@article{PirklNunezGonzalezKofleretal.2021, author = {Pirkl, Carolin and Nunez-Gonzalez, Laura and Kofler, Florian and Endt, Sebastian and Grundl, Lioba and Golbabaee, Mohammad and G{\´o}mez, Pedro A. and Cencini, Matteo and Buonincontri, Guido and Schulte, Rolf F. and Smits, Marion and Wiestler, Benedikt and Menze, Bjoern H. and Menzel, Marion Irene and Hernandez-Tamames, Juan A.}, title = {Accelerated 3D whole-brain T1, T2, and proton density mapping}, volume = {63}, journal = {Neuroradiology}, subtitle = {feasibility for clinical glioma MR imaging}, number = {11}, publisher = {Springer}, address = {Berlin}, doi = {https://doi.org/10.1007/s00234-021-02703-0}, pages = {1831 -- 1851}, year = {2021}, abstract = {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.}, language = {en} } @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} } @article{SperlSprengerTanetal.2017, author = {Sperl, Jonathan I. and Sprenger, Tim and Tan, Ek Tsoon and Menzel, Marion Irene and Hardy, Christopher J. and Marinelli, Luca}, title = {Model-based denoising in diffusion-weighted imaging using generalized spherical deconvolution}, volume = {78}, journal = {Magnetic Resonance in Medicine}, number = {6}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.26626}, pages = {2428 -- 2438}, year = {2017}, language = {en} } @article{FeuereckerDurstMichaliketal.2017, author = {Feuerecker, Benedikt and Durst, Markus and Michalik, Michael and Schneider, G{\"u}nter and Saur, Dieter and Menzel, Marion Irene and Schwaiger, Markus and Schilling, Franz}, title = {Hyperpolarized 13C Diffusion MRS of Co-Polarized Pyruvate and Fumarate to Measure Lactate Export and Necrosis}, volume = {8}, journal = {Journal of Cancer}, number = {15}, publisher = {Ivyspring International Publisher}, address = {Sydney}, issn = {1837-9664}, doi = {https://doi.org/10.7150/jca.20250}, pages = {3078 -- 3085}, year = {2017}, abstract = {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.}, language = {en} } @article{GomezDamianSperlJanichetal.2014, author = {G{\´o}mez Dami{\´a}n, Pedro A. and Sperl, Jonathan I. and Janich, Martin A. and Khegai, Oleksandr and Wiesinger, Florian and Glaser, Steffen J. and Haase, Axel and Schwaiger, Markus and Schulte, Rolf F. and Menzel, Marion Irene}, title = {Multisite Kinetic Modeling of 13C Metabolic MR Using [1-13C]Pyruvate}, volume = {2014}, pages = {871619}, journal = {Radiology Research and Practice}, publisher = {Hindawi}, address = {New York}, issn = {2090-195X}, doi = {https://doi.org/10.1155/2014/871619}, year = {2014}, abstract = {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.}, 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} } @article{NanDelSerWalshetal.2022, author = {Nan, Yang and Del Ser, Javier and Walsh, Simon and Sch{\"o}nlieb, Carola and Roberts, Michael and Selby, Ian and Howard, Kit and Owen, John and Neville, Jon and Guiot, Julien and Ernst, Benoit and Pastor, Ana and Alberich-Bayarri, Angel and Menzel, Marion Irene and Walsh, Sean and Vos, Wim and Flerin, Nina and Charbonnier, Jean-Paul and Rikxoort, Eva van and Chatterjee, Avishek and Woodruff, Henry and Lambin, Philippe and Cerd{\´a}-Alberich, Leonor and Mart{\´i}-Bonmat{\´i}, Luis and Herrera, Francisco and Yang, Guang}, title = {Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions}, volume = {2022}, journal = {Information Fusion}, number = {82}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1566-2535}, doi = {https://doi.org/10.1016/j.inffus.2022.01.001}, pages = {99 -- 122}, year = {2022}, abstract = {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}, language = {en} } @article{LiuGomezSolanaetal.2020, author = {Liu, Xin and G{\´o}mez, Pedro A. and Solana, Ana Beatriz and Wiesinger, Florian and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Silent 3D MR sequence for quantitative and multicontrast T1 and proton density imaging}, volume = {65}, pages = {185010}, journal = {Physics in Medicine \& Biology}, number = {18}, publisher = {IOP Publishing}, address = {Bristol}, issn = {1361-6560}, doi = {https://doi.org/10.1088/1361-6560/aba5e8}, year = {2020}, abstract = {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.}, language = {en} } @article{CoelloHafalirNoeskeetal.2019, author = {Coello, Eduardo and Hafalir, Fatih S. and Noeske, Ralph and Menzel, Marion Irene and Haase, Axel and Menze, Bjoern H. and Schulte, Rolf F.