@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{LiuGomezSolanaetal.2020, author = {Liu, Xin and G{\´o}mez, Pedro A. and Solana, Ana Beatriz and Wiesinger, Florian and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Silent 3D MR sequence for quantitative and multicontrast T1 and proton density imaging}, volume = {65}, pages = {185010}, journal = {Physics in Medicine \& Biology}, number = {18}, publisher = {IOP Publishing}, address = {Bristol}, issn = {1361-6560}, doi = {https://doi.org/10.1088/1361-6560/aba5e8}, year = {2020}, abstract = {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.}, language = {en} } @unpublished{KaushikBylundCozzinietal.2022, author = {Kaushik, Sandeep and Bylund, Mikael and Cozzini, Cristina and Shanbhag, Dattesh and Petit, Steven F. and Wyatt, Jonathan J. and Menzel, Marion Irene and Pirkl, Carolin and Mehta, Bhairav and Chauhan, Vikas and Chandrasekharan, Kesavadas and Jonsson, Joakim and Nyholm, Tufve and Wiesinger, Florian and Menze, Bjoern H.}, title = {Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2203.16288}, year = {2022}, language = {en} } @article{KaushikBylundCozzinietal.2023, author = {Kaushik, Sandeep and Bylund, Mikael and Cozzini, Cristina and Shanbhag, Dattesh and Petit, Steven F. and Wyatt, Jonathan J. and Menzel, Marion Irene and Pirkl, Carolin and Mehta, Bhairav and Chauhan, Vikas and Chandrasekharan, Kesavadas and Jonsson, Joakim and Nyholm, Tufve and Wiesinger, Florian and Menze, Bjoern H.}, title = {Region of interest focused MRI to synthetic CT translation using regression and segmentation multi-task network}, volume = {68}, pages = {195003}, journal = {Physics in Medicine \& Biology}, number = {19}, publisher = {IOP Publishing}, address = {Bristol}, issn = {0031-9155}, doi = {https://doi.org/10.1088/1361-6560/acefa3}, year = {2023}, language = {en} } @article{DurstKoellischFranketal.2015, author = {Durst, Markus and Koellisch, Ulrich and Frank, Annette and Rancan, Giaime and Gringeri, Concetta V. and Karas, Vincent and Wiesinger, Florian and Menzel, Marion Irene and Schwaiger, Markus and Haase, Axel and Schulte, Rolf F.}, title = {Comparison of acquisition schemes for hyperpolarised 13C imaging}, volume = {28}, journal = {NMR in Biomedicine}, number = {6}, publisher = {Wiley}, address = {New York}, issn = {1099-1492}, doi = {https://doi.org/10.1002/nbm.3301}, pages = {715 -- 725}, year = {2015}, language = {en} }