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 - 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 N1 - Der Nachweis einer Preprint-Version dieser Veröffentlichung ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4648 VL - 9 PB - Springer Nature CY - London 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 N1 - Die veröffentlichte Version dieses Preprints ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/1885 PB - arXiv CY - Ithaca 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 N1 - Die veröffentlichte Version dieses Preprints ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/2817 PB - arXiv CY - Ithaca 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 N1 - Der Nachweis einer Preprint-Version dieser Veröffentlichung ist ebenfalls in diesem Repositorium verzeichnet, s. https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/4468 IS - 121 SP - 639 EP - 654 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 - 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 -