Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Diffusion tensor imaging, convolutional neural network, curve fitting, mean diffusivity, fractional anisotropy
Motivation: Diffusion-MRI faced limitations due to extended scan times and scanner/protocol variations.
Goal(s): This study aims to assess its ability to accelerate imaging procedures and unify data from diverse sources.
Approach: A convolutional neural network was employed to reconstruct diffusion-weighted images into diffusion tensor images. The effectiveness of reconstructed model was evaluated by normalized mean-square error (NMSE) and structural similarity index (SSIM).
Results: The CNN showed significantly better SSIM and lower NMSE in FA and MD (p < 0.001) compared to conventional methods. Moreover, the CNN model maintained strong performance when applied to other Scanners for FA and MD.
Impact: Through convolutional neural networks, images might be acquired fast and easily be harmonized across platforms . Subsequent research will further utilize deep/machine learning tools to investigate the impact of reconstructed image-segmented brain regions on the performance of classification models.
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