-46224-9_28}, pages = {155 -- 160}, year = {2015}, language = {en} } @unpublished{GomezMolinaRomeroBuonincontrietal.2019, author = {G{\´o}mez, Pedro A. and Molina-Romero, Miguel and Buonincontri, Guido and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1901.07800}, year = {2019}, language = {en} } @unpublished{GolbabaeeBuonincontriPirkletal.2020, author = {Golbabaee, Mohammad and Buonincontri, Guido and Pirkl, Carolin and Menzel, Marion Irene and Menze, Bjoern H. and Davies, Mike E. and G{\´o}mez, Pedro A.}, title = {Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2001.08746}, year = {2020}, 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{UlasGomezKrahmeretal.2017, author = {Ulas, Cagdas and G{\´o}mez, Pedro A. and Krahmer, Felix and Sperl, Jonathan I. and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Robust Reconstruction of Accelerated Perfusion MRI Using Local and Nonlocal Constraints}, booktitle = {Reconstruction, Segmentation, and Analysis of Medical Images, First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers}, editor = {Zuluaga, Maria A. and Bhatia, Kanwal and Kainz, Bernhard and Moghari, Mehdi H. and Pace, Danielle F.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-52280-7}, doi = {https://doi.org/10.1007/978-3-319-52280-7_4}, pages = {37 -- 47}, year = {2017}, language = {en} } @inproceedings{GomezUlasSperletal.2016, author = {G{\´o}mez, Pedro A. and Ulas, Cagdas and Sperl, Jonathan I. and Sprenger, Tim and Molina-Romero, Miguel and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compressed Sensing}, booktitle = {Patch-Based Techniques in Medical Imaging, First International Workshop, Patch-MI 2015, Held in Conjunction with MICCAI 2015 Munich, Germany, October 9, 2015 Revised Selected Papers}, editor = {Wu, Guorong and Coup{\´e}, Pierrick and Zhan, Yiqiang and Munsell, Brent and Rueckert, Daniel}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-28194-0}, doi = {https://doi.org/10.1007/978-3-319-28194-0_14}, pages = {112 -- 119}, year = {2016}, language = {en} } @unpublished{PirklGomezLippetal.2020, author = {Pirkl, Carolin and G{\´o}mez, Pedro A. and Lipp, Ilona and Buonincontri, Guido and Molina-Romero, Miguel and Sekuboyina, Anjany and Waldmannstetter, Diana and Dannenberg, Jonathan and Endt, Sebastian and Merola, Alberto and Whittaker, Joseph R. and Tomassini, Valentina and Tosetti, Michela and Jones, Derek K. and Menze, Bjoern H. and Menzel, Marion Irene}, title = {Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2005.02020}, year = {2020}, 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} } @inproceedings{GomezMolinaRomeroUlasetal.2016, author = {G{\´o}mez, Pedro A. and Molina-Romero, Miguel and Ulas, Cagdas and Bounincontri, Guido and Sperl, Jonathan I. and Jones, Derek K. and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Simultaneous Parameter Mapping, Modality Synthesis, and Anatomical Labeling of the Brain with MR Fingerprinting}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III}, editor = {Ourselin, Sebastien and Joskowicz, Leo and Sabuncu, Mert R. and Unal, Gozde and Wells, William M.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-46726-9}, doi = {https://doi.org/10.1007/978-3-319-46726-9_67}, pages = {579 -- 586}, year = {2016}, 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} }