@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a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series.}, language = {en} } @unpublished{ChenGolbabaeeGomezetal.2019, author = {Chen, Dongdong and Golbabaee, Mohammad and G{\´o}mez, Pedro A. and Menzel, Marion Irene and Davies, Mike E.}, title = {A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} } @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} } @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} } @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} } @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} } @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} } @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} } @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} } @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} } @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} } @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{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{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} } @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} }