TY - CHAP A1 - Eisenmann, Matthias A1 - Reinke, Annika A1 - Weru, Vivienn A1 - Tizabi, Minu Dietlinde A1 - Isensee, Fabian A1 - Adler, Tim J. A1 - Ali, Sharib A1 - Andrearczyk, Vincent A1 - Aubreville, Marc A1 - Baid, Ujjwal A1 - Bakas, Spyridon A1 - Balu, Niranjan A1 - Bano, Sophia A1 - Bernal, Jorge A1 - Bodenstedt, Sebastian A1 - Casella, Alessandro A1 - Cheplygina, Veronika A1 - Daum, Marie A1 - De Bruijne, Marleen A1 - Depeursinge, Adrien A1 - Dorent, Reuben A1 - Egger, Jan A1 - Ellis, David G. A1 - Engelhardt, Sandy A1 - Ganz, Melanie A1 - Ghatwary, Noha M. A1 - Girard, Gabriel A1 - Godau, Patrick A1 - Gupta, Anubha A1 - Hansen, Lasse A1 - Harada, Kanako A1 - Heinrich, Mattias A1 - Heller, Nicholas A1 - Hering, Alessa A1 - Huaulmé, Arnoud A1 - Jannin, Pierre A1 - Kavur, A. Emre A1 - Kodym, Oldrich A1 - Kozubek, Michal A1 - Li, Jianning A1 - Li, Hongwei A1 - Ma, Jun A1 - Martín-Isla, Carlos A1 - Menze, Bjoern H. A1 - Noble, Alison A1 - Oreiller, Valentin A1 - Padoy, Nicolas A1 - Pati, Sarthak A1 - Payette, Kelly A1 - Rädsch, Tim A1 - Rafael-Patiño, Jonathan A1 - Bawa, Vivek Singh A1 - Speidel, Stefanie A1 - Sudre, Carole H. A1 - Van Wijnen, Kimberlin M. H. A1 - Wagner, M. A1 - Wei, D. A1 - Yamlahi, Amine A1 - Yap, Moi Hoon A1 - Yuan, C. A1 - Zenk, Maximilian A1 - Zia, A. A1 - Zimmerer, David A1 - Aydogan, Dogu Baran A1 - Bhattarai, B. A1 - Bloch, Louise A1 - Brüngel, Raphael A1 - Cho, J. A1 - Choi, C. A1 - Dou, Q. A1 - Ezhov, Ivan A1 - Friedrich, Christoph M. A1 - Fuller, C. A1 - Gaire, Rebati Raman A1 - Galdran, Adrian A1 - García-Faura, Álvaro A1 - Grammatikopoulou, Maria A1 - Hong, S. A1 - Jahanifar, Mostafa A1 - Jang, I. A1 - Kadkhodamohammadi, Abdolrahim A1 - Kang, I. A1 - Kofler, Florian A1 - Kondo, Satoshi A1 - Kuijf, Hugo Jaco A1 - Li, M. A1 - Luu, M. A1 - Martinčič, Tomaz A1 - Morais, P. A1 - Naser, M. A. A1 - Oliveira, B. A1 - Owen, D. A1 - Pang, S. A1 - Park, Jinah A1 - Park, S. A1 - Płotka, S. A1 - Puybareau, Élodie A1 - Rajpoot, Nasir M. A1 - Ryu, K. A1 - Saeed, N. A1 - Shephard, Adam A1 - Shi, P. A1 - Štepec, Dejan A1 - Subedi, Ronast A1 - Tochon, Guillaume A1 - Torres, Helena R. A1 - Urien, Hélène A1 - Vilaça, João L. A1 - Wahid, Kareem A. A1 - Wang, H. A1 - Wang, J. A1 - Wang, L. A1 - Wang, Xiyue A1 - Wiestler, Benedikt A1 - Wodzinski, Marek A1 - Xia, F. A1 - Xie, J. A1 - Xiong, Z. A1 - Yang, Sen A1 - Yang, Y. A1 - Zhao, Z. A1 - Maier-Hein, Klaus H. A1 - Jäger, Paul F. A1 - Kopp-Schneider, Annette A1 - Maier-Hein, Lena T1 - Why is the Winner the Best? T2 - Proceedings: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition UR - https://doi.org/10.1109/CVPR52729.2023.01911 KW - Medical and biological vision KW - cell microscopy Y1 - 2023 UR - https://doi.org/10.1109/CVPR52729.2023.01911 SN - 979-8-3503-0129-8 SN - 2575-7075 SP - 19955 EP - 19967 PB - IEEE CY - Los Alamitos ER - TY - CHAP A1 - Endt, Sebastian A1 - Pirkl, Carolin A1 - Verdun, Claudio Mayrink A1 - Menze, Bjoern H. A1 - Menzel, Marion Irene ED - Romero, Eduardo ED - Costa, Eduardo Tavares ED - Brieva, Jorge ED - Rittner, Leticia ED - Linguraru, Marius George ED - Lepore, Natasha T1 - Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging T2 - 17th International Symposium on Medical Information Processing