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J. Rajan, Van Audekerke, J., Verhoye, M., Van Der Linden, A., and Sijbers, J., “Denoising magnitude MRI using an adaptive NLML method”, ESMRMB Congress 28th Annual Scientific Meeting. Leipzig, Germay, p. 383, 2011.
J. Rajan, Veraart, J., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Nonlocal maximum likelihood estimation method for denoising multiple-coil magnetic resonance images.”, Magnetic resonance imaging, vol. 30, no. 10, pp. 1512-8, 2012.
J. Rajan, Van Audekerke, J., Van Der Linden, A., Verhoye, M., and Sijbers, J., “An adaptive non local maximum likelihood estimation method for denoising magnetic resonance images”, in IEEE International Symposium on Biomedical Imaging (ISBI), 2012.PDF icon Download full paper (307.46 KB)
J. Rajan, Jeurissen, B., Sijbers, J., and Kannan, K., “Denoising Magnetic Resonance Images using Fourth Order Complex Diffusion”, in 13th International Machine Vision and Image Processing Conference, Dublin, Ireland, 2009, pp. 123-127.
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.PDF icon Download paper (3.3 MB)
G. Ramos-Llordén, den Dekker, A. J., Bladt, P., Cuyt, A., and Sijbers, J., “Statistically optimal separation of multi-component MR signals with a Majorize-Minimize approach: application to MWF estimation”, 34th annual scientific meeting of the ESMRMB. 2017.
G. Ramos-Llordén, “Improved MRI Relaxometry through Statistical Signal Processing”, University of Antwerp, Antwerp, 2018.PDF icon Download thesis (19.25 MB)
G. Ramos-Llordén, Segers, H., Palenstijn, W. J., den Dekker, A. J., and Sijbers, J., “Partial discreteness: a new type of prior knowledge for MRI reconstruction”, 23rd Annual meeting of the ISMRM, Toronto, Canada., vol. 23. p. 3417, 2015.PDF icon Download abstract (1.14 MB)
G. Ramos-Llordén, den Dekker, A. J., Björk, M., Verhoye, M., and Sijbers, J., “NOVIFAST: A fast non-linear least squares method for accurate and precise estimation of T1 from SPGR signals”, 24th Annual Meeting of the ISMRM, Singapore. 2016.
G. Ramos-Llordén, Segers, H., Palenstijn, W. J., den Dekker, A. J., and Sijbers, J., “Partially discrete magnetic resonance tomography”, in 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 1653-1657.
G. Ramos-Llordén, den Dekker, A. J., Van Steenkiste, G., Jeurissen, B., Vanhevel, F., Van Audekerke, J., Verhoye, M., and Sijbers, J., “A unified Maximum Likelihood framework for simultaneous motion and T1 estimation in quantitative MR T1 mapping”, IEEE Transactions on Medical Imaging, vol. 36, no. 2, pp. 433 - 446, 2017.
G. Ramos-Llordén, den Dekker, A. J., Van Steenkiste, G., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Simultaneous motion correction and T1 estimation in quantitative T1 mapping: An ML restoration approach”, in 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 3160-3164.
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.PDF icon Download paper (3.72 MB)
G. Ramos-Llordén, den Dekker, A. J., and Sijbers, J., “Joint motion correction and estimation for T1 mapping: proof of concept”, Medical Imaging Summer School 2014, Favignana, Italy. 2014.
G. Ramos-Llordén, Beirinckx, Q., den Dekker, A. J., and Sijbers, J., “An educational presentation on accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation”, 10th Annual Meeting of the ISMRM Benelux Chapter. Antwerp, Belgium, 2018.
G. Ramos-Llordén, den Dekker, A. J., Van Steenkiste, G., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping”, 23rd Annual meeting of the ISMRM, Toronto, Canada., vol. 23. p. 447, 2015.PDF icon Download abstract (910.6 KB)
G. Ramos-Llordén and Sijbers, J., “Misalignment correction for T1 maps using a maximum likelihood estimator approach”, Imaging the brain at different scales: How to integrate multi-scale structural information?, Antwerp, Belgium, 2 Sep - 6 Sep, 2013. 2013.
G. Ramos-Llordén, den Dekker, A. J., Van Steenkiste, G., Van Audekerke, J., Verhoye, M., and Sijbers, J., “Simultaneous group-wise rigid registration and T1 ML estimation for T1 mapping”, 7th meeting of the ISMRM Benelux Chapter, Gent, Belgium, January. 2015.
G. Ramos-Llordén, Beirinckx, Q., den Dekker, A. J., and Sijbers, J., “Accurate and precise MRI relaxometry: the often disregarded but critical role of statistical parameter estimation”, Proceedings of the International Society for Magnetic Resonance in Medicine (ISMRM), 26th Annual Meeting. Paris, France, p. 5664, 2018.
B. Rast, 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.PDF icon ieee_journal_misicnet.pdf (11.02 MB)
B. Rasti, Koirala, B., and Scheunders, P., “Sparse Unmixing using Deep Convolutional Networks”, in IEEE International Geoscience and Remote Sensing Symposium, 2022, pp. 24-27.PDF icon suncnn_igarss2022.pdf (1.03 MB)
B. Rasti, Koirala, B., and Scheunders, P., “Sparse unmixing using deep convolutional networks”, in IGARSS 2022, International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022.
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.PDF icon nonlinear_unmixing.pdf (8.14 MB)
B. Rasti, Koirala, B., Scheunders, P., and Ghamisi, P., “Spectral Unmixing Using Deep Convolutional Encoder-Decoder”, in 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 3829-3832.PDF icon igarss2021.pdf (659.27 KB)
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.PDF icon ieee_grsl_sundip.pdf (2.34 MB)

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