3D Diffusion tensor magnetic resonance images denoising based on sparse representation
Refereed conference paper presented and published in conference proceedings

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摘要Diffusion tensor magnetic resonance imaging (DT-MRI) is widely used to characterize white matter health and brain disease. However, the DT-MRI is very sensitive to noise. This paper proposes a sparse representation based denoising method for 3D diffusion weighted images (DWI) in DT-MRI. As consecutive 2D images in DWI volume have similar content and structure, we can process a fixed number of adjacent images from DWI volume simultaneously. The proposed method first learned a dictionary from the selected 2d diffusion weighted images according to the K-SVD learning algorithm. Then the clean images are obtained by gradually approximating the underlying images using the bases selected from the learned dictionary based on sparse representation. At last, the tensor images are estimated from the diffusion weighted images. The experiments on both synthetic and real DT-MRI images show that the proposed method performs better than classical techniques by preserving image contrast and structures. © 2011 IEEE.
著者Kong Y.-Y., Wang D.-F., Wang T.-F., Chu W.C., Ahuja A.T.
會議名稱2011 International Conference on Machine Learning and Cybernetics, ICMLC 2011
會議開始日10.07.2011
會議完結日13.07.2011
會議地點Guilin, Guangxi
會議國家/地區中國
詳細描述organized by IEEE,
出版年份2011
月份11
日期7
卷號4
頁次1602 - 1606
國際標準書號9781457703065
國際標準期刊號2160-133X
語言英式英語
關鍵詞3D image denoising, Diffusion tensor image, Diffusion weighted images, K-SVD, Sparse representation

上次更新時間 2020-31-07 於 00:17