and Analysis UR - https://doi.org/10.1117/12.2604737 Y1 - 2021 UR - https://doi.org/10.1117/12.2604737 SN - 978-1-5106-5053-4 SN - 978-1-5106-5052-7 PB - SPIE CY - Bellingham ER - TY - JOUR A1 - Liu, Xin A1 - Gómez, Pedro A. A1 - Solana, Ana Beatriz A1 - Wiesinger, Florian A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Silent 3D MR sequence for quantitative and multicontrast T1 and proton density imaging JF - Physics in Medicine & Biology N2 - 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. UR - https://doi.org/10.1088/1361-6560/aba5e8 KW - T1 mapping KW - proton density KW - silent MRI KW - inversion recovery KW - temporal subspace Y1 - 2020 UR - https://doi.org/10.1088/1361-6560/aba5e8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-18793 SN - 1361-6560 SN - 0031-9155 VL - 65 IS - 18 PB - IOP Publishing CY - Bristol ER - TY - JOUR A1 - Coello, Eduardo A1 - Hafalir, Fatih S. A1 - Noeske, Ralph A1 - Menzel, Marion Irene A1 - Haase, Axel A1 - Menze, Bjoern H. A1 - Schulte, Rolf F. T1 - Overdiscrete echo-planar spectroscopic imaging with correlated higher-order phase correction JF - Magnetic Resonance in Medicine UR - https://doi.org/10.1002/mrm.28105 Y1 - 2019 UR - https://doi.org/10.1002/mrm.28105 SN - 1522-2594 VL - 84 IS - 1 SP - 11 EP - 24 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Gómez, Pedro A. A1 - Molina-Romero, Miguel A1 - Buonincontri, Guido A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging JF - Scientific Reports N2 - 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. UR - https://doi.org/10.1038/s41598-019-44832-w Y1 - 2019 UR - https://doi.org/10.1038/s41598-019-44832-w UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-18854 SN - 2045-2322 VL - 9 PB - Springer Nature CY - London ER - TY - JOUR A1 - Golbabaee, Mohammad A1 - Buonincontri, Guido A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. A1 - Davies, Mike E. A1 - Gómez, Pedro A. T1 - Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks JF - Medical Image Analysis UR - https://doi.org/10.1016/j.media.2020.101945 KW - magnetic resonance fingerprinting KW - compressed sensing KW - convex model-based reconstruction KW - residual network KW - encoder-decoder network Y1 - 2020 UR - https://doi.org/10.1016/j.media.2020.101945 SN - 1361-8415 SN - 1361-8423 VL - 2021 IS - 69 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Pirkl, Carolin A1 - Nunez-Gonzalez, Laura A1 - Kofler, Florian A1 - Endt, Sebastian A1 - Grundl, Lioba A1 - Golbabaee, Mohammad A1 - Gómez, Pedro A. A1 - Cencini, Matteo A1 - Buonincontri, Guido A1 - Schulte, Rolf F. A1 - Smits, Marion A1 - Wiestler, Benedikt A1 - Menze, Bjoern H. A1 - Menzel, Marion Irene A1 - Hernandez-Tamames, Juan A. T1 - Accelerated 3D whole-brain T1, T2, and proton density mapping BT - feasibility for clinical glioma MR imaging JF - Neuroradiology N2 - 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. UR - https://doi.org/10.1007/s00234-021-02703-0 KW - MRI KW - Image-based biomarkers KW - Multiparametric imaging KW - Glioma imaging KW - Neural networks Y1 - 2021 UR - https://doi.org/10.1007/s00234-021-02703-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13885 VL - 63 IS - 11 SP - 1831 EP - 1851 PB - Springer CY - Berlin