TY - JOUR A1 - Pirkl, Carolin A1 - Cencini, Matteo A1 - Kurzawski, Jan W. A1 - Waldmannstetter, Diana A1 - Li, Hongwei A1 - Menzel, Marion Irene T1 - Learning residual motion correction for fast and robust 3D multiparametric MRI JF - Medical Image Analysis N2 - Voluntary and involuntary patient motion is a major problem for data quality in clinical routine of Magnetic Resonance Imaging (MRI). It has been thoroughly investigated and, yet it still remains unresolved. In quantitative MRI, motion artifacts impair the entire temporal evolution of the magnetization and cause errors in parameter estimation. Here, we present a novel strategy based on residual learning for retrospective motion correction in fast 3D whole-brain multiparametric MRI. We propose a 3D multiscale convolutional neural network (CNN) that learns the non-linear relationship between the motion-affected quantitative parameter maps and the residual error to their motion-free reference. For supervised model training, despite limited data availability, we propose a physics-informed simulation to generate self-contained paired datasets from a priori motion-free data. We evaluate motion-correction performance of the proposed method for the example of 3D Quantitative Transient-state Imaging at 1.5T and 3T. We show the robustness of the motion correction for various motion regimes and demonstrate the generalization capabilities of the residual CNN in terms of real-motion in vivo data of healthy volunteers and clinical patient cases, including pediatric and adult patients with large brain lesions. Our study demonstrates that the proposed motion correction outperforms current state of the art, reliably providing a high, clinically relevant image quality for mild to pronounced patient movements. This has important implications in clinical setups where large amounts of motion affected data must be discarded as they are rendered diagnostically unusable. UR - https://doi.org/10.1016/j.media.2022.102387 KW - Multiparametric MRI KW - 3D Motion correction KW - Residual learning KW - Multiscale CNN Y1 - 2022 UR - https://doi.org/10.1016/j.media.2022.102387 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13861 SN - 1361-8423 VL - 2022 IS - 77 PB - Elsevier CY - Amsterdam ER - TY - 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 - Pirkl, Carolin A1 - Cencini, Matteo A1 - Kurzawski, Jan W. A1 - Waldmannstetter, Diana A1 - Li, Hongwei A1 - Sekuboyina, Anjany A1 - Endt, Sebastian A1 - Peretti, Luca A1 - Donatelli, Graziella A1 - Pasquariello, Rosa A1 - Costagli, Mauro A1 - Buonincontri, Guido A1 - Tosetti, Michela A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Residual learning for 3D motion corrected quantitative MRI BT - Robust clinical T1, T2 and proton density mapping T2 - Medical Imaging with Deep Learning MIDL 2021 KW - 3D multiparametric MRI KW - motion correction KW - deep learning KW - residual learning KW - multiscale CNN Y1 - 2021 UR - https://openreview.net/forum?id=hxgQM71AuRA PB - OpenReview ER - TY - CHAP A1 - 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 - Pirkl, Carolin A1 - Cencini, Matteo A1 - Kurzawski, Jan W. A1 - Waldmannstetter, Diana A1 - Li, Hongwei A1 - Sekuboyina, Anjany A1 - Endt, Sebastian A1 - Peretti, Luca A1 - Donatelli, Graziella A1 - Pasquariello, Rosa A1 - Costagli, Mauro A1 - Buonincontri, Guido A1 - Tosetti, Michela A1 - Menzel, Marion Irene A1 - Menze, Bjoern H. T1 - Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping T2 - Proceedings of Machine Learning Research Y1 - 2021 UR - https://proceedings.mlr.press/v143/pirkl21a.html SP - 618 EP - 632 PB - PMLR CY - [s. l.] 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 -