}, title = {Overdiscrete echo-planar spectroscopic imaging with correlated higher-order phase correction}, volume = {84}, journal = {Magnetic Resonance in Medicine}, number = {1}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.28105}, pages = {11 -- 24}, year = {2019}, language = {en} } @article{BenjaminGomezGolbabaeeetal.2019, author = {Benjamin, Arnold Julian Vinoj and G{\´o}mez, Pedro A. and Golbabaee, Mohammad and Bin Mahbub, Zaid and Sprenger, Tim and Menzel, Marion Irene and Davies, Mike E. and Marshall, Ian}, title = {Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting}, volume = {2019}, journal = {Magnetic Resonance Imaging}, number = {61}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0730-725X}, doi = {https://doi.org/10.1016/j.mri.2019.04.014}, pages = {20 -- 32}, year = {2019}, language = {en} } @article{GomezMolinaRomeroBuonincontrietal.2019, author = {G{\´o}mez, Pedro A. and Molina-Romero, Miguel and Buonincontri, Guido and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging}, volume = {9}, pages = {8468}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-019-44832-w}, year = {2019}, abstract = {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.}, language = {en} } @inproceedings{GolbabaeeChenGomezetal.2019, author = {Golbabaee, Mohammad and Chen, Dongdong and G{\´o}mez, Pedro A. and Menzel, Marion Irene and Davies, Mike E.}, title = {Geometry of Deep Learning for Magnetic Resonance Fingerprinting}, booktitle = {ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-5386-4658-8}, doi = {https://doi.org/10.1109/ICASSP.2019.8683549}, pages = {7825 -- 7829}, year = {2019}, language = {en} } @article{GolbabaeeBuonincontriPirkletal.2020, author = {Golbabaee, Mohammad and Buonincontri, Guido and Pirkl, Carolin and Menzel, Marion Irene and Menze, Bjoern H. and Davies, Mike E. and G{\´o}mez, Pedro A.}, title = {Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks}, volume = {2021}, pages = {101945}, journal = {Medical Image Analysis}, number = {69}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1361-8415}, doi = {https://doi.org/10.1016/j.media.2020.101945}, year = {2020}, language = {en} } @article{WuMulderBaronetal.2020, author = {Wu, Mingming and Mulder, Hendrik T. and Baron, Paul and Coello, Eduardo and Menzel, Marion Irene and van Rhoon, Gerard C. and Haase, Axel}, title = {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}, volume = {84}, journal = {Magnetic Resonance in Medicine}, number = {5}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.28302}, pages = {2495 -- 2511}, year = {2020}, abstract = {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.}, language = {en} } @article{PirklCenciniKurzawskietal.2022, author = {Pirkl, Carolin and Cencini, Matteo and Kurzawski, Jan W. and Waldmannstetter, Diana and Li, Hongwei and Menzel, Marion Irene}, title = {Learning residual motion correction for fast and robust 3D multiparametric MRI}, volume = {2022}, pages = {102387}, journal = {Medical Image Analysis}, number = {77}, publisher = {Elsevier}, address = {Amsterdam}, issn = {1361-8423}, doi = {https://doi.org/10.1016/j.media.2022.102387}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{FataniaPirklMenzeletal.2022, author = {Fatania, Ketan and Pirkl, Carolin and Menzel, Marion Irene and Hall, Peter and Golbabaee, Mohammad}, title = {A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers}, booktitle = {Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-6654-2923-8}, doi = {https://doi.org/10.1109/ISBI52829.2022.9761603}, year = {2022}, language = {en} } @article{MartiBonmatiMiguelSuarezetal.2022, author = {Mart{\´i}-Bonmat{\´i}, Luis and Miguel, Ana and Su{\´a}rez, Amelia and Aznar, Mario and Beregi, Jean Paul and Fournier, Laure and Neri, Emanuele and Laghi, Andrea and Fran{\c{c}}a, Manuela and Sardanelli, Francesco and Penzkofer, Tobias and Lambin, Philippe and Blanquer, Ignacio and Menzel, Marion Irene and Seymour, Karine and Figueiras, Sergio and Krischak, Katharina and Mart{\´i}nez, Ricard and Mirsky, Yisroel and Yang, Guang and Alberich-Bayarri, {\´A}ngel}, title = {CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools}, volume = {12}, pages = {742701}, journal = {Frontiers in oncology}, publisher = {Frontiers Media}, address = {Lausanne}, issn = {2234-943X}, doi = {https://doi.org/10.3389/fonc.2022.742701}, year = {2022}, abstract = {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.}, language = {en} } @inproceedings{PirklCenciniKurzawskietal.2021, author = {Pirkl, Carolin and Cencini, Matteo and Kurzawski, Jan W. and Waldmannstetter, Diana and Li, Hongwei and Sekuboyina, Anjany and Endt, Sebastian and Peretti, Luca and Donatelli, Graziella and Pasquariello, Rosa and Costagli, Mauro and Buonincontri, Guido and Tosetti, Michela and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Residual learning for 3D motion corrected quantitative MRI}, booktitle = {Medical Imaging with Deep Learning MIDL 2021}, subtitle = {Robust clinical T1, T2 and proton density mapping}, publisher = {OpenReview}, url = {https://openreview.net/forum?id=hxgQM71AuRA}, year = {2021}, language = {en} } @inproceedings{PirklGomezLippetal.2020, author = {Pirkl, Carolin and G{\´o}mez, Pedro A. and Lipp, Ilona and Buonincontri, Guido and Molina-Romero, Miguel and Sekuboyina, Anjany and Waldmannstetter, Diana and Dannenberg, Jonathan and Endt, Sebastian and Merola, Alberto and Whittaker, Joseph R. and Tomassini, Valentina and Tosetti, Michela and Jones, Derek K. and Menze, Bjoern H. and Menzel, Marion Irene}, title = {Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting}, booktitle = {Proceedings of Machine Learning Research}, number = {121}, publisher = {PMLR}, address = {[s. l.]