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 - JOUR A1 - Sperl, Jonathan I. A1 - Sprenger, Tim A1 - Tan, Ek Tsoon A1 - Menzel, Marion Irene A1 - Hardy, Christopher J. A1 - Marinelli, Luca T1 - Model‐based denoising in diffusion‐weighted imaging using generalized spherical deconvolution JF - Magnetic Resonance in Medicine UR - https://doi.org/10.1002/mrm.26626 Y1 - 2017 UR - https://doi.org/10.1002/mrm.26626 SN - 1522-2594 VL - 78 IS - 6 SP - 2428 EP - 2438 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Feuerecker, Benedikt A1 - Durst, Markus A1 - Michalik, Michael A1 - Schneider, Günter A1 - Saur, Dieter A1 - Menzel, Marion Irene A1 - Schwaiger, Markus A1 - Schilling, Franz T1 - Hyperpolarized 13C Diffusion MRS of Co-Polarized Pyruvate and Fumarate to Measure Lactate Export and Necrosis JF - Journal of Cancer N2 - Background: Non-invasive tumor characterization and monitoring are among the key goals of medical imaging. Using hyperpolarized 13C-labelled metabolic probes fast metabolic pathways can be probed in real-time, providing new opportunities for tumor characterization. In this in vitro study, we investigated whether measurement of apparent diffusion coefficient (ADC) measurements and magnetic resonance spectroscopy (MRS) of co-polarized 13C-labeled pyruvic acid and fumaric acid can non-invasively detect both necrosis and changes in lactate export, which are parameters indicative of tumor aggressiveness. Methods: 13C-labeled pyruvic acid and fumaric acid were co-polarized in a preclinical hyperpolarizer and the dissolved compounds were added to prepared samples of 8932 pancreatic cancer and MCF-7 breast carcinoma cells. Extracellular lactate concentrations and cell viability were measured in separate assays. Results: The mean ratios of the ADC values of lactate and pyruvate (ADClac/ADCpyr) between MCF-7 (0.533 ± 0.015, n = 3) and 8932 pancreatic cancer cells (0.744 ± 0.064, n = 3) showed a statistically significant difference (p = 0.048). 8932 cells had higher extracellular lactate concentrations in the extracellular medium (22.97 ± 2.53 ng/µl) compared with MCF-7 cells (7.52 ± 0.59 ng/µl; p < 0.001). Fumarate-to-malate conversion was only detectable in necrotic cells, thereby allowing clear differentiation between necrotic and viable cells. Conclusion: We provide evidence that MRS of hyperpolarized 13C-labelled pyruvic acid and fumaric acid, with their respective conversions to lactate and malate, are useful for characterization of necrosis and lactate efflux in tumor cells. UR - https://doi.org/10.7150/jca.20250 Y1 - 2017 UR - https://doi.org/10.7150/jca.20250 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41636 SN - 1837-9664 VL - 8 IS - 15 SP - 3078 EP - 3085 PB - Ivyspring International Publisher CY - Sydney ER - TY - JOUR A1 - Gómez Damián, Pedro A. A1 - Sperl, Jonathan I. A1 - Janich, Martin A. A1 - Khegai, Oleksandr A1 - Wiesinger, Florian A1 - Glaser, Steffen J. A1 - Haase, Axel A1 - Schwaiger, Markus A1 - Schulte, Rolf F. A1 - Menzel, Marion Irene T1 - Multisite Kinetic Modeling of 13C Metabolic MR Using [1-13C]Pyruvate JF - Radiology Research and Practice N2 - 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. UR - https://doi.org/10.1155/2014/871619 Y1 - 2014 UR - https://doi.org/10.1155/2014/871619 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-41646 SN - 2090-195X VL - 2014 PB - Hindawi CY - New York 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 - Liu, Xin A1 - Gómez, Pedro A. A1 - Solana, Ana Beatriz A1 - Wiesinger, Florian A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Silent 3D MR sequence for quantitative and multicontrast T1 and proton density imaging JF - Physics in Medicine & Biology N2 - 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. UR - https://doi.org/10.1088/1361-6560/aba5e8 KW - T1 mapping KW - proton density KW - silent MRI KW - inversion recovery KW - temporal subspace Y1 - 2020 UR - https://doi.org/10.1088/1361-6560/aba5e8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-18793 SN - 1361-6560 SN - 0031-9155 VL - 65 IS - 18 PB - IOP