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Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting.

C. Pirkl, P. Gómez, I. Lipp, G. Buonincontri, M. Molina-Romero, A. Sekuboyina, D. Waldmannstetter, J. Dannenberg, S. Endt, A. Merola, J. Whittaker, V. Tomassini, M. Tosetti, D. Jones, B. Menze, and M. Menzel. MIDL, volume 121 of Proceedings of Machine Learning Research, page 638-654. PMLR, (2020)

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Klaus Pirklbauer

Hans-Georg Pirkl

Klaus Pirklbauer

Friedrich Johannes Pirkl

Friedrich Pirkl

Other publications of authors with the same name

Compressive MRI quantification using convex spatiotemporal priors and deep auto-encoders.M. Golbabaee, G. Bounincontri, C. Pirkl, M. Menzel, B. Menze, M. Davies, and P. Gómez. CoRR, (2020)Unpaired MR Image Homogenisation by Disentangled Representations and Its Uncertainty.H. Li, S. Gopal, A. Sekuboyina, J. Zhang, C. Niu, C. Pirkl, J. Kirschke, B. Wiestler, and B. Menze. UNSURE/PIPPI@MICCAI, volume 12959 of Lecture Notes in Computer Science, page 44-53. Springer, (2021)SRflow: Deep learning based super-resolution of 4D-flow MRI data.S. Shit, J. Zimmermann, I. Ezhov, J. Paetzold, A. Fava-Sanches, C. Pirkl, and B. Menze. Frontiers Artif. Intell., (2022)Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI.K. Fatania, K. Chau, C. Pirkl, M. Menzel, P. Hall, and M. Golbabaee. CoRR, (2022)Deep MR Fingerprinting with total-variation and low-rank subspace priors.M. Golbabaee, C. Pirkl, M. Menzel, G. Buonincontri, and P. Gómez. CoRR, (2019)Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI.K. Fatania, K. Chau, C. Pirkl, M. Menzel, and M. Golbabaee. ISBI, page 1-4. IEEE, (2023)Region of Interest focused MRI to Synthetic CT Translation using Regression and Classification Multi-task Network.S. Kaushik, M. Bylund, C. Cozzini, D. Shanbhag, S. Petit, J. Wyatt, M. Menzel, C. Pirkl, B. Mehta, V. Chauhan and 5 other author(s). CoRR, (2022)Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting.C. Pirkl, P. Gómez, I. Lipp, G. Buonincontri, M. Molina-Romero, A. Sekuboyina, D. Waldmannstetter, J. Dannenberg, S. Endt, A. Merola and 6 other author(s). MIDL, volume 121 of Proceedings of Machine Learning Research, page 638-654. PMLR, (2020)Learning residual motion correction for fast and robust 3D multiparametric MRI.C. Pirkl, M. Cencini, J. Kurzawski, D. Waldmannstetter, H. Li, A. Sekuboyina, S. Endt, L. Peretti, G. Donatelli, R. Pasquariello and 5 other author(s). Medical Image Anal., (2022)Compressive MRI quantification using convex spatiotemporal priors and deep encoder-decoder networks.M. Golbabaee, G. Buonincontri, C. Pirkl, M. Menzel, B. Menze, M. Davies, and P. Gómez. Medical Image Anal., (2021)

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Disambiguation of "Pirkl, Carolin M."

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Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting.

Please choose a person to relate this publication to

Klaus Pirklbauer

Hans-Georg Pirkl

Klaus Pirklbauer

Friedrich Johannes Pirkl

Friedrich Pirkl

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Pirkl, Carolin M."

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting.

Please choose a person to relate this publication to

Klaus Pirklbauer

Hans-Georg Pirkl

Klaus Pirklbauer

Friedrich Johannes Pirkl

Friedrich Pirkl

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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history of this post
URL
DOI
BibTeX
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Deep learning-based parameter mapping for joint relaxation and diffusion tensor MR Fingerprinting.