}, issn = {2640-3498}, url = {https://proceedings.mlr.press/v121/pirk20a.html}, pages = {639 -- 654}, year = {2020}, language = {en} } @article{WuMulderZuretal.2018, author = {Wu, Mingming and Mulder, Hendrik T. and Zur, Yuval and Lechner-Greite, Silke and Menzel, Marion Irene and Paulides, Margarethus M. and Rhoon, Gerard C. van and Haase, Axel}, title = {A phase-cycled temperature-sensitive fast spin echo sequence with conductivity bias correction for monitoring of mild RF hyperthermia with PRFS}, volume = {32}, journal = {Magnetic Resonance Materials in Physics, Biology and Medicine}, number = {3}, publisher = {Springer}, address = {Heidelberg}, issn = {1352-8661}, doi = {https://doi.org/10.1007/s10334-018-0725-5}, pages = {369 -- 380}, year = {2018}, language = {en} } @article{MolinaRomeroGomezSperletal.2018, author = {Molina-Romero, Miguel and G{\´o}mez, Pedro A. and Sperl, Jonathan I. and Czisch, Michael and S{\"a}mann, Philipp G. and Jones, Derek K. and Menzel, Marion Irene and Menze, Bjoern H.}, title = {A diffusion model-free framework with echo time dependence for free-water elimination and brain tissue microstructure characterization}, volume = {80}, journal = {Magnetic Resonance in Medicine}, number = {5}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.27181}, pages = {2155 -- 2172}, year = {2018}, abstract = {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.}, language = {en} } @inproceedings{MolinaRomeroWiestlerGomezetal.2018, author = {Molina-Romero, Miguel and Wiestler, Benedikt and G{\´o}mez, Pedro A. and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Deep Learning with Synthetic Diffusion MRI Data for Free-Water Elimination in Glioblastoma Cases}, booktitle = {Medical Image Computing and Computer Assisted Intervention - MICCAI 2018}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-00931-1}, doi = {https://doi.org/10.1007/978-3-030-00931-1_12}, pages = {98 -- 106}, year = {2018}, language = {en} } @inproceedings{ChenGolbabaeeGomezetal.2019, author = {Chen, Dongdong and Golbabaee, Mohammad and G{\´o}mez, Pedro A. and Menzel, Marion Irene and Davies, Mike E.}, title = {Deep Fully Convolutional Network for MR Fingerprinting}, booktitle = {Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, url = {https://openreview.net/forum?id=SJxUdvJTtN}, year = {2019}, abstract = {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.}, language = {en} } @inproceedings{GolbabaeeChenDaviesetal.2019, author = {Golbabaee, Mohammad and Chen, Dongdong and Davies, Mike E. and Menzel, Marion Irene and G{\´o}mez, Pedro A.}, title = {Spatio-temporal regularization for deep MR Fingerprinting}, booktitle = {Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track}, editor = {Cardoso, M. Jorge and Feragen, Aasa and Glocker, Ben and Konukoglu, Ender and Oguz, Ipek and Unal, Gozde and Vercauteren, Tom}, url = {https://openreview.net/forum?id=ryx64UL6YE}, year = {2019}, abstract = {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.}, language = {en} } @unpublished{KaushikBylundCozzinietal.2022, 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 Classification Multi-task Network}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2203.16288}, year = {2022}, language = {en} } @unpublished{FataniaChauPirkletal.2022, 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}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2211.12786}, year = {2022}, language = {en} } @unpublished{ChenGolbabaeeGomezetal.2019, author = {Chen, Dongdong and Golbabaee, Mohammad and G{\´o}mez, Pedro A. and Menzel, Marion Irene and Davies, Mike E.}, title = {A Fully Convolutional Network for MR Fingerprinting}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1911.09846}, year = {2019}, language = {en} } @unpublished{BenjaminGomezGolbabaeeetal.2018, author = {Benjamin, Arnold Julian Vinoj and G{\´o}mez, Pedro A. and Golbabaee, Mohammad and Sprenger, Tim and Menzel, Marion Irene and Davies, Mike E. and Marshall, Ian}, title = {Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1809.02506}, year = {2018}, language = {en} } @unpublished{BenjaminGomezGolbabaeeetal.2019, author = {Benjamin, Arnold Julian Vinoj and G{\´o}mez, Pedro A. and Golbabaee, Mohammad and Bin Mahbub, Zaid and Sprenger, Tim and Menzel, Marion Irene and Davies, Mike E. and Marshall, Ian}, title = {Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: an alternative to conventional spiral MR Fingerprinting}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1906.08195}, year = {2019}, 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{GolkovSprengerSperletal.2016, author = {Golkov, Vladimir and Sprenger, Tim and Sperl, Jonathan I. and Menzel, Marion Irene and Czisch, Michael and S{\"a}mann, Philipp G. and Cremers, Daniel}, title = {Model-free novelty-based