@unpublished{EndtEngelNaldietal.2023, author = {Endt, Sebastian and Engel, Maria and Naldi, Emanuele and Assereto, Rodolfo and Molendowska, Malwina and Mueller, Lars and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{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{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} } @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{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} } @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} } @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} } @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} } @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} } @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} } @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{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} }