ER - TY - CHAP A1 - Pirkl, Carolin A1 - Cencini, Matteo A1 - Kurzawski, Jan W. A1 - Waldmannstetter, Diana A1 - Li, Hongwei A1 - Sekuboyina, Anjany A1 - Endt, Sebastian A1 - Peretti, Luca A1 - Donatelli, Graziella A1 - Pasquariello, Rosa A1 - Costagli, Mauro A1 - Buonincontri, Guido A1 - Tosetti, Michela A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Residual learning for 3D motion corrected quantitative MRI BT - Robust clinical T1, T2 and proton density mapping T2 - Medical Imaging with Deep Learning MIDL 2021 KW - 3D multiparametric MRI KW - motion correction KW - deep learning KW - residual learning KW - multiscale CNN Y1 - 2021 UR - https://openreview.net/forum?id=hxgQM71AuRA PB - OpenReview ER - TY - CHAP A1 - Pirkl, Carolin A1 - Gómez, Pedro A. A1 - Lipp, Ilona A1 - Buonincontri, Guido A1 - Molina-Romero, Miguel A1 - Sekuboyina, Anjany A1 - Waldmannstetter, Diana A1 - Dannenberg, Jonathan A1 - Endt, Sebastian A1 - Merola, Alberto A1 - Whittaker, Joseph R. A1 - Tomassini, Valentina A1 - Tosetti, Michela A1 - Jones, Derek K. A1 - Menze, Bjoern H. A1 - Menzel, Marion Irene T1 - Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting T2 - Proceedings of Machine Learning Research KW - Magnetic Resonance Fingerprinting KW - Convolutional Neural Network KW - Image Reconstruction KW - Diffusion Tensor KW - Multiple Sclerosis Y1 - 2020 UR - https://proceedings.mlr.press/v121/pirk20a.html SN - 2640-3498 IS - 121 SP - 639 EP - 654 PB - PMLR CY - [s. l.] ER - TY - JOUR A1 - Molina-Romero, Miguel A1 - Gómez, Pedro A. A1 - Sperl, Jonathan I. A1 - Czisch, Michael A1 - Sämann, Philipp G. A1 - Jones, Derek K. A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization JF - Magnetic Resonance in Medicine N2 - 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. UR - https://doi.org/10.1002/mrm.27181 KW - blind source separation KW - brain microstructure KW - diffusion MRI KW - free-water elimination KW - MR relaxometry KW - non-negative matrix factorization Y1 - 2018 UR - https://doi.org/10.1002/mrm.27181 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28210 SN - 1522-2594 VL - 80 IS - 5 SP - 2155 EP - 2172 PB - Wiley CY - Hoboken ER - TY - CHAP A1 - Molina-Romero, Miguel A1 - Wiestler, Benedikt A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases T2 - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 UR - https://doi.org/10.1007/978-3-030-00931-1_12 KW - Glioblastoma KW - Brain tumor KW - DTI KW - Deep learning KW - Free-water elimination KW - Data harmonization KW - Fractional anisotropy Y1 - 2018 UR - https://doi.org/10.1007/978-3-030-00931-1_12 SN - 978-3-030-00931-1 SN - 978-3-030-00930-4 SP - 98 EP - 106 PB - Springer CY - Cham ER - TY - INPR A1 - Kaushik, Sandeep A1 - Bylund, Mikael A1 - Cozzini, Cristina A1 - Shanbhag, Dattesh A1 - Petit, Steven F. A1 - Wyatt, Jonathan J. A1 - Menzel, Marion Irene A1 - Pirkl, Carolin A1 - Mehta, Bhairav A1 - Chauhan, Vikas A1 - Chandrasekharan, Kesavadas A1 - Jonsson, Joakim A1 - Nyholm, Tufve A1 - Wiesinger, Florian A1 - Menze, Bjoern H. T1 - Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network UR - https://doi.org/10.48550/arXiv.2203.16288 KW - MRI Radiation Therapy KW - Synthetic CT KW - Multi-task Network KW - image translation