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Magnetic Resonance Imaging

Magnetic resonance imaging and derived modalities.

More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging

J. Veraart, Poot, D. H. J., Van Hecke, W., Blockx, I., Van Der Linden, A., Verhoye, M., and Sijbers, J., “More accurate estimation of diffusion tensor parameters using diffusion kurtosis imaging”, Magnetic Resonance in Medicine, vol. 65, pp. 138-145, 2011.

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Correlation of cognitive dysfunction and diffusion tensor MRI measures in patients with mild and moderate multiple sclerosis

W. Van Hecke, Nagels, G., Leemans, A., Vandervliet, E., Sijbers, J., and Parizel, P. M., “Correlation of cognitive dysfunction and diffusion tensor MRI measures in patients with mild and moderate multiple sclerosis”, Journal of magnetic resonance imaging, vol. 31, pp. 1492-1498, 2010.

A Machine Learning Study of Several Classifiers Trained with Texture Analysis Features to Differentiate Benign from Malignant Soft Tissue Tumors in T1-MRI Images

J. Juntu, Sijbers, J., De Backer, S., Rajan, J., and Van Dyck, D., “A Machine Learning Study of Several Classifiers Trained with Texture Analysis Features to Differentiate Benign from Malignant Soft Tissue Tumors in T1-MRI Images”, Journal of Magnetic Resonance Imaging, vol. 31, pp. 680–689, 2010.

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Optimal experimental design for Diffusion Kurtosis Imaging

D. H. J. Poot, den Dekker, A. J., Achten, E., Verhoye, M., and Sijbers, J., “Optimal experimental design for Diffusion Kurtosis Imaging”, IEEE Transactions on Medical Imaging, vol. 29, pp. 819-829, 2010.

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Quantitative Evaluation of 10 Tractography Algorithms on a Realistic Diffusion MR Phantom

P. Fillard, Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., Ramirez-Manzanares, A., Reisert, M., Sakaie, K., Tensaouti, F., Yo, T. - S., Mangin, J. - F., and Poupon, C., “Quantitative Evaluation of 10 Tractography Algorithms on a Realistic Diffusion MR Phantom”, NeuroImage, vol. 56, pp. 220-234, 2011.

Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis

A. Smolders, Martino, D. F., Staeren, N., Scheunders, P., Sijbers, J., Goebel, R., and Formisano, E., “Dissecting cognitive stages with time-resolved fMRI data: a comparison of fuzzy clustering and independent component analysis”, Magnetic Resonance Imaging, vol. 25, pp. 860-868, 2007.

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Automatic estimation of the noise variance from the histogram of a magnetic resonance image

J. Sijbers, Poot, D. H. J., den Dekker, A. J., and Pintjens, W., “Automatic estimation of the noise variance from the histogram of a magnetic resonance image”, Physics in Medicine and Biology, vol. 52, pp. 1335-1348, 2007.

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Comparing isotropic and anisotropic smoothing for voxel-based DTI analyses: A simulation study

W. Van Hecke, Leemans, A., De Backer, S., Jeurissen, B., Parizel, P. M., and Sijbers, J., “Comparing isotropic and anisotropic smoothing for voxel-based DTI analyses: A simulation study”, Human Brain Mapping, vol. 31, pp. 98-114, 2010.

Improved B0 field map estimation for high field EPI

D. H. J. Poot, Pintjens, W., Verhoye, M., Van Der Linden, A., and Sijbers, J., “Improved B0 field map estimation for high field EPI”, Magnetic Resonance Imaging, vol. 28, pp. 441-450, 2010.

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On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods

W. Van Hecke, Sijbers, J., De Backer, S., Poot, D. H. J., Parizel, P. M., and Leemans, A., “On the construction of a ground truth framework for evaluating voxel-based diffusion tensor MRI analysis methods”, NeuroImage, vol. 46, pp. 692-707, 2009.

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