DocumentCode :
3300314
Title :
Reconstructing diffusion kurtosis tensors from sparse noisy measurements
Author :
Liu, Yugang ; Wei, Siming ; Jiang, Quan ; Yu, Yizhou
Author_Institution :
Univ. of Electron. Sci. & Technol. of China, Chengdu, China
fYear :
2010
fDate :
26-29 Sept. 2010
Firstpage :
4185
Lastpage :
4188
Abstract :
Diffusion kurtosis imaging (DKI) is a recent MRI based method that can quantify deviation from Gaussian behavior using a kurtosis tensor. DKI has potential value for the assessment of neurologic diseases. Existing techniques for diffusion kurtosis imaging typically need to capture hundreds of MRI images, which is not clinically feasible on human subjects. In this paper, we develop robust denoising and model fitting methods that make it possible to accurately reconstruct a kurtosis tensor from 75 or less noisy measurements. Our denoising method is based on subspace learning for multi-dimensional signals and our model fitting technique uses iterative reweighting to effectively discount the influences of outliers. The total data acquisition time thus drops significantly, making diffusion kurtosis imaging feasible for many clinical applications involving human subjects.
Keywords :
Gaussian processes; biomedical MRI; image denoising; iterative methods; medical image processing; neurophysiology; DKI; Gaussian behavior; MRI; diffusion kurtosis imaging; diffusion kurtosis tensor; iterative reweighting; model fitting method; multidimensional signal; neurologic disease; robust denoising; sparse noisy measurement; subspace learning; Image reconstruction; Imaging; Noise; Noise measurement; Noise reduction; Pixel; Tensile stress; Denoising; Kurtosis Tensors; MRI; Model Reconstruction; Optimization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2010 17th IEEE International Conference on
Conference_Location :
Hong Kong
ISSN :
1522-4880
Print_ISBN :
978-1-4244-7992-4
Electronic_ISBN :
1522-4880
Type :
conf
DOI :
10.1109/ICIP.2010.5649554
Filename :
5649554
Link To Document :
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