diffusion MRI}, booktitle = {2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4799-2349-6}, doi = {https://doi.org/10.1109/ISBI.2016.7493489}, pages = {1233 -- 1236}, year = {2016}, language = {en} } @article{GolkovDosovitskiySperletal.2016, author = {Golkov, Vladimir and Dosovitskiy, Alexey and Sperl, Jonathan I. and Menzel, Marion Irene and Czisch, Michael and S{\"a}mann, Philipp G. and Brox, Thomas and Cremers, Daniel}, title = {q-Space Deep Learning: Twelve-Fold Shorter and Model-Free Diffusion MRI Scans}, volume = {35}, journal = {IEEE Transactions on Medical Imaging}, number = {5}, publisher = {IEEE}, address = {New York}, issn = {1558-254X}, doi = {https://doi.org/10.1109/TMI.2016.2551324}, pages = {1344 -- 1351}, year = {2016}, language = {en} } @unpublished{GolbabaeePirklMenzeletal.2019, author = {Golbabaee, Mohammad and Pirkl, Carolin and Menzel, Marion Irene and Buonincontri, Guido and G{\´o}mez, Pedro A.}, title = {Deep MR Fingerprinting with total-variation and low-rank subspace priors}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1902.10205}, year = {2019}, 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} } @unpublished{FataniaPirklMenzeletal.2022, author = {Fatania, Ketan and Pirkl, Carolin and Menzel, Marion Irene and Hall, Peter and Golbabaee, Mohammad}, title = {A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2202.05269}, year = {2022}, language = {en} } @inproceedings{GomezSperlSprengeretal.2015, author = {G{\´o}mez, Pedro A. and Sperl, Jonathan I. and Sprenger, Tim and Metzler-Baddeley, Claudia and Jones, Derek K. and Saemann, Philipp and Czisch, Michael and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Joint Reconstruction of Multi-Contrast MRI for Multiple Sclerosis Lesion Segmentation}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2015, Algorithmen - Systeme - Anwendungen, Proceedings des Workshops vom 15. bis 17. M{\"a}rz 2015 in L{\"u}beck}, editor = {Handels, Heinz and Deserno, Thomas Martin and Meinzer, Hans-Peter and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Berlin}, isbn = {978-3-662-46224-9}, doi = {https://doi.org/10.1007/978-3-662-46224-9_28}, pages = {155 -- 160}, year = {2015}, language = {en} } @unpublished{GolbabaeeChenGomezetal.2018, author = {Golbabaee, Mohammad and Chen, Dongdong and G{\´o}mez, Pedro A. and Menzel, Marion Irene and Davies, Mike E.}, title = {Geometry of Deep Learning for Magnetic Resonance Fingerprinting}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1809.01749}, year = {2018}, language = {en} } @unpublished{GomezMolinaRomeroBuonincontrietal.2019, author = {G{\´o}mez, Pedro A. and Molina-Romero, Miguel and Buonincontri, Guido and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Designing contrasts for rapid, simultaneous parameter quantification and flow visualization with quantitative transient-state imaging}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.1901.07800}, year = {2019}, language = {en} } @unpublished{GolbabaeeBuonincontriPirkletal.2020, author = {Golbabaee, Mohammad and Buonincontri, Guido and Pirkl, Carolin and Menzel, Marion Irene and Menze, Bjoern H. and Davies, Mike E. and G{\´o}mez, Pedro A.}, title = {Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2001.08746}, year = {2020}, language = {en} } @inproceedings{MayoCenciniPirkletal.2024, author = {Mayo, Perla and Cencini, Matteo and Pirkl, Carolin and Menzel, Marion Irene and Tosetti, Michela and Menze, Bjoern H. and Golbabaee, Mohammad}, title = {StoDIP: Efficient 3D MRF Image Reconstruction with Deep Image Priors and Stochastic Iterations}, booktitle = {Machine Learning in Medical Imaging: 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings, Part II}, editor = {XU, Xuanang and Cui, Zhiming and Rekik, Islem and Ouyang, Xi and Sun, Kaicong}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-73290-4}, doi = {https://doi.org/10.1007/978-3-031-73290-4_13}, pages = {128 -- 137}, year = {2024}, language = {en} } @inproceedings{KrajsekMenzelScharr2009, author = {Krajsek, Kai and Menzel, Marion Irene and Scharr, Hanno}, title = {Riemannian Bayesian estimation of diffusion tensor images}, booktitle = {2009 IEEE 12th International Conference on Computer Vision}, publisher = {IEEE}, address = {Piscataway}, isbn = {978-1-4244-4420-5}, doi = {https://doi.org/10.1109/ICCV.2009.5459431}, pages = {2327 -- 2334}, year = {2009}, language = {en} } @article{MenzelOros‐PeusquensPohlmeieretal.2007, author = {Menzel, Marion Irene and Oros-Peusquens, Ana-Maria and Pohlmeier, Andreas and Shah, N. Jon and Schurr, Ulrich and Schneider, Heike U.}, title = {Comparing 1H-NMR imaging and relaxation mapping of German white asparagus from five different cultivation sites}, volume = {170}, journal = {Journal of Plant Nutrition and Soil Science}, number = {1}, publisher = {Wiley}, address = {Weinheim}, issn = {1522-2624}, doi = {https://doi.org/https://doi.org/10.1002/jpln.200625114}, pages = {24 -- 38}, year = {2007}, language = {en} } @article{MenzelBluemich2002, author = {Menzel, Marion Irene and Bl{\"u}mich, Bernhard}, title = {NMR: still listening to whispering hydrogens? What else do they tell us 50 years after their discovery?