TY - JOUR A1 - Gómez Damián, Pedro A. A1 - Sperl, Jonathan I. A1 - Janich, Martin A. A1 - Khegai, Oleksandr A1 - Wiesinger, Florian A1 - Glaser, Steffen J. A1 - Haase, Axel A1 - Schwaiger, Markus A1 - Schulte, Rolf F. A1 - Menzel, Marion Irene T1 - Multisite Kinetic Modeling of 13C Metabolic MR Using [1-13C]Pyruvate JF - Radiology Research and Practice N2 - Hyperpolarized13C imaging allows real-timein vivomeasurements of metabolite levels. Quantification of metabolite conversion between [1-13C]pyruvate and downstream metabolites [1-13C]alanine, [1-13C]lactate, and [13C]bicarbonate can be achieved through kinetic modeling. Since pyruvate interacts dynamically and simultaneously with its downstream metabolites, the purpose of this work is the determination of parameter values through a multisite, dynamic model involving possible biochemical pathways present in MR spectroscopy. Kinetic modeling parameters were determined by fitting the multisite model to time-domain dynamic metabolite data. The results for different pyruvate doses were compared with those of different two-site models to evaluate the hypothesis that for identical data the uncertainty of a model and the signal-to-noise ratio determine the sensitivity in detecting small physiological differences in the target metabolism. In comparison to the two-site exchange models, the multisite model yielded metabolic conversion rates with smaller bias and smaller standard deviation, as demonstrated in simulations with different signal-to-noise ratio. Pyruvate dose effects observed previously were confirmed and quantified through metabolic conversion rate values. Parameter interdependency allowed an accurate quantification and can therefore be useful for monitoring metabolic activity in different tissues. UR - https://doi.org/10.1155/2014/871619 Y1 - 2014 UR - https://doi.org/10.1155/2014/871619 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41646 SN - 2090-195X VL - 2014 PB - Hindawi CY - New York 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 - JOUR A1 - Kubala, Eugen A1 - Muñoz-Álvarez, Kim A. A1 - Topping, Geoffrey A1 - Hundshammer, Christian A1 - Feuerecker, Benedikt A1 - Gómez, Pedro A. A1 - Pariani, Giorgio A1 - Schilling, Franz A1 - Glaser, Steffen J. A1 - Schulte, Rolf F. A1 - Menzel, Marion Irene A1 - Schwaiger, Markus T1 - Hyperpolarized 13C Metabolic Magnetic Resonance Spectroscopy and Imaging JF - Journal of Visualized Experiments UR - https://doi.org/10.3791/54751 Y1 - 2016 UR - https://doi.org/10.3791/54751 SN - 1940-087X VL - 2016 IS - 118 PB - MyJoVE Corporation CY - Cambridge 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 - Benjamin, Arnold Julian Vinoj A1 - Gómez, Pedro A. A1 - Golbabaee, Mohammad A1 - Bin Mahbub, Zaid A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Davies, Mike E. A1 - Marshall, Ian T1 - Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting JF - Magnetic Resonance Imaging UR - https://doi.org/10.1016/j.mri.2019.04.014 KW - cartesian MRF KW - multi-shot EPI KW - quantitative maps Y1 - 2019 UR - https://doi.org/10.1016/j.mri.2019.04.014 SN - 0730-725X VL - 2019 IS - 61 SP - 20 EP - 32 PB - Elsevier CY - Amsterdam 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 - CHAP A1 - Golbabaee, Mohammad A1 - Chen, Dongdong A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. T1 - Geometry of Deep Learning for Magnetic Resonance Fingerprinting T2 - ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) UR - https://doi.org/10.1109/ICASSP.2019.8683549 KW - magnetic resonance fingerprinting KW - inverse problem KW - deep learning KW - dictionary KW - manifold compressed sensing Y1 - 2019 UR - https://doi.org/10.1109/ICASSP.2019.8683549 SN - 978-1-5386-4658-8 SP - 7825 EP - 7829 PB - IEEE CY - Piscataway 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 - 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 - CHAP A1 - Chen, Dongdong A1 - Golbabaee, Mohammad A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. ED - Cardoso, M. Jorge ED - Feragen, Aasa ED - Glocker, Ben ED - Konukoglu, Ender ED - Oguz, Ipek ED - Unal, Gozde ED - Vercauteren, Tom T1 - Deep Fully Convolutional Network for MR Fingerprinting T2 - Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track N2 - This work proposes an end-to-end deep fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employs linear dimensionality reduction and then uses a neural network to project the data into the tissue parameters. The MRF dictionary is only used for training