TY - INPR A1 - Endt, Sebastian A1 - Engel, Maria A1 - Naldi, Emanuele A1 - Assereto, Rodolfo A1 - Molendowska, Malwina A1 - Mueller, Lars A1 - Verdun, Claudio Mayrink A1 - Pirkl, Carolin A1 - Palombo, Marco A1 - Jones, Derek K. A1 - Menzel, Marion Irene T1 - In-vivo myelin water quantification using diffusion-relaxation correlation MRI: a comparison of 1D and 2D methods T2 - Research Square N2 - 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. UR - https://doi.org/10.21203/rs.3.rs-3069146/v1 Y1 - 2023 UR - https://doi.org/10.21203/rs.3.rs-3069146/v1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46448 SN - 2693-5015 PB - Research Square CY - Durham ER - TY - JOUR A1 - Endt, Sebastian A1 - Engel, Maria A1 - Naldi, Emanuele A1 - Assereto, Rodolfo A1 - Molendowska, Malwina A1 - Mueller, Lars A1 - Verdun, Claudio Mayrink A1 - Pirkl, Carolin A1 - Palombo, Marco A1 - Jones, Derek K. A1 - Menzel, Marion Irene T1 - In Vivo Myelin Water Quantification Using Diffusion–Relaxation Correlation MRI: A Comparison of 1D and 2D Methods JF - Applied Magnetic Resonance N2 - 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. UR - https://doi.org/10.1007/s00723-023-01584-1 KW - MWF mapping KW - Microstructure KW - Relaxometry KW - Diffusometry KW - Multi-component KW - Multidimensional KW - Multi-exponential KW - Multiparametric KW - Correlation imaging Y1 - 2023 UR - https://doi.org/10.1007/s00723-023-01584-1 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-40317 SN - 0937-9347 SN - 1613-7507 VL - 54 IS - 11-12 SP - 1571 EP - 1588 PB - Springer CY - Wien ER - TY - CHAP A1 - Endt, Sebastian A1 - Pirkl, Carolin A1 - Verdun, Claudio Mayrink A1 - Menze, Bjoern H. A1 - Menzel, Marion Irene ED - Romero, Eduardo ED - Costa, Eduardo Tavares ED - Brieva, Jorge ED - Rittner, Leticia ED - Linguraru, Marius George ED - Lepore, Natasha T1 - Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging T2 - 17th International Symposium on Medical Information Processing and Analysis UR - https://doi.org/10.1117/12.2604737 Y1 - 2021 UR - https://doi.org/10.1117/12.2604737 SN - 978-1-5106-5053-4 SN - 978-1-5106-5052-7 PB - SPIE CY - Bellingham ER - TY - JOUR A1 - Golbabaee, Mohammad A1 - Buonincontri, Guido A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. A1 - Davies, Mike E. A1 - Gómez, Pedro A. T1 - Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks JF - Medical Image Analysis UR - https://doi.org/10.1016/j.media.2020.101945 KW - magnetic resonance fingerprinting KW - compressed sensing KW - convex model-based reconstruction KW - residual network KW - encoder-decoder network Y1 - 2020 UR - https://doi.org/10.1016/j.media.2020.101945 SN - 1361-8415 SN - 1361-8423 VL - 2021 IS - 69 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Pirkl, Carolin A1 - Cencini, Matteo A1 - Kurzawski, Jan W. A1 - Waldmannstetter, Diana A1 - Li, Hongwei A1 - Menzel, Marion Irene T1 - Learning residual motion correction for fast and robust 3D multiparametric MRI JF - Medical Image Analysis N2 - Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable. UR - https://doi.org/10.1016/j.media.2022.102387 KW - Multiparametric MRI KW - 3D Motion correction KW - Residual learning KW - Multiscale CNN Y1 - 2022 UR - https://doi.org/10.1016/j.media.2022.102387 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13861 SN - 1361-8423 VL - 2022 IS - 77 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Pirkl, Carolin A1 - Nunez-Gonzalez, Laura A1 - Kofler, Florian A1 - Endt, Sebastian A1 - Grundl, Lioba A1 - Golbabaee, Mohammad A1 - Gómez, Pedro A. A1 - Cencini, Matteo A1 - Buonincontri, Guido A1 - Schulte, Rolf F. A1 - Smits, Marion A1 - Wiestler, Benedikt A1 - Menze, Bjoern H. A1 - Menzel, Marion Irene A1 - Hernandez-Tamames, Juan A. T1 - Accelerated 3D whole-brain T1, T2, and proton density mapping BT - feasibility for clinical glioma MR imaging JF - Neuroradiology N2 - Purpose: Advanced MRI-based biomarkers offer comprehensive and quantitative information for the evaluation and characterization of brain tumors. In this study, we report initial clinical experience in routine glioma imaging with a novel, fully 3D multiparametric quantitative transient-state imaging (QTI) method for tissue characterization based on T1 and T2 values. Methods: To demonstrate the viability of the proposed 3D QTI technique, nine glioma patients (grade II–IV), with a variety of disease