@inproceedings{HoppeVerdunLausetal.2024, author = {Hoppe, Frederik and Verdun, Claudio Mayrink and Laus, Hannah and Endt, Sebastian and Menzel, Marion Irene and Krahmer, Felix and Rauhut, Holger}, title = {Imaging with Confidence: Uncertainty Quantification for High-Dimensional Undersampled MR Images}, booktitle = {Computer Vision - ECCV 2024: 18th European Conference, Proceedings, Part LXXVIII}, editor = {Leonardis, Aleš and Ricci, Elisa and Roth, Stefan and Russakovsky, Olga and Sattler, Torsten and Varol, G{\"u}l}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-73229-4}, doi = {https://doi.org/10.1007/978-3-031-73229-4_25}, pages = {432 -- 450}, year = {2024}, language = {en} } @inproceedings{HoppeKrahmerVerdunetal.2023, author = {Hoppe, Frederik and Krahmer, Felix and Verdun, Claudio Mayrink and Menzel, Marion Irene and Rauhut, Holger}, title = {High-Dimensional Confidence Regions in Sparse MRI}, booktitle = {Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-7281-6327-7}, doi = {https://doi.org/10.1109/ICASSP49357.2023.10096320}, year = {2023}, language = {en} } @unpublished{EndtEngelNaldietal.2023, author = {Endt, Sebastian and Engel, Maria and Naldi, Emanuele and Assereto, Rodolfo and Molendowska, Malwina and Mueller, Lars and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} } @inproceedings{EndtPirklVerdunetal.2021, author = {Endt, Sebastian and Pirkl, Carolin and Verdun, Claudio Mayrink and Menze, Bjoern H. and Menzel, Marion Irene}, title = {Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging}, booktitle = {17th International Symposium on Medical Information Processing and Analysis}, editor = {Romero, Eduardo and Costa, Eduardo Tavares and Brieva, Jorge and Rittner, Leticia and Linguraru, Marius George and Lepore, Natasha}, publisher = {SPIE}, address = {Bellingham}, isbn = {978-1-5106-5053-4}, doi = {https://doi.org/10.1117/12.2604737}, year = {2021}, language = {en} } @unpublished{HoppeKrahmerVerdunetal.2022, author = {Hoppe, Frederik and Krahmer, Felix and Verdun, Claudio Mayrink and Menzel, Marion Irene and Rauhut, Holger}, title = {Uncertainty quantification for sparse Fourier recovery}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2212.14864}, year = {2022}, language = {en} } @inproceedings{HoppeVerdunKrahmeretal.2024, author = {Hoppe, Frederik and Verdun, Claudio Mayrink and Krahmer, Felix and Menzel, Marion Irene and Rauhut, Holger}, title = {With or Without Replacement? Improving Confidence in Fourier Imaging}, booktitle = {2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging (CoSeRa)}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-6550-4}, doi = {https://doi.org/10.1109/CoSeRa60846.2024.10720357}, pages = {66 -- 70}, year = {2024}, language = {en} }