}, volume = {14}, journal = {Spectroscopy Europe}, number = {4}, publisher = {IMP Open}, address = {Chichester}, issn = {2634-2561}, url = {https://www.spectroscopyeurope.com/article/nmr-still-listening-whispering-hydrogens-what-else-do-they-tell-us-50-years-after-their?page=2}, year = {2002}, language = {en} } @inproceedings{KudielkaKlugeShairetal.2015, author = {Kudielka, Guido P. and Kluge, Thomas and Shair, Sultan and Menzel, Marion Irene}, title = {Magnetic Resonance Imaging for 3D Resin Flow and Curing Process Monitoring}, booktitle = {CAMX, the Composites and Advanced Materials Expo, October 26-29, 2015: Conference / October 27-29, 2015: Exhibits, Dallas, Texas USA / Dallas Convention Center}, publisher = {ACMA}, address = {Arlington}, isbn = {978-1-934551-20-2}, url = {https://www.nasampe.org/store/viewproduct.aspx?id=5477796}, pages = {144 -- 154}, year = {2015}, language = {en} } @article{LacerdaSperlMenzeletal.2015, author = {Lacerda, Luis M. and Sperl, Jonathan I. and Menzel, Marion Irene and Sprenger, Tim and Barker, Gareth J. and Dell'Acqua, Flavio}, title = {Diffusion in realistic biophysical systems can lead to aliasing effects in diffusion spectrum imaging}, volume = {76}, journal = {Magnetic Resonance in Medicine}, number = {6}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.26080}, pages = {1837 -- 1847}, year = {2015}, abstract = {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.}, language = {en} } @inproceedings{UlasGomezKrahmeretal.2017, author = {Ulas, Cagdas and G{\´o}mez, Pedro A. and Krahmer, Felix and Sperl, Jonathan I. and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Robust Reconstruction of Accelerated Perfusion MRI Using Local and Nonlocal Constraints}, booktitle = {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}, editor = {Zuluaga, Maria A. and Bhatia, Kanwal and Kainz, Bernhard and Moghari, Mehdi H. and Pace, Danielle F.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-52280-7}, doi = {https://doi.org/10.1007/978-3-319-52280-7_4}, pages = {37 -- 47}, year = {2017}, language = {en} } @article{TanMarinelliSperletal.2014, author = {Tan, Ek Tsoon and Marinelli, Luca and Sperl, Jonathan I. and Menzel, Marion Irene and Hardy, Christopher J.}, title = {Multi-directional anisotropy from diffusion orientation distribution functions}, volume = {41}, journal = {Journal of Magnetic Resonance Imaging}, number = {3}, publisher = {Wiley}, address = {New York}, issn = {1522-2586}, doi = {https://doi.org/10.1002/jmri.24589}, pages = {841 -- 850}, year = {2014}, language = {en} } @article{SprengerSperlFernandezetal.2016, author = {Sprenger, Tim and Sperl, Jonathan I. and Fernandez, Brice and Haase, Axel and Menzel, Marion Irene}, title = {Real valued diffusion-weighted imaging using decorrelated phase filtering}, volume = {77}, journal = {Magnetic Resonance in Medicine}, number = {2}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.26138}, pages = {559 -- 570}, year = {2016}, language = {en} } @article{SprengerSperlFernandezetal.2016, author = {Sprenger, Tim and Sperl, Jonathan I. and Fernandez, Brice and Golkov, Vladimir and Eidner, Ines and S{\"a}mann, Philipp G. and Czisch, Michael and Tan, Ek Tsoon and Hardy, Christopher J. and Marinelli, Luca and Haase, Axel and Menzel, Marion Irene}, title = {Bias and precision analysis of diffusional kurtosis imaging for different acquisition schemes}, volume = {76}, journal = {Magnetic Resonance in Medicine}, number = {6}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.26008}, pages = {1684 -- 1696}, year = {2016}, language = {en} } @article{KoellischLaustsenNorlingeretal.2016, author = {Koellisch, Ulrich and Laustsen, Christoffer and N{\o}rlinger, Thomas S. and {\O}stergaard, Jakob A. and Flyvbjerg, Allan and Gringeri, Concetta V. and Menzel, Marion Irene and Schulte, Rolf F. and Haase, Axel and St{\o}dkilde-J{\o}rgensen, Hans}, title = {Current state-of-the-art hyperpolarized 13C-acetate-to-acetylcarnitine imaging is not indicative of the alteredbalance between glucose and fatty acid utilizationassociated with diabetes}, volume = {4}, pages = {e12975}, journal = {Physiological Reports}, number = {17}, publisher = {Wiley}, address = {New York}, issn = {2051-817X}, doi = {https://doi.org/10.14814/phy2.12975}, year = {2016}, language = {en} } @article{ScholzJanichKoellischetal.2014, author = {Scholz, David Johannes and Janich, Martin A. and Koellisch, Ulrich and Schulte, Rolf F. and Ardenkjaer-Larsen, Jan H. and Frank, Annette and Haase, Axel and Schwaiger, Markus and Menzel, Marion Irene}, title = {Quantified pH imaging with hyperpolarized 13C-bicarbonate}, volume = {73}, journal = {Magnetic Resonance in Medicine}, number = {6}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.25357}, pages = {2274 -- 2282}, year = {2014}, language = {en} } @article{KubalaMunozAlvarezToppingetal.2016, author = {Kubala, Eugen and Mu{\~n}oz-{\´A}lvarez, Kim A. and Topping, Geoffrey and Hundshammer, Christian and Feuerecker, Benedikt and G{\´o}mez, Pedro A. and Pariani, Giorgio and Schilling, Franz and Glaser, Steffen J. and Schulte, Rolf F. and Menzel, Marion Irene and Schwaiger, Markus}, title = {Hyperpolarized 13C Metabolic Magnetic Resonance Spectroscopy and Imaging}, volume = {2016}, pages = {e54751}, journal = {Journal of Visualized Experiments}, number = {118}, publisher = {MyJoVE Corporation}, address = {Cambridge}, issn = {1940-087X}, doi = {https://doi.org/10.3791/54751}, year = {2016}, language = {en} } @inbook{KubalaMenzelFeuereckeretal.2017, author = {Kubala, Eugen and Menzel, Marion Irene and Feuerecker, Benedikt and Glaser, Steffen J. and Schwaiger, Markus}, title = {Molecular Imaging}, booktitle = {Biophysical Techniques in Drug Discovery}, editor = {Canales, Angeles}, publisher = {Royal Society of Chemistry}, address = {Cambridge}, isbn = {978-1-78262-733-3}, doi = {https://doi.org/10.1039/9781788010016-00277}, pages = {277 -- 306}, year = {2017}, language = {en} } @inproceedings{KrajsekMenzelZwangeretal.2008, author = {Krajsek, Kai and Menzel, Marion Irene and Zwanger, Michael and Scharr, Hanno}, title = {Riemannian Anisotropic Diffusion for Tensor Valued Images}, booktitle = {Computer vision - ECCV 2008, 10th European Conference on Computer Vision, Marseille, France, October 12-18, 2008, Proceedings, Part IV}, editor = {Forsyth, David and Torr, Philipp and Zisserman, Andrew}, publisher = {Springer}, address = {Berlin}, isbn = {978-3-540-88692-1}, doi = {https://doi.org/10.1007/978-3-540-88693-8_24}, pages = {326 -- 339}, year = {2008}, language = {en} } @article{KrajsekMenzelScharr2016, author = {Krajsek, Kai and Menzel, Marion Irene and Scharr, Hanno}, title = {A Riemannian Bayesian Framework for Estimating Diffusion Tensor Images}, volume = {120}, journal = {International Journal of Computer Vision}, number = {3}, publisher = {Springer Science+Business Media}, address = {New York}, issn = {1573-1405}, doi = {https://doi.org/10.1007/s11263-016-0909-2}, pages = {272 -- 299}, year = {2016}, language = {en} } @article{KoellischLaustsenNorlingeretal.2015, author = {Koellisch, Ulrich and Laustsen, Christoffer and N{\o}rlinger, Thomas S. and {\O}stergaard, Jakob Appel and Flyvbjerg, Allan and Gringeri, Concetta V. and Menzel, Marion Irene and Schulte, Rolf F. and Haase, Axel and St{\o}dkilde-J{\o}rgensen, Hans}, title = {Investigation of metabolic changes in STZ-induced diabetic rats with hyperpolarized [1-13C]acetate}, volume = {3}, pages = {e12474}, journal = {Physiological Reports}, number = {8}, publisher = {Wiley}, address = {Weinheim}, issn = {2051-817X}, doi = {https://doi.org/10.14814/phy2.12474}, year = {2015}, abstract = {In the metabolism of acetate several enzymes are involved, which play an important role in free fatty acid oxidation. Fatty acid metabolism is altered in diabetes patients and therefore acetate might serve as a marker for pathological changes in the fuel selection of cells, as these changes occur in diabetes patients. Acetylcarnitine is a metabolic product of acetate, which enables its transport into the mitochondria for energy production. This study investigates whether the ratio of acetylcarnitine to acetate, measured by noninvasive hyperpolarized [1-13C]acetate magnetic resonance spectroscopy, could serve as a marker for myocardial, hepatic, and renal metabolic changes in rats with Streptozotocin (STZ)-induced diabetes in vivo. We demonstrate that the conversion of acetate to acetylcarnitine could be detected and quantified in all three organs of interest. More interestingly, we found that the hyperpolarized acetylcarnitine to acetate ratio was independent of blood glucose levels and prolonged hyperglycemia following diabetes induction in a type-1 diabetes model.}, language = {en} } @inproceedings{GolkovSperlMenzeletal.2014, author = {Golkov, Vladimir and Sperl, Jonathan I. and Menzel, Marion Irene and Sprenger, Tim and Tan, Ek Tsoon and Marinelli, Luca and Hardy, Christopher J. and Haase, Axel and Cremers, Daniel}, title = {Joint Super-Resolution Using Only One Anisotropic Low-Resolution Image per q-Space Coordinate}, booktitle = {Computational Diffusion MRI, MICCAI Workshop, Boston, MA, USA, September 2014}, editor = {O'Donnell, Lauren and Nedjati-Gilan, Gemma and Rathi, Yogesh and Reisert, Marco and Schneider, Torben}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-11182-7}, doi = {https://doi.org/10.1007/978-3-319-11182-7_16}, pages = {181 -- 191}, year = {2014}, language = {en} } @inproceedings{GomezUlasSperletal.2016, author = {G{\´o}mez, Pedro A. and Ulas, Cagdas and Sperl, Jonathan I. and Sprenger, Tim and Molina-Romero, Miguel and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compressed Sensing}, booktitle = {Patch-Based