M
Maartje M. Nillesen
Researcher at Radboud University Nijmegen
Publications - 66
Citations - 991
Maartje M. Nillesen is an academic researcher from Radboud University Nijmegen. The author has contributed to research in topics: Segmentation & Image segmentation. The author has an hindex of 16, co-authored 66 publications receiving 910 citations. Previous affiliations of Maartje M. Nillesen include Radboud University Nijmegen Medical Centre.
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Journal ArticleDOI
Performance evaluation of methods for two-dimensional displacement and strain estimation using ultrasound radio frequency data.
Richard G.P. Lopata,Maartje M. Nillesen,Hendrik H.G. Hansen,I. H. Gerrits,Johan M. Thijssen,Chris L. de Korte +5 more
TL;DR: A coarse-to-fine approach is favored using RF data on a fine scale, using envelope data for window sizes exceeding the theoretical upper bound for strain estimation and the use of 2D parabolic interpolation to obtain subsample displacement estimates.
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Dynamic imaging of skeletal muscle contraction in three orthogonal directions
Richard G.P. Lopata,Richard G.P. Lopata,Johannes P. van Dijk,Sigrid Pillen,Maartje M. Nillesen,Huub Maas,Johan M. Thijssen,Dick F. Stegeman,Dick F. Stegeman,Chris L. de Korte +9 more
TL;DR: The voluntary contraction patterns were found to be both practically feasible and reproducible, which will enable muscles and more natural contraction patterns to be examined without the need of electrical stimulation.
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Ultrafast vascular strain compounding using plane wave transmission.
TL;DR: The performance of strain imaging using plane wave compounding is investigated using simulations of an artery with a vulnerable plaque and experimental data of a two-layered vessel phantom, and the results show that planeWave compounding outperforms 0° focused strain imaging.
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Three-dimensional cardiac strain imaging in healthy children using rf-data
Richard G.P. Lopata,Richard G.P. Lopata,Maartje M. Nillesen,Johan M. Thijssen,L. Kapusta,L. Kapusta,Chris L. de Korte +6 more
TL;DR: 3-D strain imaging using RF-data is feasible, but validation with other modalities and with conventional 3-D speckle tracking techniques will be necessary.
Journal ArticleDOI
Comparison of one-dimensional and two-dimensional least-squares strain estimators for phased array displacement data.
Richard G.P. Lopata,Hendrik H.G. Hansen,Maartje M. Nillesen,Johan M. Thijssen,Chris L. de Korte +4 more
TL;DR: RMSE analysis revealed that the 2D LSQSE yields better results for phased array data, especially for larger insonification angles, and showed that the LSQ kernel size should be limited to avoid loss in resolution.