@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} } @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} } @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} } @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{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} } @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} } @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} }