the network and not during image reconstruction. We show that MRF-FCNN is capable of achieving accuracy comparable to the ground-truth maps thanks to capturing spatio-temporal data structures without a need for the non-scalable dictionary matching step used in the baseline reconstructions. KW - Magnetic Resonance Fingerprinting KW - Deep Learning Y1 - 2019 UR - https://openreview.net/forum?id=SJxUdvJTtN UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31163 ER - TY - CHAP A1 - Golbabaee, Mohammad A1 - Chen, Dongdong A1 - Davies, Mike E. A1 - Menzel, Marion Irene A1 - Gómez, Pedro A. ED - Cardoso, M. Jorge ED - Feragen, Aasa ED - Glocker, Ben ED - Konukoglu, Ender ED - Oguz, Ipek ED - Unal, Gozde ED - Vercauteren, Tom T1 - Spatio-temporal regularization for deep MR Fingerprinting T2 - Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track N2 - We study a deep learning approach to address the heavy storage and computation re- quirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprint- ing (MRF) reconstruction. The MRF-Net provides a piece-wise affine approximation to the (temporal) Bloch response manifold projection. Fed with non-iterated back-projected images, the network alone is unable to fully resolve spatially-correlated artefacts which ap- pear in highly undersampling regimes. We propose an accelerated iterative reconstruction to minimize these artefacts before feeding into the network. This is done through a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series. KW - magnetic resonance fingerprinting KW - deep learning KW - regularisation Y1 - 2019 UR - https://openreview.net/forum?id=ryx64UL6YE UR - https://2019.midl.io/program/extended-abstracts.html UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31174 ER - TY - INPR A1 - Chen, Dongdong A1 - Golbabaee, Mohammad A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. T1 - A Fully Convolutional Network for MR Fingerprinting UR - https://doi.org/10.48550/arXiv.1911.09846 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1911.09846 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Benjamin, Arnold Julian Vinoj A1 - Gómez, Pedro A. A1 - Golbabaee, Mohammad A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Davies, Mike E. A1 - Marshall, Ian T1 - Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF UR - https://doi.org/10.48550/arXiv.1809.02506 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.1809.02506 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Benjamin, Arnold Julian Vinoj A1 - Gómez, Pedro A. A1 - Golbabaee, Mohammad A1 - Bin Mahbub, Zaid A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Davies, Mike E. A1 - Marshall, Ian T1 - Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: an alternative to conventional spiral MR Fingerprinting UR - https://doi.org/10.48550/arXiv.1906.08195 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1906.08195 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Golbabaee, Mohammad A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Buonincontri, Guido A1 - Gómez, Pedro A. T1 - Deep MR Fingerprinting with total-variation and low-rank subspace priors UR - https://doi.org/10.48550/arXiv.1902.10205 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1902.10205 PB - arXiv CY - Ithaca 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 - Golbabaee, Mohammad A1 - Chen, Dongdong A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. T1 - Geometry of Deep Learning for Magnetic Resonance Fingerprinting UR - https://doi.org/10.48550/arXiv.1809.01749 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.1809.01749 PB - arXiv CY - Ithaca 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 - 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 - Golkov, Vladimir A1 - Dosovitskiy, Alexey A1 - Sämann, Philipp G. A1 - Sperl, Jonathan I. A1 - Sprenger, Tim A1 - Czisch, Michael A1 - Menzel, Marion Irene A1 - Gómez, Pedro A. A1 - Haase, Axel A1 - Brox, Thomas A1 - Cremers, Daniel ED - Navab, Nassir ED - Hornegger, Joachim ED - Wells, William M. ED - Frangi, Alejandro F. T1 - q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans T2 - Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015, 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part I UR - https://doi.org/10.1007/978-3-319-24553-9_5 Y1 - 2015 UR - https://doi.org/10.1007/978-3-319-24553-9_5 SN - 978-3-319-24553-9 SN - 978-3-319-24552-2 SP - 37 EP - 44 PB - Springer CY - Cham 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 -