Publishing CY - Bristol ER - TY - JOUR A1 - Coello, Eduardo A1 - Hafalir, Fatih S. A1 - Noeske, Ralph A1 - Menzel, Marion Irene A1 - Haase, Axel A1 - Menze, Bjoern H. A1 - Schulte, Rolf F. T1 - Overdiscrete echo-planar spectroscopic imaging with correlated higher-order phase correction JF - Magnetic Resonance in Medicine UR - https://doi.org/10.1002/mrm.28105 Y1 - 2019 UR - https://doi.org/10.1002/mrm.28105 SN - 1522-2594 VL - 84 IS - 1 SP - 11 EP - 24 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Benjamin, Arnold Julian Vinoj A1 - Gómez, Pedro A. A1 - Golbabaee, Mohammad A1 - Bin Mahbub, Zaid A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Davies, Mike E. A1 - Marshall, Ian T1 - Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting JF - Magnetic Resonance Imaging UR - https://doi.org/10.1016/j.mri.2019.04.014 KW - cartesian MRF KW - multi-shot EPI KW - quantitative maps Y1 - 2019 UR - https://doi.org/10.1016/j.mri.2019.04.014 SN - 0730-725X VL - 2019 IS - 61 SP - 20 EP - 32 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Gómez, Pedro A. A1 - Molina-Romero, Miguel A1 - Buonincontri, Guido A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging JF - Scientific Reports N2 - Magnetic resonance imaging (MRI) has evolved into an outstandingly versatile diagnostic modality, as it has the ability to non-invasively produce detailed information on a tissue’s structure and function. Complementary data is normally obtained in separate measurements, either as contrast-weighted images, which are fast and simple to acquire, or as quantitative parametric maps, which offer an absolute quantification of underlying biophysical effects, such as relaxation times or flow. Here, we demonstrate how to acquire and reconstruct data in a transient-state with a dual purpose: 1 – to generate contrast-weighted images that can be adjusted to emphasise clinically relevant image biomarkers; exemplified with signal modulation according to flow to obtain angiography information, and 2 – to simultaneously infer multiple quantitative parameters with a single, highly accelerated acquisition. This is achieved by introducing three novel elements: a model that accounts for flowing blood, a method for sequence design using smooth flip angle excitation patterns that incorporates both parameter encoding and signal contrast, and the reconstruction of temporally resolved contrast-weighted images. From these images we simultaneously obtain angiography projections and multiple quantitative maps. By doing so, we increase the amount of clinically relevant data without adding measurement time, creating new dimensions for biomarker exploration and adding value to MR examinations for patients and clinicians alike. UR - https://doi.org/10.1038/s41598-019-44832-w Y1 - 2019 UR - https://doi.org/10.1038/s41598-019-44832-w UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-18854 SN - 2045-2322 VL - 9 PB - Springer Nature CY - London ER - TY - CHAP A1 - Golbabaee, Mohammad A1 - Chen, Dongdong A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. T1 - Geometry of Deep Learning for Magnetic Resonance Fingerprinting T2 - ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) UR - https://doi.org/10.1109/ICASSP.2019.8683549 KW - magnetic resonance fingerprinting KW - inverse problem KW - deep learning KW - dictionary KW - manifold compressed sensing Y1 - 2019 UR - https://doi.org/10.1109/ICASSP.2019.8683549 SN - 978-1-5386-4658-8 SP - 7825 EP - 7829 PB - IEEE CY - Piscataway 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 - Wu, Mingming A1 - Mulder, Hendrik T. A1 - Baron, Paul A1 - Coello, Eduardo A1 - Menzel, Marion Irene A1 - van Rhoon, Gerard C. A1 - Haase, Axel T1 - Correction of motion-induced susceptibility artifacts and B0 drift during proton resonance frequency shift-based MR thermometry in the pelvis with background field removal methods JF - Magnetic Resonance in Medicine N2 - Purpose The linear change of the water proton resonance frequency shift (PRFS) with temperature is used to monitor temperature change based on the temporal difference