Techniques in Medical Imaging, First International Workshop, Patch-MI 2015, Held in Conjunction with MICCAI 2015 Munich, Germany, October 9, 2015 Revised Selected Papers}, editor = {Wu, Guorong and Coup{\´e}, Pierrick and Zhan, Yiqiang and Munsell, Brent and Rueckert, Daniel}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-28194-0}, doi = {https://doi.org/10.1007/978-3-319-28194-0_14}, pages = {112 -- 119}, year = {2016}, language = {en} } @article{PohlmeierOros‐PeusquensJavauxetal.2008, author = {Pohlmeier, Andreas and Oros-Peusquens, Ana-Maria and Javaux, Mathieu and Menzel, Marion Irene and Vanderborght, Jan and Kaffanke, Joachim and Romanzetti, Sandro and Lindenmair, J. and Vereecken, Harry and Shah, N. Jon}, title = {Changes in Soil Water Content Resulting from Ricinus Root Uptake Monitored by Magnetic Resonance Imaging}, volume = {7}, journal = {Vadose Zone Journal}, number = {3}, publisher = {Soil Science Society of America}, address = {Madison}, issn = {1539-1663}, doi = {https://doi.org/10.2136/vzj2007.0110}, pages = {1010 -- 1017}, year = {2008}, language = {en} } @unpublished{PirklGomezLippetal.2020, author = {Pirkl, Carolin and G{\´o}mez, Pedro A. and Lipp, Ilona and Buonincontri, Guido and Molina-Romero, Miguel and Sekuboyina, Anjany and Waldmannstetter, Diana and Dannenberg, Jonathan and Endt, Sebastian and Merola, Alberto and Whittaker, Joseph R. and Tomassini, Valentina and Tosetti, Michela and Jones, Derek K. and Menze, Bjoern H. and Menzel, Marion Irene}, title = {Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2005.02020}, year = {2020}, language = {en} } @inproceedings{PirklCenciniKurzawskietal.2021, author = {Pirkl, Carolin and Cencini, Matteo and Kurzawski, Jan W. and Waldmannstetter, Diana and Li, Hongwei and Sekuboyina, Anjany and Endt, Sebastian and Peretti, Luca and Donatelli, Graziella and Pasquariello, Rosa and Costagli, Mauro and Buonincontri, Guido and Tosetti, Michela and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping}, booktitle = {Proceedings of Machine Learning Research}, publisher = {PMLR}, address = {[s. l.]}, url = {https://proceedings.mlr.press/v143/pirkl21a.html}, pages = {618 -- 632}, year = {2021}, language = {en} } @inproceedings{GolkovDosovitskiySaemannetal.2015, author = {Golkov, Vladimir and Dosovitskiy, Alexey and S{\"a}mann, Philipp G. and Sperl, Jonathan I. and Sprenger, Tim and Czisch, Michael and Menzel, Marion Irene and G{\´o}mez, Pedro A. and Haase, Axel and Brox, Thomas and Cremers, Daniel}, title = {q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I}, editor = {Navab, Nassir and Hornegger, Joachim and Wells, William M. and Frangi, Alejandro F.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-24553-9}, doi = {https://doi.org/10.1007/978-3-319-24553-9_5}, pages = {37 -- 44}, year = {2015}, language = {en} } @inproceedings{GomezMolinaRomeroUlasetal.2016, author = {G{\´o}mez, Pedro A. and Molina-Romero, Miguel and Ulas, Cagdas and Bounincontri, Guido and Sperl, Jonathan I. and Jones, Derek K. and Menzel, Marion Irene and Menze, Bjoern H.}, title = {Simultaneous Parameter Mapping, Modality Synthesis, and Anatomical Labeling of the Brain with MR Fingerprinting}, booktitle = {Medical Image Computing and Computer-Assisted Intervention - MICCAI 2016, 19th International Conference, Athens, Greece, October 17-21, 2016, Proceedings, Part III}, editor = {Ourselin, Sebastien and Joskowicz, Leo and Sabuncu, Mert R. and Unal, Gozde and Wells, William M.}, publisher = {Springer}, address = {Cham}, isbn = {978-3-319-46726-9}, doi = {https://doi.org/10.1007/978-3-319-46726-9_67}, pages = {579 -- 586}, year = {2016}, language = {en} } @article{ScholzOttoHintermairetal.2015, author = {Scholz, David Johannes and Otto, Angela M. and Hintermair, Josef and Schilling, Franz and Frank, Annette and K{\"o}llisch, Ulrich and Janich, Martin A. and Schulte, Rolf F. and Schwaiger, Markus and Haase, Axel and Menzel, Marion Irene}, title = {Parameterization of hyperpolarized 13C-bicarbonate-dissolution dynamic nuclear polarization}, volume = {28}, journal = {Magnetic Resonance Materials in Physics, Biology and Medicine}, number = {6}, publisher = {Springer}, address = {Heidelberg}, issn = {1352-8661}, doi = {https://doi.org/10.1007/s10334-015-0500-9}, pages = {591 -- 598}, year = {2015}, language = {en} } @article{ZechSchoenbergHerrmannetal.2004, author = {Zech, Christoph and Schoenberg, S. O. and Herrmann, K. A. and Dietrich, O. and Menzel, Marion Irene and Lanz, Titus and Walln{\"o}fer, A. and Helmberger, T. and Reiser, M. F.