| Accelerated 3D whole-brain T1, T2, and proton density mapping: feasibility for clinical glioma MR imaging CM Pirkl, L Nunez-Gonzalez, F Kofler, S Endt, L Grundl, M Golbabaee, ... Neuroradiology, 1-21, 2021 | 31 | 2021 |
| Contrast-to-noise ratio analysis of microscopic diffusion anisotropy indices in q-space trajectory imaging J Martin, S Endt, A Wetscherek, TA Kuder, A Doerfler, M Uder, B Hensel, ... Zeitschrift für Medizinische Physik 30 (1), 4-16, 2020 | 23 | 2020 |
| Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting CM Pirk, PA Gómez, I Lipp, G Buonincontri, M Molina-Romero, ... Medical Imaging with Deep Learning, 638-654, 2020 | 14 | 2020 |
| Learning residual motion correction for fast and robust 3D multiparametric MRI CM Pirkl, M Cencini, JW Kurzawski, D Waldmannstetter, H Li, ... Medical Image Analysis 77, 102387, 2022 | 9 | 2022 |
| Twice‐refocused stimulated echo diffusion imaging: Measuring diffusion time dependence at constant T1 weighting J Martin, S Endt, A Wetscherek, TA Kuder, A Doerfler, M Uder, B Hensel, ... Magnetic Resonance in Medicine 83 (5), 1741-1749, 2020 | 5 | 2020 |
| Unmixing tissue compartments via deep learning T1-T2-relaxation correlation imaging S Endt, CM Pirkl, CM Verdun, BH Menze, MI Menzel 17th International Symposium on Medical Information Processing and Analysis …, 2021 | 3 | 2021 |
| In vivo myelin water quantification using diffusion–relaxation correlation MRI: A comparison of 1D and 2D methods S Endt, M Engel, E Naldi, R Assereto, M Molendowska, L Mueller, ... Applied Magnetic Resonance 54 (11), 1571-1588, 2023 | 2 | 2023 |
| An observational study on the diffusion time and field strength dependency of the diffusion coefficient in human white matter S Endt, J Martin, L Ning, L Müller, A Wetscherek, M Uder, B Hensel, ... Proceedings of the International Society for Magnetic Resonance in Medicine …, 2019 | 2 | 2019 |
| Residual learning for 3D motion corrected quantitative MRI: Robust clinical T1, T2 and proton density mapping C Pirkl, M Cencini, JW Kurzawski, D Waldmannstetter, H Li, A Sekuboyina, ... Medical Imaging with Deep Learning, 2021 | 1 | 2021 |
| Accelerated 3D multiparametric MRI in glioma patients: initial clinical experience CM Pirkl, L Nuñez-Gonzalez, PA Gómez, S Endt, RF Schulte, ... Int Soc. Mag. Res. Med.(ISMRM) 8, 558-559, 2020 | 1 | 2020 |
| Classification of multiparametric correlation MRI signals using deep neural networks S Endt, T Lachermeier, M Menzel Medical Imaging with Deep Learning, 2024 | | 2024 |
| MicroCoP: digital Microstructure Correlation Phantoms for benchmarking of multicomponent MRI methods S Endt, CM Pirkl, M Palombo, MI Menzel International Society for Magnetic Resonance in Medicine (ISMRM), 2023 ISMRM …, 2023 | | 2023 |
| Myelin Water Quantification Using Spiral-Based Magnetic Resonance Correlation Imaging S Endt, E Naldi, M Engel, R Assereto, C Mayrink Verdun, CM Pirkl, ... 20th IEEE International Symposium on Biomedical Imaging (ISBI), 2023 | | 2023 |
| A deep-learning approach to accelerated T1-T2-relaxation-correlation imaging S Endt, CM Pirkl, CM Verdun, BH Menze, MI Menzel European Society for Magnetic Resonance in Medicine and Biology (ESMRMB …, 2020 | | 2020 |
| Radial vs. Spiral–A Comparison of Stack-of-stars and Stack-of-spirals Spatial Encoding Schemes in Multiparametric Body MRI with QTI CM Pirkl, RF Schulte, P García-Polo, M Cencini, S Endt, L Biagi, M Tosetti, ... | | |
| Synchronizing dimension reduction and parameter inference in 3D multiparametric MRI: A hybrid dual-pathway neural network approach CM Pirkl, I Horvath, S Endt, G Buonincontri, MI Menzel, PA Gómez, ... | | |
