Abstract #4634

Exploiting diffusion MRI data redundancy on a denoising framework - application on MR images (OGSE, μFA protocols) of the Marmoset Brain (in-vivo)

Vinicius P. Campos¹, Tales Santini², Corey Baron², and Marcelo A. C. Vieira¹

¹Computer and Electrical Engineering, University of São Paulo, São Carlos, Brazil, ²Robarts Research Institute, Western University, London, ON, Canada

Synopsis

Keywords: Data Processing, Data Analysis, DenoiseOscillating gradients spin-echo (OGSE) and microscopic fractional anisotropy (μFA) are diffusion MRI advanced techniques able to provide additional information of the microstructures of the brain^1-5, when compared to traditional diffusion MRI. However, high-resolution DWI images present low signal to noise ratio (SNR). In this work, we presented a different framework, named VST_dMRI_BM4D, for denoising dMRI data by exploiting data redundancy and using the variance stabilization transformation (VST) concept^7-9. Results show the proposed method is comparable to, and in some cases superior than, MPPCA⁶, potentially making it a useful tool to be used in the dMRI field.

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