KW - PET/MR Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2203.16288 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Kaushik, Sandeep A1 - Bylund, Mikael A1 - Cozzini, Cristina A1 - Shanbhag, Dattesh A1 - Petit, Steven F. A1 - Wyatt, Jonathan J. A1 - Menzel, Marion Irene A1 - Pirkl, Carolin A1 - Mehta, Bhairav A1 - Chauhan, Vikas A1 - Chandrasekharan, Kesavadas A1 - Jonsson, Joakim A1 - Nyholm, Tufve A1 - Wiesinger, Florian A1 - Menze, Bjoern H. T1 - Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network JF - Physics in Medicine & Biology UR - https://doi.org/10.1088/1361-6560/acefa3 Y1 - 2023 UR - https://doi.org/10.1088/1361-6560/acefa3 SN - 0031-9155 SN - 1361-6560 VL - 68 IS - 19 PB - IOP Publishing CY - Bristol ER - TY - CHAP A1 - Gómez, Pedro A. A1 - Sperl, Jonathan I. A1 - Sprenger, Tim A1 - Metzler-Baddeley, Claudia A1 - Jones, Derek K. A1 - Saemann, Philipp A1 - Czisch, Michael A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. ED - Handels, Heinz ED - Deserno, Thomas Martin ED - Meinzer, Hans-Peter ED - Tolxdorff, Thomas T1 - Joint Reconstruction of Multi-Contrast MRI for Multiple Sclerosis Lesion Segmentation T2 - Bildverarbeitung für die Medizin 2015, Algorithmen – Systeme – Anwendungen, Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck UR - https://doi.org/10.1007/978-3-662-46224-9_28 Y1 - 2015 UR - https://doi.org/10.1007/978-3-662-46224-9_28 SN - 978-3-662-46224-9 SN - 978-3-662-46223-2 N1 - Die vollständige Angabe der Autorinnen und Autoren findet sich im PDF. SP - 155 EP - 160 PB - Springer Vieweg CY - Berlin ER - TY - INPR A1 - Gómez, Pedro A. A1 - Molina-Romero, Miguel A1 - Buonincontri, Guido A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging UR - https://doi.org/10.48550/arXiv.1901.07800 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1901.07800 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Golbabaee, Mohammad A1 - Buonincontri, Guido A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. A1 - Davies, Mike E. A1 - Gómez, Pedro A. T1 - Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders UR - https://doi.org/10.48550/arXiv.2001.08746 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2001.08746 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Mayo, Perla A1 - Cencini, Matteo A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Tosetti, Michela A1 - Menze, Bjoern H. A1 - Golbabaee, Mohammad ED - XU, Xuanang ED - Cui, Zhiming ED - Rekik, Islem ED - Ouyang, Xi ED - Sun, Kaicong T1 - StoDIP: Efficient 3D MRF Image Reconstruction with Deep Image Priors and Stochastic Iterations T2 - Machine Learning in Medical Imaging: 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings, Part II UR - https://doi.org/10.1007/978-3-031-73290-4_13 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-73290-4_13 SN - 978-3-031-73290-4 SP - 128 EP - 137 PB - Springer CY - Cham ER - TY - CHAP A1 - Ulas, Cagdas A1 - Gómez, Pedro A. A1 - Krahmer, Felix A1 - Sperl, Jonathan I. A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. ED - Zuluaga, Maria A. ED - Bhatia, Kanwal ED - Kainz, Bernhard ED - Moghari, Mehdi H. ED - Pace, Danielle F. T1 - Robust Reconstruction of Accelerated Perfusion MRI Using Local and Nonlocal Constraints T2 - 