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Abstract #2439

Improved image quality with Deep learning based denoising of diffusion MRI data

Radhika Madhavan1, Jaemin Shin2, Nastaren Abad1, Luca Marinelli1, J Kevin DeMarco3, Robert Y Shih3, Vincent B Ho3, Suchandrima Banerjee4, and Thomas K Foo1
1GE Global Research, Niskayuna, NY, United States, 2GE Healthcare, New York, NY, United States, 3Walter Reed National Military Medical Center and Uniformed Services University of the Health Sciences, Bethesda, MD, United States, 4GE Healthcare, Menlo Park, CA, United States

Structure-preserved denoising of MRI images is a critical step in medical image analysis. This is particularly critical in diffusion MRI where higher spatial and angular resolutions required to map tissue microstructure in low SNR (especially at higher b-values) situations, if longer acquisition times are not used. Denoising using deep convolutional neural networks (DCNN) can reduce noise without requiring extensive averaging, enabling shorter scan times and high image quality, especially in the resulting tensor-derived maps. Preliminary results using DCNN based denoising on multi-shell diffusion data demonstrates improved image quality and reduced noise, without compromising on structural integrity and tensor derived metrics.

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