@unpublished{EndtEngelNaldietal.2023, author = {Endt, Sebastian and Engel, Maria and Naldi, Emanuele and Assereto, Rodolfo and Molendowska, Malwina and Mueller, Lars and Verdun, Claudio Mayrink and Pirkl, Carolin and Palombo, Marco and Jones, Derek K. and Menzel, Marion Irene}, title = {In-vivo myelin water quantification using diffusion-relaxation correlation MRI: a comparison of 1D and 2D methods}, titleParent = {Research Square}, publisher = {Research Square}, address = {Durham}, doi = {https://doi.org/10.21203/rs.3.rs-3069146/v1}, year = {2023}, abstract = {Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion-recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion-relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. While 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion.}, language = {en} } @article{EndtEngelNaldietal.2023, author = {Endt, Sebastian and Engel, Maria and Naldi, Emanuele and Assereto, Rodolfo and Molendowska, Malwina and Mueller, Lars and Verdun, Claudio Mayrink and Pirkl, Carolin and Palombo, Marco and Jones, Derek K. and Menzel, Marion Irene}, title = {In Vivo Myelin Water Quantification Using Diffusion-Relaxation Correlation MRI: A Comparison of 1D and 2D Methods}, volume = {54}, journal = {Applied Magnetic Resonance}, number = {11-12}, publisher = {Springer}, address = {Wien}, issn = {0937-9347}, doi = {https://doi.org/10.1007/s00723-023-01584-1}, pages = {1571 -- 1588}, year = {2023}, abstract = {Multidimensional Magnetic Resonance Imaging (MRI) is a versatile tool for microstructure mapping. We use a diffusion weighted inversion recovery spin echo (DW-IR-SE) sequence with spiral readouts at ultra-strong gradients to acquire a rich diffusion-relaxation data set with sensitivity to myelin water. We reconstruct 1D and 2D spectra with a two-step convex optimization approach and investigate a variety of multidimensional MRI methods, including 1D multi-component relaxometry, 1D multi-component diffusometry, 2D relaxation correlation imaging, and 2D diffusion-relaxation correlation spectroscopic imaging (DR-CSI), in terms of their potential to quantify tissue microstructure, including the myelin water fraction (MWF). We observe a distinct spectral peak that we attribute to myelin water in multi-component T1 relaxometry, T1-T2 correlation, T1-D correlation, and T2-D correlation imaging. Due to lower achievable echo times compared to diffusometry, MWF maps from relaxometry have higher quality. Whilst 1D multi-component T1 data allows much faster myelin mapping, 2D approaches could offer unique insights into tissue microstructure and especially myelin diffusion.}, 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} } @inproceedings{FataniaChauPirkletal.2023, author = {Fatania, Ketan and Chau, Kwai Y. and Pirkl, Carolin and Menzel, Marion Irene and Golbabaee, Mohammad}, title = {Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI}, booktitle = {2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-7358-3}, doi = {https://doi.org/10.1109/ISBI53787.2023.10230440}, year = {2023}, language = {en} }