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 UR - https://doi.org/10.1007/978-3-319-52280-7_4 Y1 - 2017 UR - https://doi.org/10.1007/978-3-319-52280-7_4 SN - 978-3-319-52280-7 SN - 978-3-319-52279-1 SP - 37 EP - 47 PB - Springer CY - Cham ER - TY - CHAP A1 - Gómez, Pedro A. A1 - Ulas, Cagdas A1 - Sperl, Jonathan I. A1 - Sprenger, Tim A1 - Molina-Romero, Miguel A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. ED - Wu, Guorong ED - Coupé, Pierrick ED - Zhan, Yiqiang ED - Munsell, Brent ED - Rueckert, Daniel T1 - Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compressed Sensing T2 - 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 UR - https://doi.org/10.1007/978-3-319-28194-0_14 Y1 - 2016 UR - https://doi.org/10.1007/978-3-319-28194-0_14 SN - 978-3-319-28194-0 SN - 978-3-319-28193-3 SP - 112 EP - 119 PB - Springer CY - Cham ER - TY - INPR A1 - Pirkl, Carolin A1 - Gómez, Pedro A. A1 - Lipp, Ilona A1 - Buonincontri, Guido A1 - Molina-Romero, Miguel A1 - Sekuboyina, Anjany A1 - Waldmannstetter, Diana A1 - Dannenberg, Jonathan A1 - Endt, Sebastian A1 - Merola, Alberto A1 - Whittaker, Joseph R. A1 - Tomassini, Valentina A1 - Tosetti, Michela A1 - Jones, Derek K. A1 - Menze, Bjoern H. A1 - Menzel, Marion Irene T1 - Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting UR - https://doi.org/10.48550/arXiv.2005.02020 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2005.02020 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Pirkl, Carolin A1 - Cencini, Matteo A1 - Kurzawski, Jan W. A1 - Waldmannstetter, Diana A1 - Li, Hongwei A1 - Sekuboyina, Anjany A1 - Endt, Sebastian A1 - Peretti, Luca A1 - Donatelli, Graziella A1 - Pasquariello, Rosa A1 - Costagli, Mauro A1 - Buonincontri, Guido A1 - Tosetti, Michela A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping T2 - Proceedings of Machine Learning Research Y1 - 2021 UR - https://proceedings.mlr.press/v143/pirkl21a.html SP - 618 EP - 632 PB - PMLR CY - [s. l.] ER - TY - CHAP A1 - Gómez, Pedro A. A1 - Molina-Romero, Miguel A1 - Ulas, Cagdas A1 - Bounincontri, Guido A1 - Sperl, Jonathan I. A1 - Jones, Derek K. A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. ED - Ourselin, Sebastien ED - Joskowicz, Leo ED - Sabuncu, Mert R. ED - Unal, Gozde ED - Wells, William M. T1 - Simultaneous Parameter Mapping, Modality Synthesis, and Anatomical Labeling of the Brain with MR Fingerprinting T2 - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2016, 19th International Conference, Athens, Greece, October 17–21, 2016, Proceedings, Part III UR - https://doi.org/10.1007/978-3-319-46726-9_67 Y1 - 2016 UR - https://doi.org/10.1007/978-3-319-46726-9_67 SN - 978-3-319-46726-9 SN - 978-3-319-46725-2 SP - 579 EP - 586 PB - Springer CY - Cham ER - TY - CHAP A1 - Mayo, Perla A1 - Cencini, Matteo A1 - Fatania, Ketan A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. A1 - Tosetti, Michela A1 - Golbabaee, Mohammad T1 - Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders T2 - IEEE International Symposium on Biomedical Imaging (ISBI 2024): Conference Proceedings UR - https://doi.org/10.1109/ISBI56570.2024.10635677 Y1 - 2024 UR - https://doi.org/10.1109/ISBI56570.2024.10635677 SN - 979-8-3503-1333-8 PB - IEEE CY - Piscataway ER -