states and treatment histories, were included in this study. First, we investigated the feasibility of 3D QTI (6:25 min scan time) for its use in clinical routine imaging, focusing on image reconstruction, parameter estimation, and contrast-weighted image synthesis. Second, for an initial assessment of 3D QTI-based quantitative MR biomarkers, we performed a ROI-based analysis to characterize T1 and T2 components in tumor and peritumoral tissue. Results: The 3D acquisition combined with a compressed sensing reconstruction and neural network-based parameter inference produced parametric maps with high isotropic resolution (1.125 × 1.125 × 1.125 mm3 voxel size) and whole-brain coverage (22.5 × 22.5 × 22.5 cm3 FOV), enabling the synthesis of clinically relevant T1-weighted, T2-weighted, and FLAIR contrasts without any extra scan time. Our study revealed increased T1 and T2 values in tumor and peritumoral regions compared to contralateral white matter, good agreement with healthy volunteer data, and high inter-subject consistency. Conclusion: 3D QTI demonstrated comprehensive tissue assessment of tumor substructures captured in T1 and T2 parameters. Aiming for fast acquisition of quantitative MR biomarkers, 3D QTI has potential to improve disease characterization in brain tumor patients under tight clinical time-constraints. UR - https://doi.org/10.1007/s00234-021-02703-0 KW - MRI KW - Image-based biomarkers KW - Multiparametric imaging KW - Glioma imaging KW - Neural networks Y1 - 2021 UR - https://doi.org/10.1007/s00234-021-02703-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13885 VL - 63 IS - 11 SP - 1831 EP - 1851 PB - Springer CY - Berlin ER - TY - CHAP A1 - Fatania, Ketan A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Hall, Peter A1 - Golbabaee, Mohammad T1 - A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers T2 - Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) UR - https://doi.org/10.1109/ISBI52829.2022.9761603 KW - Quantitative MRI KW - Magnetic Resonance Fingerprinting KW - Compressed Sensing KW - Inverse Problems KW - Deep Learning KW - Iterative Image Reconstruction KW - Plug-and-Play Y1 - 2022 UR - https://doi.org/10.1109/ISBI52829.2022.9761603 SN - 978-1-6654-2923-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Pirkl, Carolin A1 - Cencini, Matteo A1 - Kurzawski, Jan W. A1 - Waldmannstetter, Diana A1 - Li, Hongwei A1 - Sekuboyina, Anjany A1 - Endt, Sebastian A1 - Peretti, Luca A1 - Donatelli, Graziella A1 - Pasquariello, Rosa A1 - Costagli, Mauro A1 - Buonincontri, Guido A1 - Tosetti, Michela A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Residual learning for 3D motion corrected quantitative MRI BT - Robust clinical T1, T2 and proton density mapping T2 - Medical Imaging with Deep Learning MIDL 2021 KW - 3D multiparametric MRI KW - motion correction KW - deep learning KW - residual learning KW - multiscale CNN Y1 - 2021 UR - https://openreview.net/forum?id=hxgQM71AuRA PB - OpenReview ER - TY - CHAP A1 - Pirkl, Carolin A1 - Gómez, Pedro A. A1 - Lipp, Ilona A1 - Buonincontri, Guido A1 - Molina-Romero, Miguel A1 - Sekuboyina, Anjany A1 - Waldmannstetter, Diana A1 - Dannenberg, Jonathan A1 - Endt, Sebastian A1 - Merola, Alberto A1 - Whittaker, Joseph R. A1 - Tomassini, Valentina A1 - Tosetti, Michela A1 - Jones, Derek K. A1 - Menze, Bjoern H. A1 - Menzel, Marion Irene T1 - Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting T2 - Proceedings of Machine Learning Research KW - Magnetic Resonance Fingerprinting KW - Convolutional Neural Network KW - Image Reconstruction KW - Diffusion Tensor KW - Multiple Sclerosis Y1 - 2020 UR - https://proceedings.mlr.press/v121/pirk20a.html SN - 2640-3498 IS - 121 SP - 639 EP - 654 PB - PMLR CY - [s. l.] ER - TY - INPR A1 - Kaushik, Sandeep A1 - Bylund, Mikael A1 - Cozzini, Cristina A1 - Shanbhag, Dattesh A1 - Petit, Steven F. A1 - Wyatt, Jonathan J. A1 - Menzel, Marion Irene A1 - Pirkl, Carolin A1 - Mehta, Bhairav A1 - Chauhan, Vikas A1 - Chandrasekharan, Kesavadas A1 - Jonsson, Joakim A1 - Nyholm, Tufve A1 - Wiesinger, Florian A1 - Menze, Bjoern H. T1 - Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network UR - https://doi.org/10.48550/arXiv.2203.16288 KW - MRI Radiation Therapy KW - Synthetic CT KW - Multi-task Network KW - image translation KW - PET/MR Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2203.16288 PB - arXiv CY - Ithaca ER -