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",
Medical Image Computing and Computer Assisted Intervention, vol. 13, issue 1, pp. 615-622, 2010.
Download paper (229.84 KB)
Download paper (229.84 KB)
"High resolution T1 estimation from multiple low resolution magnetic resonance images",
IEEE International Symposium on Biomedical Imaging (ISBI): From nano to macro, vol. 12, pp. 1036-1039, 2015.
"Super-Resolution for Multislice Diffusion Tensor Imaging",
Magnetic Resonance in Medicine, vol. 69, issue 1, pp. 103–113, 2013.
Download paper (1.04 MB)
Download paper (1.04 MB)
"Super-resolution reconstruction of diffusion parameters from diffusion-weighted images with different slice orientations",
Magnetic Resonance in Medicine, vol. 75, issue 1, pp. 181-195, 2016.
"Super-resolution structural connectivity and anatomy of the zebra finch brain ",
ISMRM Benelux, 2015.
"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, issue 5, pp. 1818–1830, 2017.
Download paper (3.3 MB)
Download paper (3.3 MB)
Main researcher(s):
Research Area:
Research keywords: