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G. Ramos-Llordén, den Dekker, A. J., and Sijbers, J., “Partial Discreteness: a Novel Prior for Magnetic Resonance Image Reconstruction”, IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1041 - 1053, 2017.

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G. Ramos-Llordén, Vegas-Sánchez-Ferrero, G., Björk, M., Vanhevel, F., Parizel, P. M., Estépar, R. San José, den Dekker, A. J., and Sijbers, J., “NOVIFAST: A fast algorithm for accurate and precise VFA MRI T1 mapping”, IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2414 - 2427, 2018.

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B. Rasti, Scheunders, P., Ghesami, P., Licciardi, G., and Chanussot, J., “Noise reduction in hyperspectral imagery: overview and application”, Remote Sensing , vol. 10, no. 3, p. 482, 2018.

B. Rasti and Koirala, B., “SUnCNN: Sparse Unmixing Using Unsupervised Convolutional Neural Network”, IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1-5, 2021.

ieee_grsl_sundip.pdf (2.34 MB)

B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., “MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, no. 5522815, 2022.

misicnet_ieee_tgrs_author_version.pdf (5.57 MB)

B. Rasti, Koirala, B., Scheunders, P., and Chanussot, J., “MiSiCNet: Minimum Simplex Convolutional Network for Deep Hyperspectral Unmixing”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.

misicnet_ieee_tgrs_author_version.pdf (5.52 MB)

B. Rasti, Koirala, B., and Scheunders, P., “HapkeCNN: Blind Nonlinear Unmixing for Intimate Mixtures Using Hapke Model and Convolutional Neural Network”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2022.

hapkecnnrg.pdf (8.23 MB)

B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “UnDIP: hyperspectral unmixing using deep image prior”, IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1-15, 2021.

manuscript.pdf (13 MB)

B. Rasti, Koirala, B., and Scheunders, P., “HapkeCNN: Blind nonlinear unmixing for intimate mixtures using Hapke model and convolutional neural network”, IEEE Transactions on Geoscience and Remote Sensing, 2022.

hapke_cnn.pdf (8.14 MB)

B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “How Hyperspectral Image Unmixing and Denoising Can Boost Each Other”, Remote Sensing, vol. 12, no. 1728, 2020.

remotesensing-12-01728.pdf (2.27 MB)

A. J. Rebelo, Scheunders, P., Esler, K. J., and Meire, P., “Evaluating palmiet wetland decline: a comparison of three methods”, Remote Sensing Applications: Society and Environment, vol. 8, pp. 212-223, 2017.

Y. D. Reijmer, Leemans, A., Heringa, S. M., Wielaard, I., Jeurissen, B., Koek, H. L., and Biessels, G. J., “Improved sensitivity to cerebral white matter abnormalities in Alzheimer's disease with spherical deconvolution based tractography.”, PloS one, vol. 7, no. 8, p. e44074, 2012.

P. Reischig, King, A., Nervo, L., Viganó, N., Guilhem, Y., Palenstijn, W. J., Batenburg, K. J., Preuss, M., and Ludwig, W., “Advances in X-ray diffraction contrast tomography: flexibility in the setup geometry and application to multiphase materials”, Journal of Applied Crystallography, vol. 46, no. 2, pp. 297 - 311, 2013.

J. Renders, Jeurissen, B., Nguyen, A. - T., De Beenhouwer, J., and Sijbers, J., “ImWIP: open-source image warping toolbox with adjoints and derivatives”, SoftwareX, vol. 24, p. 101524, 2023.

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J. Renders, Sijbers, J., and De Beenhouwer, J., “Adjoint image warping using multivariate splines with application to 4D-CT”, Medical Physics, vol. 48, no. 10, pp. 6362-6374, 2021.

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J. Renders, Mancini, L., De Beenhouwer, J., and Sijbers, J., “Direct reconstruction of wet foam from sparse-view, dynamic X-ray CT scans”, Advanced Engineering Materials, vol. 2400957, pp. 1-10, 2024.

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T. Roelandts, Batenburg, K. J., Biermans, E., Kubel, C., Bals, S., and Sijbers, J., “Accurate segmentation of dense nanoparticles by partially discrete electron tomography”, Ultramicroscopy, vol. 114, pp. 96-105, 2012.

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T. Roelandts, Batenburg, K. J., den Dekker, A. J., and Sijbers, J., “The reconstructed residual error: A novel segmentation evaluation measure for reconstructed images in tomography”, Computer Vision and Image Understanding, vol. 126, pp. 28-37, 2014.

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T. Roine, Jeurissen, B., Perrone, D., Aelterman, J., Leemans, A., Philips, W., and Sijbers, J., “Isotropic non-white matter partial volume effects in constrained spherical deconvolution”, Information-based methods for neuroimaging: analyzing structure, function and dynamics, p. 112, 2015.

T. Roine, Jeurissen, B., Perrone, D., Aelterman, J., Leemans, A., Philips, W., and Sijbers, J., “Isotropic non-white matter partial volume effects in constrained spherical deconvolution”, Frontiers in Neuroinformatics, vol. 8, pp. 1-9, 2014.

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T. Roine, Jeurissen, B., Perrone, D., Aelterman, J., Philips, W., Sijbers, J., and Leemans, A., “Reproducibility and intercorrelation of graph theoretical measures in structural brain connectivity networks”, Medical Image Analysis, vol. 52, pp. 56-67, 2019.

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U. Roine, Salmi, J., Roine, T., Nieminen-von Wendt, T., Leppämäki, S., Rintahaka, P., Tani, P., Leemans, A., and Sams, M., “Constrained spherical deconvolution-based tractography and tract-based spatial statistics show abnormal microstructural organization in Asperger syndrome”, Molecular Autism, vol. 6, p. 4, 2015.

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U. Roine, Roine, T., Salmi, J., Nieminen-von Wendt, T., Tani, P., Leppämäki, S., Rintahaka, P., Caeyenberghs, K., Leemans, A., and Sams, M., “Abnormal wiring of the connectome in adults with high-functioning autism spectrum disorder”, Molecular Autism, vol. 6, p. 65, 2015.

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T. Roine, Jeurissen, B., Perrone, D., Aelterman, J., Philips, W., Leemans, A., and Sijbers, J., “Informed constrained spherical deconvolution (iCSD)”, Medical Image Analysis, vol. 24, no. 1, pp. 269–281, 2015.

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U. Roine, Roine, T., Salmi, J., Nieminen-von Wendt, T., Leppämäki, S., Rintahaka, P., Tani, P., Leemans, A., and Sams, M., “Increased coherence of white matter fiber tract organization in adults with Asperger syndrome: A diffusion tensor imaging study”, Autism Research, vol. 6, no. 6, pp. 642-650, 2013.

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