of image phase. Here, the effect of motion-induced susceptibility artifacts on the phase difference was studied in the context of mild radio frequency hyperthermia in the pelvis. Methods First, the respiratory-induced field variations were disentangled from digestive gas motion in the pelvis. The projection onto dipole fields (PDF) as well as the Laplacian boundary value (LBV) algorithm were applied on the phase difference data to eliminate motion-induced susceptibility artifacts. Both background field removal (BFR) algorithms were studied using simulations of susceptibility artifacts, a phantom heating experiment, and volunteer and patient heating data. Results Respiratory-induced field variations were negligible in the presence of the filled water bolus. Even though LBV and PDF showed comparable results for most data, LBV seemed more robust in our data sets. Some data sets suggested that PDF tends to overestimate the background field, thus removing phase attributed to temperature. The BFR methods even corrected for susceptibility variations induced by a subvoxel displacement of the phantom. The method yielded successful artifact correction in 2 out of 4 patient treatment data sets during the entire treatment duration of mild RF heating of cervical cancer. The heating pattern corresponded well with temperature probe data. Conclusion The application of background field removal methods in PRFS-based MR thermometry has great potential in various heating applications and body regions to reduce motion-induced susceptibility artifacts that originate outside the region of interest, while conserving temperature-induced PRFS. In addition, BFR automatically removes up to a first-order spatial B0 drift. UR - https://doi.org/10.1002/mrm.28302 KW - B0 drift KW - background field removal KW - hyperthermia KW - motion KW - MR thermometry KW - susceptibility Y1 - 2020 UR - https://doi.org/10.1002/mrm.28302 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-18776 SN - 1522-2594 VL - 84 IS - 5 SP - 2495 EP - 2511 PB - Wiley CY - Hoboken 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 - JOUR A1 - Martí-Bonmatí, Luis A1 - Miguel, Ana A1 - Suárez, Amelia A1 - Aznar, Mario A1 - Beregi, Jean Paul A1 - Fournier, Laure A1 - Neri, Emanuele A1 - Laghi, Andrea A1 - França, Manuela A1 - Sardanelli, Francesco A1 - Penzkofer, Tobias A1 - Lambin, Philippe A1 - Blanquer, Ignacio A1 - Menzel, Marion Irene A1 - Seymour, Karine A1 - Figueiras, Sergio A1 - Krischak, Katharina A1 - Martínez, Ricard A1 - Mirsky, Yisroel A1 - Yang, Guang A1 - Alberich-Bayarri, Ángel T1 - CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools JF - Frontiers in oncology N2 - The CHAIMELEON project aims to set up a pan-European repository of health imaging data, tools and methodologies, with the ambition to set a standard and provide resources for future AI experimentation for cancer management. The project is a 4 year long, EU-funded project tackling some of the most ambitious research in the fields of biomedical imaging, artificial intelligence and cancer treatment, addressing the four types of cancer that currently have the highest prevalence worldwide: lung, breast, prostate and colorectal. To allow this, clinical partners and external collaborators will populate the repository with multimodality (MR, CT, PET/CT) imaging and related clinical data. Subsequently, AI developers will enable a multimodal analytical data engine facilitating the interpretation, extraction and exploitation of the information stored at the repository. The development and implementation of AI-powered pipelines will enable advancement towards automating data deidentification, curation, annotation, integrity securing and image harmonization. By the end of the project, the usability and performance of the repository as a tool fostering AI experimentation will be technically validated, including a validation subphase by world-class European AI developers, participating in Open Challenges to the AI Community. Upon successful validation of the repository, a set of selected AI tools will undergo