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Abstract #0337

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

Chih-Chien Tsai1, Yao-Liang Chen2, and Jiun-Jie Wang1,2,3
1Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan, 2Department of Diagnostic Radiology, Chang Gung Memorial Hospital at Keelung, Keelung, Taiwan, 3Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan, Taiwan

Synopsis

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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Keywords