Image Distortion Correction for Diffusion MR Imaging Using a Transformer-based U-Net.

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Abstract

To evaluate the performance of a Transformer-based U-Net, TransDisCo, for distortion correction in clinical diffusion-weighted imaging (DWI), including high b-value DWI, diffusion tensor imaging (DTI), and diffusion kurtosis imaging (DKI) and, furthermore, to compare its efficacy with an established U-Net-based method (Synb0) on a clinical dataset including lesion cases. This study utilized a clinical dataset acquired at the University of Tokyo Hospital, comprising DWI data from patients (including cases with brain tumors and cerebrovascular disorders) suitable for DTI and DKI analysis. We compared the distortion-correction performance of our proposed TransDisCo with that of Synb0, a U-Net-based model. Both methods were trained on T1-weighted images and uncorrected DWI images, with FMRIB Software Library-corrected DWI images used as ground truth. To assess the contribution of the self-attention mechanism in TransDisCo, an ablation study was conducted using a convolutional neural network (CNN)-based variant, in which encoder Transformer blocks were replaced with convolutional blocks. We evaluated corrected DWI images and derived DTI/DKI maps using peak SNR, structural similarity, mutual information, and Dice coefficients. All 3 models corrected distortions in the clinical DWI datasets, including high b-value images and derived DTI/DKI maps. Furthermore, TransDisCo demonstrated significantly superior performance compared to Synb0 across quantitative metrics, particularly in preserving anatomical details and reducing artifacts, even in the presence of pathological changes. Additionally, in the ablation study, TransDisCo outperformed the CNN-based variant trained under identical conditions, indicating that the self-attention mechanism, which enables content-adaptive long-range feature interactions, of the Transformer-based encoder contributed to the observed performance gains. TransDisCo alleviates limitations of existing CNN-based models and represents a promising approach to DWI distortion correction that surpasses conventional U-Net-based methods on clinical data with diverse pathologies. It has the potential to improve the precision and reliability of diffusion MRI across a wide range of imaging conditions and clinical applications.

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