early in-silico validation in observational clinical studies coordinated by leading experts in the partner hospitals. Tool performance will be assessed, including external independent validation on hallmark clinical decisions in response to some of the currently most important clinical end points in cancer. The project brings together a consortium of 18 European partners including hospitals, universities, R&D centers and private research companies, constituting an ecosystem of infrastructures, biobanks, AI/in-silico experimentation and cloud computing technologies in oncology. UR - https://doi.org/10.3389/fonc.2022.742701 KW - radiology KW - artificial intelligence-AI KW - cancer imaging KW - cancer management KW - quantitative imaging biomarkers KW - image harmonization Y1 - 2022 UR - https://doi.org/10.3389/fonc.2022.742701 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28156 SN - 2234-943X VL - 12 PB - Frontiers Media CY - Lausanne 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 - JOUR A1 - Wu, Mingming A1 - Mulder, Hendrik T. A1 - Zur, Yuval A1 - Lechner-Greite, Silke A1 - Menzel, Marion Irene A1 - Paulides, Margarethus M. A1 - Rhoon, Gerard C. van A1 - Haase, Axel T1 - A phase-cycled temperature-sensitive fast spin echo sequence with conductivity bias correction for monitoring of mild RF hyperthermia with PRFS JF - Magnetic Resonance Materials in Physics, Biology and Medicine UR - https://doi.org/10.1007/s10334-018-0725-5 KW - MR thermometry KW - Hyperthermia KW - Proton resonance frequency shift KW - Fast spin echo KW - Double echo gradient echo KW - Intervention KW - Conductivity Y1 - 2018 UR - https://doi.org/10.1007/s10334-018-0725-5 SN - 1352-8661 VL - 32 IS - 3 SP - 369 EP - 380 PB - Springer CY - Heidelberg ER - TY - JOUR A1 - Molina-Romero, Miguel A1 - Gómez, Pedro A. A1 - Sperl, Jonathan I. A1 - Czisch, Michael A1 - Sämann, Philipp G. A1 - Jones, Derek K. A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization JF - Magnetic Resonance in Medicine N2 - Purpose The compartmental nature of brain tissue microstructure is typically studied by diffusion MRI, MR relaxometry or their correlation. Diffusion MRI relies on signal representations or biophysical models, while MR relaxometry and correlation studies are based on regularized inverse Laplace transforms (ILTs). Here we introduce a general framework for characterizing microstructure that does not depend on diffusion modeling and replaces ill-posed ILTs with blind source separation (BSS). This framework yields proton density, relaxation times, volume fractions, and signal disentanglement, allowing for separation of the free-water component. Theory and Methods Diffusion experiments repeated for several different echo times, contain entangled diffusion and relaxation compartmental information. These can be disentangled by BSS using a physically constrained nonnegative matrix factorization. Results Computer simulations, phantom studies, together with repeatability and reproducibility experiments demonstrated that BSS is capable of estimating proton density, compartmental volume fractions and transversal relaxations. In vivo results proved its potential to correct for free-water contamination and to estimate tissue parameters. Conclusion Formulation of the diffusion-relaxation dependence as a BSS problem introduces a new framework for studying microstructure compartmentalization, and a novel tool for free-water elimination. UR - https://doi.org/10.1002/mrm.27181 KW - blind source separation KW - brain microstructure KW - diffusion MRI KW - free-water elimination KW - MR relaxometry KW - non-negative matrix factorization Y1 - 2018 UR - https://doi.org/10.1002/mrm.27181 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-28210 SN - 1522-2594 VL - 80 IS - 5 SP - 2155 EP - 2172 PB - Wiley CY - Hoboken ER - TY - CHAP A1 - Molina-Romero, Miguel A1 - Wiestler, Benedikt A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases T2 - Medical Image Computing and Computer Assisted Intervention – MICCAI 2018 UR - https://doi.org/10.1007/978-3-030-00931-1_12 