@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} } @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{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} }