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 - Hoppe, Frederik A1 - Verdun, Claudio Mayrink A1 - Laus, Hannah A1 - Endt, Sebastian A1 - Menzel, Marion Irene A1 - Krahmer, Felix A1 - Rauhut, Holger ED - Leonardis, Aleš ED - Ricci, Elisa ED - Roth, Stefan ED - Russakovsky, Olga ED - Sattler, Torsten ED - Varol, Gül T1 - Imaging with Confidence: Uncertainty Quantification for High-Dimensional Undersampled MR Images T2 - Computer Vision – ECCV 2024: 18th European Conference, Proceedings, Part LXXVIII UR - https://doi.org/10.1007/978-3-031-73229-4_25 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-73229-4_25 SN - 978-3-031-73229-4 SP - 432 EP - 450 PB - Springer CY - Cham ER - TY - CHAP A1 - Hoppe, Frederik A1 - Krahmer, Felix A1 - Verdun, Claudio Mayrink A1 - Menzel, Marion Irene A1 - Rauhut, Holger T1 - High-Dimensional Confidence Regions in Sparse MRI T2 - Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) UR - https://doi.org/10.1109/ICASSP49357.2023.10096320 KW - debiased LASSO KW - compressed sensing KW - confidence regions KW - MRI Y1 - 2023 UR - https://doi.org/10.1109/ICASSP49357.2023.10096320 SN - 978-1-7281-6327-7 PB - IEEE CY - Piscataway ER - 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 - INPR A1 - Hoppe, Frederik A1 - Krahmer, Felix A1 - Verdun, Claudio Mayrink A1 - Menzel, Marion Irene A1 - Rauhut, Holger T1 - Uncertainty quantification for sparse Fourier recovery UR - https://doi.org/10.48550/arXiv.2212.14864 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2212.14864 PB - arXiv CY - Ithaca ER - TY - JOUR A1 - Nan, Yang A1 - Del Ser, Javier A1 - Walsh, Simon A1 - Schönlieb, Carola A1 - Roberts, Michael A1 - Selby, Ian A1 - Howard, Kit A1 - Owen, John A1 - Neville, Jon A1 - Guiot, Julien A1 - Ernst, Benoit A1 - Pastor, Ana A1 - Alberich-Bayarri, Angel A1 - Menzel, Marion Irene A1 - Walsh, Sean A1 - Vos, Wim A1 - Flerin, Nina A1 - Charbonnier, Jean-Paul A1 - Rikxoort, Eva van A1 - Chatterjee, Avishek A1 - Woodruff, Henry A1 - Lambin, Philippe A1 - Cerdá-Alberich, Leonor A1 - Martí-Bonmatí, Luis A1 - Herrera, Francisco A1 - Yang, Guang T1 - Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions JF - Information Fusion N2 - Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research UR - https://doi.org/10.1016/j.inffus.2022.01.001 KW - Information fusion KW - data harmonisation KW - data standardisation KW - domain adaptation KW - reproducibility Y1 - 2022 UR - https://doi.org/10.1016/j.inffus.2022.01.001 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13845 SN - 1566-2535 VL - 2022 IS - 82 SP - 99 EP - 122 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 - 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 - INPR A1 - Fatania, Ketan A1 - Chau, Kwai Y. A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Golbabaee, Mohammad T1 - Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI UR - https://doi.org/10.48550/arXiv.2211.12786 KW - Quantitative MRI KW - Magnetic Resonance Fingerprinting KW - Compressed Sensing KW - Inverse Problems KW - Self-Supervised Deep Learning KW - Equivariant Imaging Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2211.12786 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Fatania, Ketan A1 - Chau, Kwai Y. A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Golbabaee, Mohammad T1 - Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI T2 - 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) UR - https://doi.org/10.1109/ISBI53787.2023.10230440 Y1 - 2023 UR - https://doi.org/10.1109/ISBI53787.2023.10230440 SN - 978-1-6654-7358-3 PB - IEEE CY - Piscataway ER - TY - INPR 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 UR - https://doi.org/10.48550/arXiv.2202.05269 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2202.05269 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Mayo, Perla A1 - Cencini, Matteo A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Tosetti, Michela A1 - Menze, Bjoern H. A1 - Golbabaee, Mohammad ED - XU, Xuanang ED - Cui, Zhiming ED - Rekik, Islem ED - Ouyang, Xi ED - Sun, Kaicong T1 - StoDIP: Efficient 3D MRF Image Reconstruction with Deep Image Priors and Stochastic Iterations T2 - Machine Learning in Medical Imaging: 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings, Part II UR - https://doi.org/10.1007/978-3-031-73290-4_13 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-73290-4_13 SN - 978-3-031-73290-4 SP - 128 EP - 137 PB - Springer CY - Cham ER - TY - INPR A1 - Nan, Yang A1 - Del Ser, Javier A1 - Walsh, Simon A1 - Schönlieb, Carola A1 - Roberts, Michael A1 - Selby, Ian A1 - Howard, Kit A1 - Owen, John A1 - Neville, Jon A1 - Guiot, Julien A1 - Ernst, Benoit A1 - Pastor, Ana A1 - Alberich-Bayarri, Angel A1 - Menzel, Marion Irene A1 - Walsh, Sean A1 - Vos, Wim A1 - Flerin, Nina A1 - Charbonnier, Jean-Paul A1 - Rikxoort, Eva van A1 - Chatterjee, Avishek A1 - Woodruff, Henry A1 - Lambin, Philippe A1 - Cerdá-Alberich, Leonor A1 - Martí-Bonmatí, Luis A1 - Herrera, Francisco A1 - Yang, Guang T1 - Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions N2 - Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research. UR - https://doi.org/10.48550/arXiv.2201.06505 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2201.06505 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-46433 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Hoppe, Frederik A1 - Verdun, Claudio Mayrink A1 - Krahmer, Felix A1 - Menzel, Marion Irene A1 - Rauhut, Holger T1 - With or Without Replacement? Improving Confidence in Fourier Imaging T2 - 2024 International Workshop on the Theory of Computational Sensing and its Applications to Radar, Multimodal Sensing and Imaging (CoSeRa) UR - https://doi.org/10.1109/CoSeRa60846.2024.10720357 Y1 - 2024 UR - https://doi.org/10.1109/CoSeRa60846.2024.10720357 SN - 979-8-3503-6550-4 SP - 66 EP - 70 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Niessen, Natascha A1 - Pirkl, Carolin A1 - Solana, Ana Beatriz A1 - Eichhorn, Hannah A1 - Spieker, Veronika A1 - Huang, Wenqi A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Schnabel, Julia A. ED - Felsner, Lina ED - Küstner, Thomas ED - Maier, Andreas ED - Qin, Chen ED - Ahmadi, Seyed-Ahmad ED - Kazi, Anees ED - Hu, Xiaoling T1 - INR Meets Multi-contrast MRI Reconstruction T2 - Reconstruction and Imaging Motion Estimation, and Graphs in Biomedical Image Analysis: First International Workshop, RIME 2025, and 7th International Workshop, GRAIL 2025, Daejeon, South Korea, September 27, 2025, Proceedings UR - https://doi.org/10.1007/978-3-032-06103-4_3 Y1 - 2025 UR - https://doi.org/10.1007/978-3-032-06103-4_3 SN - 978-3-032-06103-4 SP - 23 EP - 33 PB - Springer CY - Cham ER - TY - CHAP A1 - Mayo, Perla A1 - Cencini, Matteo A1 - Fatania, Ketan A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. A1 - Tosetti, Michela A1 - Golbabaee, Mohammad T1 - Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders T2 - IEEE International Symposium on Biomedical Imaging (ISBI 2024): Conference Proceedings UR - https://doi.org/10.1109/ISBI56570.2024.10635677 Y1 - 2024 UR - https://doi.org/10.1109/ISBI56570.2024.10635677 SN - 979-8-3503-1333-8 PB - IEEE CY - Piscataway ER -