4D wavelet noise suppression of MR diffusion tensor data

Hesamoddin Jahanian, Azadeh Yazdan-Shahmorad, Hamid Soltanian-Zadeh
2008 Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing  
Diffusion tensor imaging (DTI) is known to be promising for providing anatomical information about white-matter fiber bundles that cannot be obtained by other non-invasive in vivo imaging methods. However, its application is limited because of its low signal-to-noise ratio and significant imaging artifacts. To improve the accuracy of tissue structural and architectural characterization with diffusion tensor imaging 4D wavelet denoising technique is used to improve the signal to noise ratio
more » ... of diffusion tensor images. To evaluate the proposed method, a high SNR data set is built by repeating and averaging the data acquisition several times and is compared to the denoised data. Our results revealed that wavelets would effectively reduce the noise in DTI data with less blurring of tissue types, especially in the white matter. It would suggest that by using the 4D wavelet noise suppression, one could decrease the acquisition time and still have an acceptable SNR.
doi:10.1109/icassp.2008.4517658 dblp:conf/icassp/JahanianYS08 fatcat:thn7jdzmlngmnpyvabcfc7ni64