Accelerated Acquisition and Cross-Platform Reconstruction of Diffusion Tensor-Derived Indices Using Convolutional Neural Networks

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.