@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{GolkovSprengerSperletal.2016, author = {Golkov, Vladimir and Sprenger, Tim and Sperl, Jonathan I. and Menzel, Marion Irene and Czisch, Michael and S{\"a}mann, Philipp G. and Cremers, Daniel}, title = {Model-free novelty-based diffusion MRI}, booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4799-2349-6}, doi = {https://doi.org/10.1109/ISBI.2016.7493489}, pages = {1233 -- 1236}, year = {2016}, language = {en} } @article{GolkovDosovitskiySperletal.2016, author = {Golkov, Vladimir and Dosovitskiy, Alexey and Sperl, Jonathan I. and Menzel, Marion Irene and Czisch, Michael and S{\"a}mann, Philipp G. and Brox, Thomas and Cremers, Daniel}, title = {q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans}, volume = {35}, journal = {IEEE Transactions on Medical Imaging}, number = {5}, publisher = {IEEE}, address = {New York}, issn = {1558-254X}, doi = {https://doi.org/10.1109/TMI.2016.2551324}, pages = {1344 -- 1351}, year = {2016}, 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} } @article{SprengerSperlFernandezetal.2016, author = {Sprenger, Tim and Sperl, Jonathan I. and Fernandez, Brice and Golkov, Vladimir and Eidner, Ines and S{\"a}mann, Philipp G. and Czisch, Michael and Tan, Ek Tsoon and Hardy, Christopher J. and Marinelli, Luca and Haase, Axel and Menzel, Marion Irene}, title = {Bias and precision analysis of diffusional kurtosis imaging for different acquisition schemes}, volume = {76}, journal = {Magnetic Resonance in Medicine}, number = {6}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.26008}, pages = {1684 -- 1696}, year = {2016}, language = {en} } @inproceedings{GolkovDosovitskiySaemannetal.2015, author = {Golkov, Vladimir and Dosovitskiy, Alexey and S{\"a}mann, Philipp G. and Sperl, Jonathan I. and Sprenger, Tim and Czisch, Michael and Menzel, Marion Irene and G{\´o}mez, Pedro A. and Haase, Axel and Brox, Thomas and Cremers, Daniel}, title = {q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I}, editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-24553-9}, doi = {https://doi.org/10.1007/978-3-319-24553-9_5}, pages = {37 -- 44}, year = {2015}, language = {en} }