}, title = {Moderne Leberbildgebung mit der MRT}, volume = {44}, journal = {Der Radiologe}, subtitle = {Aktuelle Trends und Zukunft}, number = {12}, publisher = {Springer Medizin Verlag}, address = {Berlin}, issn = {0033-832X}, doi = {https://doi.org/10.1007/s00117-004-1132-7}, pages = {1160 -- 1169}, year = {2004}, language = {de} } @article{DurstKoellischDanieleetal.2016, author = {Durst, Markus and Koellisch, Ulrich and Daniele, Valeria and Steiger, Katja and Schwaiger, Markus and Haase, Axel and Menzel, Marion Irene and Schulte, Rolf F. and Aime, Silvio and Reineri, Francesca}, title = {Probing lactate secretion in tumours with hyperpolarised NMR}, volume = {29}, journal = {NMR in Biomedicine}, number = {8}, publisher = {Wiley}, address = {New York}, issn = {1099-1492}, doi = {https://doi.org/10.1002/nbm.3574}, pages = {1079 -- 1087}, year = {2016}, language = {en} } @article{KoellischGringeriRancanetal.2014, author = {Koellisch, Ulrich and Gringeri, Concetta V. and Rancan, Giaime and Farell, Eliane V. and Menzel, Marion Irene and Haase, Axel and Schwaiger, Markus and Schulte, Rolf F.}, title = {Metabolic Imaging of Hyperpolarized [1-13C]Acetate and [1-13C]Acetylcarnitine - Investigation of the Influence of Dobutamine Induced Stress}, volume = {74}, journal = {Magnetic Resonance in Medicine}, number = {4}, publisher = {Wiley}, address = {Hoboken}, issn = {1522-2594}, doi = {https://doi.org/10.1002/mrm.25485}, pages = {1011 -- 1018}, year = {2014}, language = {en} } @article{DuewelDurstGringerietal.2016, author = {D{\"u}wel, Stephan and Durst, Markus and Gringeri, Concetta V. and Kosanke, Yvonne and Gross, Claudia and Janich, Martin A. and Haase, Axel and Glaser, Steffen J. and Schwaiger, Markus and Schulte, Rolf F. and Braren, Rickmer and Menzel, Marion Irene}, title = {Multiparametric human hepatocellular carcinoma characterization and therapy response evaluation by hyperpolarized 13C MRSI}, volume = {29}, journal = {NMR in Biomedicine}, number = {7}, publisher = {Wiley}, address = {New York}, issn = {1099-1492}, doi = {https://doi.org/10.1002/nbm.3561}, pages = {952 -- 960}, year = {2016}, language = {en} } @article{DurstKoellischFranketal.2015, author = {Durst, Markus and Koellisch, Ulrich and Frank, Annette and Rancan, Giaime and Gringeri, Concetta V. and Karas, Vincent and Wiesinger, Florian and Menzel, Marion Irene and Schwaiger, Markus and Haase, Axel and Schulte, Rolf F.}, title = {Comparison of acquisition schemes for hyperpolarised 13C imaging}, volume = {28}, journal = {NMR in Biomedicine}, number = {6}, publisher = {Wiley}, address = {New York}, issn = {1099-1492}, doi = {https://doi.org/10.1002/nbm.3301}, pages = {715 -- 725}, year = {2015}, language = {en} } @article{MenzelHanStapfetal.2000, author = {Menzel, Marion Irene and Han, Song-I and Stapf, Siegfried and Bl{\"u}mich, Bernhard}, title = {NMR Characterization of the Pore Structure and Anisotropic Self-Diffusion in Salt Water Ice}, volume = {143}, journal = {Journal of Magnetic Resonance}, number = {2}, publisher = {Academic Press/Elsevier}, address = {Orlando}, issn = {1090-7807}, doi = {https://doi.org/10.1006/jmre.1999.1999}, pages = {376 -- 381}, year = {2000}, language = {en} } @unpublished{NanDelSerWalshetal.2022, author = {Nan, Yang and Del Ser, Javier and Walsh, Simon and Sch{\"o}nlieb, Carola and Roberts, Michael and Selby, Ian and Howard, Kit and Owen, John and Neville, Jon and Guiot, Julien and Ernst, Benoit and Pastor, Ana and Alberich-Bayarri, Angel and Menzel, Marion Irene and Walsh, Sean and Vos, Wim and Flerin, Nina and Charbonnier, Jean-Paul and Rikxoort, Eva van and Chatterjee, Avishek and Woodruff, Henry and Lambin, Philippe and Cerd{\´a}-Alberich, Leonor and Mart{\´i}-Bonmat{\´i}, Luis and Herrera, Francisco and Yang, Guang}, title = {Data Harmonisation for Information Fusion in Digital Healthcare: A State-of-the-Art Systematic Review, Meta-Analysis and Future Research Directions}, publisher = {arXiv}, address = {Ithaca}, doi = {https://doi.org/10.48550/arXiv.2201.06505}, year = {2022}, abstract = {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.}, 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} } @inproceedings{NiessenPirklSolanaetal.2025, author = {Niessen, Natascha and Pirkl, Carolin and Solana, Ana Beatriz and Eichhorn, Hannah and Spieker, Veronika and Huang, Wenqi and Sprenger, Tim and Menzel, Marion Irene and Schnabel, Julia A.}, title = {INR Meets Multi-contrast MRI Reconstruction}, booktitle = {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}, editor = {Felsner, Lina and K{\"u}stner, Thomas and Maier, Andreas and Qin, Chen and Ahmadi, Seyed-Ahmad and Kazi, Anees and Hu, Xiaoling}, publisher = {Springer}, address = {Cham}, isbn = {978-3-032-06103-4}, doi = {https://doi.org/10.1007/978-3-032-06103-4_3}, pages = {23 -- 33}, year = {2025}, language = {en} } @inproceedings{MayoCenciniFataniaetal.2024, author = {Mayo, Perla and Cencini, Matteo and Fatania, Ketan and Pirkl, Carolin and Menzel, Marion Irene and Menze, Bjoern H. and Tosetti, Michela and Golbabaee, Mohammad}, title = {Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders}, booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI 2024): Conference Proceedings}, publisher = {IEEE}, address = {Piscataway}, isbn = {979-8-3503-1333-8}, doi = {https://doi.org/10.1109/ISBI56570.2024.10635677}, year = {2024}, language = {en} }