Keywords: Data Analysis, Diffusion Tensor ImagingDiffusion Weighted Imaging (DWI) can be disrupted due to acquisition constraints or imaging artifacts, which can lead to unreliable scalar metrics calculated and valuable scans discarded as a result. To reduce the impact of disrupted brain regions in DTI, we adapted a deep learning DTI inpainting network to reconstruct the disrupted ROIs. We evaluated the performance of the method by calculating the Fractional Anisotropy errors according to each individual brain region. Experimental results show that the inpainting method can reconstruct the relevant clinical imaging information by mitigating the Fractional Anisotropy differences overall and in individual disrupted brain regions.
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