KW - Glioblastoma KW - Brain tumor KW - DTI KW - Deep learning KW - Free-water elimination KW - Data harmonization KW - Fractional anisotropy Y1 - 2018 UR - https://doi.org/10.1007/978-3-030-00931-1_12 SN - 978-3-030-00931-1 SN - 978-3-030-00930-4 SP - 98 EP - 106 PB - Springer CY - Cham ER - TY - CHAP A1 - Chen, Dongdong A1 - Golbabaee, Mohammad A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. ED - Cardoso, M. Jorge ED - Feragen, Aasa ED - Glocker, Ben ED - Konukoglu, Ender ED - Oguz, Ipek ED - Unal, Gozde ED - Vercauteren, Tom T1 - Deep Fully Convolutional Network for MR Fingerprinting T2 - Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track N2 - This work proposes an end-to-end deep fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employs linear dimensionality reduction and then uses a neural network to project the data into the tissue parameters. The MRF dictionary is only used for training the network and not during image reconstruction. We show that MRF-FCNN is capable of achieving accuracy comparable to the ground-truth maps thanks to capturing spatio-temporal data structures without a need for the non-scalable dictionary matching step used in the baseline reconstructions. KW - Magnetic Resonance Fingerprinting KW - Deep Learning Y1 - 2019 UR - https://openreview.net/forum?id=SJxUdvJTtN UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31163 ER - TY - CHAP A1 - Golbabaee, Mohammad A1 - Chen, Dongdong A1 - Davies, Mike E. A1 - Menzel, Marion Irene A1 - Gómez, Pedro A. ED - Cardoso, M. Jorge ED - Feragen, Aasa ED - Glocker, Ben ED - Konukoglu, Ender ED - Oguz, Ipek ED - Unal, Gozde ED - Vercauteren, Tom T1 - Spatio-temporal regularization for deep MR Fingerprinting T2 - Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track N2 - We study a deep learning approach to address the heavy storage and computation re- quirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprint- ing (MRF) reconstruction. The MRF-Net provides a piece-wise affine approximation to the (temporal) Bloch response manifold projection. Fed with non-iterated back-projected images, the network alone is unable to fully resolve spatially-correlated artefacts which ap- pear in highly undersampling regimes. We propose an accelerated iterative reconstruction to minimize these artefacts before feeding into the network. This is done through a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series. KW - magnetic resonance fingerprinting KW - deep learning KW - regularisation Y1 - 2019 UR - https://openreview.net/forum?id=ryx64UL6YE UR - https://2019.midl.io/program/extended-abstracts.html UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31174 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 - 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 - INPR A1 - Chen, Dongdong A1 - Golbabaee, Mohammad A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. T1 - A Fully Convolutional Network for MR Fingerprinting UR - https://doi.org/10.48550/arXiv.1911.09846 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1911.09846 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Benjamin, Arnold Julian Vinoj A1 - Gómez, Pedro A. A1 - Golbabaee, Mohammad A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Davies, Mike E. A1 - Marshall, Ian T1 - Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF UR - https://doi.org/10.48550/arXiv.1809.02506 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.1809.02506 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Benjamin, Arnold Julian Vinoj A1 - Gómez, Pedro A. A1 - Golbabaee, Mohammad A1 - Bin Mahbub, Zaid A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Davies, Mike E. A1 - Marshall, Ian T1 - Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: an alternative to conventional spiral MR Fingerprinting UR - https://doi.org/10.48550/arXiv.1906.08195 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1906.08195 PB - arXiv CY - Ithaca ER - TY - JOUR 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 segmentation multi-task network JF - Physics in Medicine & Biology UR - https://doi.org/10.1088/1361-6560/acefa3 Y1 - 2023 UR - https://doi.org/10.1088/1361-6560/acefa3 SN - 0031-9155 SN - 1361-6560 VL - 68 IS - 19 PB - IOP Publishing CY - Bristol ER - TY - CHAP A1 - Golkov, Vladimir A1 - Sprenger, Tim A1 - Sperl, Jonathan I. A1 - Menzel, Marion Irene A1 - Czisch, Michael A1 - Sämann, Philipp G. A1 - Cremers, Daniel T1 - Model-free novelty-based diffusion MRI T2 - 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) UR - https://doi.org/10.1109/ISBI.2016.7493489 Y1 - 2016 UR - https://doi.org/10.1109/ISBI.2016.7493489 SN - 978-1-4799-2349-6 SP - 1233 EP - 1236 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Golkov, Vladimir A1 - Dosovitskiy, Alexey A1 - Sperl, Jonathan I. A1 - Menzel, Marion Irene A1 - Czisch, Michael A1 - Sämann, Philipp G. A1 - Brox, Thomas A1 - Cremers, Daniel T1 - q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans JF - IEEE Transactions on Medical Imaging UR - https://doi.org/10.1109/TMI.2016.2551324 Y1 - 2016 UR - https://doi.org/10.1109/TMI.2016.2551324 SN - 1558-254X SN - 0278-0062 VL - 35 IS - 5 SP - 1344 EP - 1351 PB - IEEE CY - New York ER - TY - INPR A1 - Golbabaee, Mohammad A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Buonincontri, Guido A1 - Gómez, Pedro A. T1 - Deep MR Fingerprinting with total-variation and low-rank subspace priors UR - https://doi.org/10.48550/arXiv.1902.10205 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1902.10205 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 - Gómez, Pedro A. A1 - Sperl, Jonathan I. A1 - Sprenger, Tim A1 - Metzler-Baddeley, Claudia A1 - Jones, Derek K. A1 - Saemann, Philipp A1 - Czisch, Michael A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. ED - Handels, Heinz ED - Deserno, Thomas Martin ED - Meinzer, Hans-Peter ED - Tolxdorff, Thomas T1 - Joint Reconstruction of Multi-Contrast MRI for Multiple Sclerosis Lesion Segmentation T2 - Bildverarbeitung für die Medizin 2015, Algorithmen – Systeme – Anwendungen, Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck UR - https://doi.org/10.1007/978-3-662-46224-9_28 Y1 - 2015 UR - https://doi.org/10.1007/978-3-662-46224-9_28 SN - 978-3-662-46224-9 SN - 978-3-662-46223-2 N1 - Die vollständige Angabe der Autorinnen und Autoren findet sich im PDF. SP - 155 EP - 160 PB - Springer Vieweg CY - Berlin ER - TY - INPR A1 - Golbabaee, Mohammad A1 - Chen, Dongdong A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. T1 - Geometry of Deep Learning for Magnetic Resonance Fingerprinting UR - https://doi.org/10.48550/arXiv.1809.01749 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.1809.01749 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Gómez, Pedro A. A1 - Molina-Romero, Miguel A1 - Buonincontri, Guido A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging UR - https://doi.org/10.48550/arXiv.1901.07800 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1901.07800 PB - arXiv CY - Ithaca ER - TY - INPR 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 auto-encoders UR - https://doi.org/10.48550/arXiv.2001.08746 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2001.08746 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 - CHAP A1 - Krajsek, Kai A1 - Menzel, Marion Irene A1 - Scharr, Hanno T1 - Riemannian Bayesian estimation of diffusion tensor images T2 - 2009 IEEE 12th International Conference on Computer Vision UR - https://doi.org/10.1109/ICCV.2009.5459431 Y1 - 2009 UR - https://doi.org/10.1109/ICCV.2009.5459431 SN - 978-1-4244-4420-5 SP - 2327 EP - 2334 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Menzel, Marion Irene A1 - Oros‐Peusquens, Ana‐Maria A1 - Pohlmeier, Andreas A1 - Shah, N. Jon A1 - Schurr, Ulrich A1 - Schneider, Heike U. T1 - Comparing 1H-NMR imaging and relaxation mapping of German white asparagus from five different cultivation sites JF - Journal of Plant Nutrition and Soil Science UR - https://doi.org/https://doi.org/10.1002/jpln.200625114 Y1 - 2007 UR - https://doi.org/https://doi.org/10.1002/jpln.200625114 SN - 1522-2624 SN - 1436-8730 VL - 170 IS - 1 SP - 24 EP - 38 PB - Wiley CY - Weinheim ER - TY - JOUR A1 - Menzel, Marion Irene A1 - Blümich, Bernhard T1 - NMR: still listening to whispering hydrogens? What else do they tell us 50 years after their discovery? JF - Spectroscopy Europe Y1 - 2002 UR - https://www.spectroscopyeurope.com/article/nmr-still-listening-whispering-hydrogens-what-else-do-they-tell-us-50-years-after-their?page=2 SN - 2634-2561 SN - 0966-0941 VL - 14 IS - 4 PB - IMP Open CY - Chichester ER - TY - CHAP A1 - Kudielka, Guido P. A1 - Kluge, Thomas A1 - Shair, Sultan A1 - Menzel, Marion Irene T1 - Magnetic Resonance Imaging for 3D Resin Flow and Curing Process Monitoring T2 - CAMX, the Composites and Advanced Materials Expo, October 26-29, 2015: Conference / October 27-29, 2015: Exhibits, Dallas, Texas USA / Dallas Convention Center Y1 - 2015 UR - https://www.nasampe.org/store/viewproduct.aspx?id=5477796 SN - 978-1-934551-20-2 SP - 144 EP - 154 PB - ACMA CY - Arlington ER - TY - JOUR A1 - Lacerda, Luis M. A1 - Sperl, Jonathan I. A1 - Menzel, Marion Irene A1 - Sprenger, Tim A1 - Barker, Gareth J. A1 - Dell'Acqua, Flavio T1 - Diffusion in realistic biophysical systems can lead to aliasing effects in diffusion spectrum imaging JF - Magnetic Resonance in Medicine N2 - Purpose Diffusion spectrum imaging (DSI) is an imaging technique that has been successfully applied to resolve white matter crossings in the human brain. However, its accuracy in complex microstructure environments has not been well characterized. Theory and Methods Here we have simulated different tissue configurations, sampling schemes, and processing steps to evaluate DSI performances' under realistic biophysical conditions. A novel approach to compute the orientation distribution function (ODF) has also been developed to include biophysical constraints, namely integration ranges compatible with axial fiber diffusivities. Results Performed simulations identified several DSI configurations that consistently show aliasing artifacts caused by fast diffusion components for both isotropic diffusion and fiber configurations. The proposed method for ODF computation showed some improvement in reducing such artifacts and improving the ability to resolve crossings, while keeping the quantitative nature of the ODF. Conclusion In this study, we identified an important limitation of current DSI implementations, specifically the presence of aliasing due to fast diffusion components like those from pathological tissues, which are not well characterized, and can lead to artifactual fiber reconstructions. To minimize this issue, a new way of computing the ODF was introduced, which removes most of these artifacts and offers improved angular resolution. UR - https://doi.org/10.1002/mrm.26080 Y1 - 2015 UR - https://doi.org/10.1002/mrm.26080 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-44426 SN - 1522-2594 SN - 0740-3194 VL - 76 IS - 6 SP - 1837 EP - 1847 PB - Wiley CY - Hoboken ER - TY - CHAP A1 - Ulas, Cagdas A1 - Gómez, Pedro A. A1 - Krahmer, Felix A1 - Sperl, Jonathan I. A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. ED - Zuluaga, Maria A. ED - Bhatia, Kanwal ED - Kainz, Bernhard ED - Moghari, Mehdi H. ED - Pace, Danielle F. T1 - Robust Reconstruction of Accelerated Perfusion MRI Using Local and Nonlocal Constraints T2 - Reconstruction, Segmentation, and Analysis of Medical Images, First International Workshops, RAMBO 2016 and HVSMR 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Revised Selected Papers UR - https://doi.org/10.1007/978-3-319-52280-7_4 Y1 - 2017 UR - https://doi.org/10.1007/978-3-319-52280-7_4 SN - 978-3-319-52280-7 SN - 978-3-319-52279-1 SP - 37 EP - 47 PB - Springer CY - Cham ER - TY - JOUR A1 - Tan, Ek Tsoon A1 - Marinelli, Luca A1 - Sperl, Jonathan I. A1 - Menzel, Marion Irene A1 - Hardy, Christopher J. T1 - Multi‐directional anisotropy from diffusion orientation distribution functions JF - Journal of Magnetic Resonance Imaging UR - https://doi.org/10.1002/jmri.24589 Y1 - 2014 UR - https://doi.org/10.1002/jmri.24589 SN - 1522-2586 SN - 1053-1807 VL - 41 IS - 3 SP - 841 EP - 850 PB - Wiley CY - New York ER -