Tim Parbs, M.Sc.
Research Associate
Institute for Signal Processing
University of Luebeck
Ratzeburger Allee 160
23562 Lübeck
Gebäude 64, 1. OG, Raum 92
Email: | t.parbs(at)uni-luebeck.de |
Phone: | +49 451 3101 5818 |
Publications
2020
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Koch, P., Dreier, M., Larsen, A., Parbs, T. J., Maass, M., Phan, H. and Mertins, A.: Regression of Hand Movements from sEMG Data with Recurrent Neural Networks in 42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC), pp. 3783--3787, 2020
@InProceedings{Koch2020a, author = {Koch, Philipp and Dreier, Mark and Larsen, Anna and Parbs, Tim J. and Maass, Marco and Phan, Huy and Mertins, Alfred}, title = {Regression of Hand Movements from sEMG Data with Recurrent Neural Networks}, booktitle = {42nd Annual International Conference of the IEEE Engineering in Medicine \& Biology Society (EMBC)}, year = {2020}, pages = {3783--3787}, }
2019
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Parbs, T. J., Möller, A. and Mertins, A.: Retrospective Blind MR Image Recovery with Parameterised Motion Models in Bildverarbeitung für die Medizin 2019, pp. 140 - 145, Springer Vieweg, Wiesbaden, Informatik aktuell, 2019
@InCollection{parbs2019a, author = {Parbs, Tim J., and M{\"o}ller, Anita and Mertins, Alfred}, title = {Retrospective Blind MR Image Recovery with Parameterised Motion Models}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2019}, publisher = {Springer Vieweg, Wiesbaden}, year = {2019}, editor = {Handels, Heinz and Deserno, Thomas Martin and Maier, A. and Maier-Hein, K.H. and Palm, Christoph and Tolxdorff, Thomas}, series = {Informatik aktuell}, pages = {140 - 145}, doi = {10.1007/978-3-658-25326-4_30}, organization = {Springer Vieweg, Wiesbaden}, }
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Koch, P., Maass, M., Bruhns, M., Droigk, C., Parbs, T. J. and Mertins, A.: Neural Network for Reconstruction of MPI Images in 9th International Workshop on Magnetic Particle Imaging (IWMPI 2019), pp. 39--40, Infinite Science Publishing, New York, USA, March, 2019
@inproceedings{koch2019a, Author = {Koch, P. and Maass, M. and Bruhns, M. and Droigk, C. and Parbs, T. J. and Mertins, A.}, Title = {Neural Network for Reconstruction of MPI Images}, Year = {2019}, Pages = {39--40}, Booktitle = {9th International Workshop on Magnetic Particle Imaging (IWMPI 2019)}, publisher = {Infinite Science Publishing}, address = {New York, USA}, month = {March}, }
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Möller, A., Maass, M., Parbs, T. J. and Mertins, A.: Blind Sparsity Based Motion Estimation and Correction Model for Arbitrary MRI Sampling Trajectories in Proceedings of the 27th Joint Annual Meeting of ISMRM, pp. 4496, Montreal, Canada, May, 2019
@INPROCEEDINGS{moeller2019, author = {Möller, Anita and Maass, Marco and Parbs, Tim Jeldrik and Mertins, Alfred}, title = {Blind Sparsity Based Motion Estimation and Correction Model for Arbitrary MRI Sampling Trajectories}, booktitle = {Proceedings of the 27th {Joint} {Annual} {Meeting} of {ISMRM}}, pages = {4496}, year = {2019}, address = {Montreal, Canada}, month = {May}, url ={https://index.mirasmart.com/ISMRM2019/PDFfiles/4496.html} }
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Möller, A., Maass, M., Parbs, T. J. and Mertins, A.: Blind Rigid Motion Estimation for Arbitrary MRI Sampling Trajectories in Bildverarbeitung für die Medizin 2019, pp. 128-133, Springer Fachmedien Wiesbaden, Wiesbaden, 2019
@InProceedings{MoellerBVM_2019, author="M{\"o}ller, Anita and Maass, Marco and Parbs, Tim Jeldrik and Mertins, Alfred", editor="Handels, Heinz and Deserno, Thomas M. and Maier, Andreas and Maier-Hein, Klaus Hermann and Palm, Christoph and Tolxdorff, Thomas", title="Blind Rigid Motion Estimation for Arbitrary MRI Sampling Trajectories", booktitle="Bildverarbeitung f{\"u}r die Medizin 2019", year="2019", publisher="Springer Fachmedien Wiesbaden", address="Wiesbaden", pages="128--133", abstract="In this publication, a new blind motion correction algorithm for magnetic resonance imaging for arbitrary sampling trajectories is presented. Patient motion during partial measurements is estimated. Exploiting the image design, a sparse approximation of the reconstructed image is calculated with the alternating direction method of multipliers. The approximation is used with gradient descent methods with derivatives of a rigid motion model to estimate the motion and extract it from the measured data. Adapted gridding is performed in the end to receive reconstruction images without motion artifacts.", isbn="978-3-658-25326-4", doi="10.1007/978-3-658-25326-4_28", }