TY - JOUR A1 - Benjamin, Arnold Julian Vinoj A1 - Gómez, Pedro A. A1 - Golbabaee, Mohammad A1 - Bin Mahbub, Zaid A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Davies, Mike E. A1 - Marshall, Ian T1 - Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: An alternative to conventional spiral MR Fingerprinting JF - Magnetic Resonance Imaging UR - https://doi.org/10.1016/j.mri.2019.04.014 KW - cartesian MRF KW - multi-shot EPI KW - quantitative maps Y1 - 2019 UR - https://doi.org/10.1016/j.mri.2019.04.014 SN - 0730-725X VL - 2019 IS - 61 SP - 20 EP - 32 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Golbabaee, Mohammad A1 - Chen, Dongdong A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. T1 - Geometry of Deep Learning for Magnetic Resonance Fingerprinting T2 - ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) UR - https://doi.org/10.1109/ICASSP.2019.8683549 KW - magnetic resonance fingerprinting KW - inverse problem KW - deep learning KW - dictionary KW - manifold compressed sensing Y1 - 2019 UR - https://doi.org/10.1109/ICASSP.2019.8683549 SN - 978-1-5386-4658-8 SP - 7825 EP - 7829 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Golbabaee, Mohammad A1 - Buonincontri, Guido A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. A1 - Davies, Mike E. A1 - Gómez, Pedro A. T1 - Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks JF - Medical Image Analysis UR - https://doi.org/10.1016/j.media.2020.101945 KW - magnetic resonance fingerprinting KW - compressed sensing KW - convex model-based reconstruction KW - residual network KW - encoder-decoder network Y1 - 2020 UR - https://doi.org/10.1016/j.media.2020.101945 SN - 1361-8415 SN - 1361-8423 VL - 2021 IS - 69 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Pirkl, Carolin A1 - Nunez-Gonzalez, Laura A1 - Kofler, Florian A1 - Endt, Sebastian A1 - Grundl, Lioba A1 - Golbabaee, Mohammad A1 - Gómez, Pedro A. A1 - Cencini, Matteo A1 - Buonincontri, Guido A1 - Schulte, Rolf F. A1 - Smits, Marion A1 - Wiestler, Benedikt A1 - Menze, Bjoern H. A1 - Menzel, Marion Irene A1 - Hernandez-Tamames, Juan A. T1 - Accelerated 3D whole-brain T1, T2, and proton density mapping BT - feasibility for clinical glioma MR imaging JF - Neuroradiology N2 - Purpose: Advanced MRI-based biomarkers offer comprehensive and quantitative information for the evaluation and characterization of brain tumors. In this study, we report initial clinical experience in routine glioma imaging with a novel, fully 3D multiparametric quantitative transient-state imaging (QTI) method for tissue characterization based on T1 and T2 values. Methods: To demonstrate the viability of the proposed 3D QTI technique, nine glioma patients (grade II–IV), with a variety of disease states and treatment histories, were included in this study. First, we investigated the feasibility of 3D QTI (6:25 min scan time) for its use in clinical routine imaging, focusing on image reconstruction, parameter estimation, and contrast-weighted image synthesis. Second, for an initial assessment of 3D QTI-based quantitative MR biomarkers, we performed a ROI-based analysis to characterize T1 and T2 components in tumor and peritumoral tissue. Results: The 3D acquisition combined with a compressed sensing reconstruction and neural network-based parameter inference produced parametric maps with high isotropic resolution (1.125 × 1.125 × 1.125 mm3 voxel size) and whole-brain coverage (22.5 × 22.5 × 22.5 cm3 FOV), enabling the synthesis of clinically relevant T1-weighted, T2-weighted, and FLAIR contrasts without any extra scan time. Our study revealed increased T1 and T2 values in tumor and peritumoral regions compared to contralateral white matter, good agreement with healthy volunteer data, and high inter-subject consistency. Conclusion: 3D QTI demonstrated comprehensive tissue assessment of tumor substructures captured in T1 and T2 parameters. Aiming for fast acquisition of quantitative MR biomarkers, 3D QTI has potential to improve disease characterization in brain tumor patients under tight clinical time-constraints. UR - https://doi.org/10.1007/s00234-021-02703-0 KW - MRI KW - Image-based biomarkers KW - Multiparametric imaging KW - Glioma imaging KW - Neural networks Y1 - 2021 UR - https://doi.org/10.1007/s00234-021-02703-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13885 VL - 63 IS - 11 SP - 1831 EP - 1851 PB - Springer CY - Berlin ER - TY - CHAP A1 - Fatania, Ketan A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Hall, Peter A1 - Golbabaee, Mohammad T1 - A Plug-and-Play Approach To Multiparametric Quantitative MRI: Image Reconstruction Using Pre-Trained Deep Denoisers T2 - Proceedings of the 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI) UR - https://doi.org/10.1109/ISBI52829.2022.9761603 KW - Quantitative MRI KW - Magnetic Resonance Fingerprinting KW - Compressed Sensing KW - Inverse Problems KW - Deep Learning KW - Iterative Image Reconstruction KW - Plug-and-Play Y1 - 2022 UR - https://doi.org/10.1109/ISBI52829.2022.9761603 SN - 978-1-6654-2923-8 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Chen, Dongdong A1 - Golbabaee, Mohammad A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. ED - Cardoso, M. Jorge ED - Feragen, Aasa ED - Glocker, Ben ED - Konukoglu, Ender ED - Oguz, Ipek ED - Unal, Gozde ED - Vercauteren, Tom T1 - Deep Fully Convolutional Network for MR Fingerprinting T2 - Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track N2 - This work proposes an end-to-end deep fully convolutional neural network for MRF reconstruction (MRF-FCNN), which firstly employs linear dimensionality reduction and then uses a neural network to project the data into the tissue parameters. The MRF dictionary is only used for training the network and not during image reconstruction. We show that MRF-FCNN is capable of achieving accuracy comparable to the ground-truth maps thanks to capturing spatio-temporal data structures without a need for the non-scalable dictionary matching step used in the baseline reconstructions. KW - Magnetic Resonance Fingerprinting KW - Deep Learning Y1 - 2019 UR - https://openreview.net/forum?id=SJxUdvJTtN UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31163 ER - TY - CHAP A1 - Golbabaee, Mohammad A1 - Chen, Dongdong A1 - Davies, Mike E. A1 - Menzel, Marion Irene A1 - Gómez, Pedro A. ED - Cardoso, M. Jorge ED - Feragen, Aasa ED - Glocker, Ben ED - Konukoglu, Ender ED - Oguz, Ipek ED - Unal, Gozde ED - Vercauteren, Tom T1 - Spatio-temporal regularization for deep MR Fingerprinting T2 - Medical Imaging with Deep Learning, MIDL 2019: Extended Abstract Track N2 - We study a deep learning approach to address the heavy storage and computation re- quirements of the baseline dictionary-matching (DM) for Magnetic Resonance Fingerprint- ing (MRF) reconstruction. The MRF-Net provides a piece-wise affine approximation to the (temporal) Bloch response manifold projection. Fed with non-iterated back-projected images, the network alone is unable to fully resolve spatially-correlated artefacts which ap- pear in highly undersampling regimes. We propose an accelerated iterative reconstruction to minimize these artefacts before feeding into the network. This is done through a convex regularization that jointly promotes spatio-temporal regularities of the MRF time-series. KW - magnetic resonance fingerprinting KW - deep learning KW - regularisation Y1 - 2019 UR - https://openreview.net/forum?id=ryx64UL6YE UR - https://2019.midl.io/program/extended-abstracts.html UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-31174 ER - TY - INPR A1 - Fatania, Ketan A1 - Chau, Kwai Y. A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Golbabaee, Mohammad T1 - Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI UR - https://doi.org/10.48550/arXiv.2211.12786 KW - Quantitative MRI KW - Magnetic Resonance Fingerprinting KW - Compressed Sensing KW - Inverse Problems KW - Self-Supervised Deep Learning KW - Equivariant Imaging Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2211.12786 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Chen, Dongdong A1 - Golbabaee, Mohammad A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. T1 - A Fully