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Abstract #2476

Model Based Denoising of Diffusion MRI Reduces Bias in Tensor Derived Parameters and Connectivity Measures

Nastaren Abad1, Luca Marinelli1, Radhika Madhavan1, and Tom K.F Foo1
1General Electric Global Research, Niskayuna, NY, United States

Higher spatial and angular resolution is essential in diffusion MRI to resolve fiber and structural ambiguities. However, quantitative measures are confounded by low SNR, particularly at high b-values, compensation of which leads to longer acquisition times. In this study, the fundamental question asked is: Can denoising aid the stability of the measurement in the presence of increasing noise? Model based denoising was used to explore accelerated sampling by evaluating bias developed in qualitative and quantitative end points. Experimental results highlight superior performance, compared to ground truth, in noise and bias reduction in metrics along with structure preservation.

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