Super resolution reconstruction for quantitative imaging
Abstract:
We developed a super-resolution reconstruction methodology for diffusion and relaxometry MRI. It allows to improve the trade-off between acquisition time, spatial resolution and SNR. From a set of low resolution multi-slice images, each with a different (diffusion or relaxometry) contrast, an image is reconstructed with a much higher spatial resolution compared to a 3D image that is directly acquired in the same time frame.
Publications:
“General and Efficient Super-Resolution method for Multi-Slice MRI”, in Medical Image Computing and Computer Assisted Intervention, 2010, vol. 13, no. 1, pp. 615-622.
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“High resolution T1 estimation from multiple low resolution magnetic resonance images”, in IEEE International Symposium on Biomedical Imaging (ISBI): From nano to macro, 2015, vol. 12, pp. 1036-1039.
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“Super-Resolution for Multislice Diffusion Tensor Imaging”, Magnetic Resonance in Medicine, vol. 69, no. 1, pp. 103–113, 2013.
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“Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations”, Magnetic Resonance in Medicine, vol. 75, no. 1, pp. 181-195, 2016.
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“Super-resolution structural connectivity and anatomy of the zebra finch brain ”, ISMRM Benelux. 2015.
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“Super-resolution T1 estimation: quantitative high resolution T1 mapping from a set of low resolution T1 weighted images with different slice orientations”, Magnetic Resonance in Medicine, vol. 77, no. 5, pp. 1818–1830, 2017.
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