Convolutional Network for MR Fingerprinting UR - https://doi.org/10.48550/arXiv.1911.09846 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1911.09846 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Benjamin, Arnold Julian Vinoj A1 - Gómez, Pedro A. A1 - Golbabaee, Mohammad A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Davies, Mike E. A1 - Marshall, Ian T1 - Balanced multi-shot EPI for accelerated Cartesian MRF: An alternative to spiral MRF UR - https://doi.org/10.48550/arXiv.1809.02506 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.1809.02506 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Benjamin, Arnold Julian Vinoj A1 - Gómez, Pedro A. A1 - Golbabaee, Mohammad A1 - Bin Mahbub, Zaid A1 - Sprenger, Tim A1 - Menzel, Marion Irene A1 - Davies, Mike E. A1 - Marshall, Ian T1 - Multi-shot Echo Planar Imaging for accelerated Cartesian MR Fingerprinting: an alternative to conventional spiral MR Fingerprinting UR - https://doi.org/10.48550/arXiv.1906.08195 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1906.08195 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Golbabaee, Mohammad A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Buonincontri, Guido A1 - Gómez, Pedro A. T1 - Deep MR Fingerprinting with total-variation and low-rank subspace priors UR - https://doi.org/10.48550/arXiv.1902.10205 Y1 - 2019 UR - https://doi.org/10.48550/arXiv.1902.10205 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Fatania, Ketan A1 - Chau, Kwai Y. A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Golbabaee, Mohammad T1 - Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI T2 - 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI) UR - https://doi.org/10.1109/ISBI53787.2023.10230440 Y1 - 2023 UR - https://doi.org/10.1109/ISBI53787.2023.10230440 SN - 978-1-6654-7358-3 PB - IEEE CY - Piscataway ER - TY - INPR A1 - Fatania, Ketan A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Hall, Peter A1 - Golbabaee, Mohammad T1 - A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers UR - https://doi.org/10.48550/arXiv.2202.05269 Y1 - 2022 UR - https://doi.org/10.48550/arXiv.2202.05269 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Golbabaee, Mohammad A1 - Chen, Dongdong A1 - Gómez, Pedro A. A1 - Menzel, Marion Irene A1 - Davies, Mike E. T1 - Geometry of Deep Learning for Magnetic Resonance Fingerprinting UR - https://doi.org/10.48550/arXiv.1809.01749 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.1809.01749 PB - arXiv CY - Ithaca ER - TY - INPR A1 - Golbabaee, Mohammad A1 - Buonincontri, Guido A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. A1 - Davies, Mike E. A1 - Gómez, Pedro A. T1 - Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders UR - https://doi.org/10.48550/arXiv.2001.08746 Y1 - 2020 UR - https://doi.org/10.48550/arXiv.2001.08746 PB - arXiv CY - Ithaca ER - TY - CHAP A1 - Mayo, Perla A1 - Cencini, Matteo A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Tosetti, Michela A1 - Menze, Bjoern H. A1 - Golbabaee, Mohammad ED - XU, Xuanang ED - Cui, Zhiming ED - Rekik, Islem ED - Ouyang, Xi ED - Sun, Kaicong T1 - StoDIP: Efficient 3D MRF Image Reconstruction with Deep Image Priors and Stochastic Iterations T2 - Machine Learning in Medical Imaging: 15th International Workshop, MLMI 2024, Held in Conjunction with MICCAI 2024, Proceedings, Part II UR - https://doi.org/10.1007/978-3-031-73290-4_13 Y1 - 2024 UR - https://doi.org/10.1007/978-3-031-73290-4_13 SN - 978-3-031-73290-4 SP - 128 EP - 137 PB - Springer CY - Cham ER - TY - CHAP A1 - Mayo, Perla A1 - Cencini, Matteo A1 - Fatania, Ketan A1 - Pirkl, Carolin A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. A1 - Tosetti, Michela A1 - Golbabaee, Mohammad T1 - Deep Image Priors for Magnetic Resonance Fingerprinting with Pretrained Bloch-Consistent Denoising Autoencoders T2 - IEEE International Symposium on Biomedical Imaging (ISBI 2024): Conference Proceedings UR - https://doi.org/10.1109/ISBI56570.2024.10635677 Y1 - 2024 UR - https://doi.org/10.1109/ISBI56570.2024.10635677 SN - 979-8-3503-1333-8 PB - IEEE CY - Piscataway ER -