@article{SperlSprengerTanetal.2017, author = {Sperl, Jonathan I. and Sprenger, Tim and Tan, Ek Tsoon and Menzel, Marion Irene and Hardy, Christopher J. and Marinelli, Luca}, title = {Model-based denoising in diffusion-weighted imaging using generalized spherical deconvolution}, volume = {78}, journal = {Magnetic Resonance in Medicine}, number = {6}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.26626}, pages = {2428 -- 2438}, year = {2017}, language = {en} } @article{GomezDamianSperlJanichetal.2014, author = {G{\´o}mez Dami{\´a}n, Pedro A. and Sperl, Jonathan I. and Janich, Martin A. and Khegai, Oleksandr and Wiesinger, Florian and Glaser, Steffen J. and Haase, Axel and Schwaiger, Markus and Schulte, Rolf F. and Menzel, Marion Irene}, title = {Multisite Kinetic Modeling of 13C Metabolic MR Using [1-13C]Pyruvate}, volume = {2014}, pages = {871619}, journal = {Radiology Research and Practice}, publisher = {Hindawi}, address = {New York}, issn = {2090-195X}, doi = {https://doi.org/10.1155/2014/871619}, year = {2014}, abstract = {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.}, 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} } @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{LacerdaSperlMenzeletal.2015, author = {Lacerda, Luis M. and Sperl, Jonathan I. and Menzel, Marion Irene and Sprenger, Tim and Barker, Gareth J. and Dell'Acqua, Flavio}, title = {Diffusion in realistic biophysical systems can lead to aliasing effects in diffusion spectrum imaging}, volume = {76}, journal = {Magnetic Resonance in Medicine}, number = {6}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.26080}, pages = {1837 -- 1847}, year = {2015}, abstract = {Purpose Diffusion spectrum imaging (DSI) is an imaging technique that has been successfully applied to resolve white matter crossings in the human brain. However, its accuracy in complex microstructure environments has not been well characterized. Theory and Methods Here we have simulated different tissue configurations, sampling schemes, and processing steps to evaluate DSI performances' under realistic biophysical conditions. A novel approach to compute the orientation distribution function (ODF) has also been developed to include biophysical constraints, namely integration ranges compatible with axial fiber diffusivities. Results Performed simulations identified several DSI configurations that consistently show aliasing artifacts caused by fast diffusion components for both isotropic diffusion and fiber configurations. The proposed method for ODF computation showed some improvement in reducing such artifacts and improving the ability to resolve crossings, while keeping the quantitative nature of the ODF. Conclusion In this study, we identified an important limitation of current DSI implementations, specifically the presence of aliasing due to fast diffusion components like those from pathological tissues, which are not well characterized, and can lead to artifactual fiber reconstructions. To minimize this issue, a new way of computing the ODF was introduced, which removes most of these artifacts and offers improved angular resolution.}, language = {en} } @inproceedings{UlasGomezKrahmeretal.2017, author = {Ulas, Cagdas and G{\´o}mez, Pedro A. and Krahmer, Felix and Sperl, Jonathan I. and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Robust Reconstruction of Accelerated Perfusion MRI Using Local and Nonlocal Constraints}, booktitle = {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}, editor = {Zuluaga, Maria A. and Bhatia, Kanwal and Kainz, Bernhard and Moghari, Mehdi H. and Pace, Danielle F.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-52280-7}, doi = {https://doi.org/10.1007/978-3-319-52280-7_4}, pages = {37 -- 47}, year = {2017}, language = {en} } @article{TanMarinelliSperletal.2014, author = {Tan, Ek Tsoon and Marinelli, Luca and Sperl, Jonathan I. and Menzel, Marion Irene and Hardy, Christopher J.}, title = {Multi-directional anisotropy from diffusion orientation distribution functions}, volume = {41}, journal = {Journal of Magnetic Resonance Imaging}, number = {3}, publisher = {Wiley}, address = {New York}, issn = {1522-2586}, doi = {https://doi.org/10.1002/jmri.24589}, pages = {841 -- 850}, year = {2014}, language = {en} } @article{SprengerSperlFernandezetal.2016, author = {Sprenger, Tim and Sperl, Jonathan I. and Fernandez, Brice and Haase, Axel and Menzel, Marion Irene}, title = {Real valued diffusion-weighted imaging using decorrelated phase filtering}, volume = {77}, journal = {Magnetic Resonance in Medicine}, number = {2}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.26138}, pages = {559 -- 570}, year = {2016}, 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{GolkovSperlMenzeletal.2014, author = {Golkov, Vladimir and Sperl, Jonathan I. and Menzel, Marion Irene and Sprenger, Tim and Tan, Ek Tsoon and Marinelli, Luca and Hardy, Christopher J. and Haase, Axel and Cremers, Daniel}, title = {Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate}, booktitle = {Computational Diffusion MRI, MICCAI Workshop, Boston, MA, USA, September 2014}, editor = {O'Donnell, Lauren and Nedjati-Gilan, Gemma and Rathi, Yogesh and Reisert, Marco and Schneider, Torben}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-11182-7}, doi = {https://doi.org/10.1007/978-3-319-11182-7_16}, pages = {181 -- 191}, year = {2014}, 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} } @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} } @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} }