Scholarlink

<p>Assessment of white matter integrity is critical for predicting functional recovery after ischemic stroke. However, conventional magnetic resonance imaging (MRI) cannot capture tract-specific microstructural changes, and diffusion tensor imaging (DTI) is limited by prolonged acquisition times. This study aimed to synthesize fractional anisotropy (FA) maps from routinely acquired T1-weighted (T1) MRI using 2.5D inputs within a generative adversarial network (GAN) framework. Specifically, our primary objective was to evaluate the relative efficacy of a proposed transfer learning strategy compared to single-domain training approaches. T1–FA paired data from 375 cognitively normal participants (832 images) from the Alzheimer’s Disease Neuroimaging Initiative served as the non-lesion dataset, while longitudinal MRI data from 69 ischemic stroke patients (236 images) were from a single-center cohort. Three models were evaluated: the non-lesion-trained (NLT) model trained on non-lesion data, the lesion-trained (LT) model trained on stroke data, and the NLT model further fine-tuned on the stroke dataset (NLT + LF). Model performance was evaluated using voxel-wise errors (mean absolute error (MAE) and root mean square error (RMSE)), structural similarity (peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM)), spatial overlap (Dice coefficient (Dice)), and distributional similarity (Kullback–Leibler divergence). Bonferroni-corrected paired t-tests showed that NLT + LF showed significantly better performance than NLT across all evaluated regions, including the whole brain, white matter, and lesions (all <i>p</i> &lt; 0.001). Compared with LT, NLT + LF showed superior performance for all metrics at <i>p</i> &lt; 0.001, except for lesion-region Dice and RMSE, which remained significant at <i>p</i> &lt; 0.01. The preservation of lesion-relevant features and anatomical fidelity, together with the capture of degeneration patterns, accompanied these gains. Overall, the proposed NLT + LF approach improved lesion-specific representation and established that high-fidelity FA maps can be reliably synthesized from T1 MRI. This transfer learning framework offers a practical alternative to DTI for clinical stroke assessment.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Generative Synthesis of Fractional Anisotropy Maps from T1 MRI Using Transfer Learning for White Matter Assessment in Stroke

  • 期刊论文

  • 发表日期：

    2026年4月24日

查看全文
  • Gyubin Kwon,
  • Hyunjin Kim,
  • Hongmin Kim,
  • Jungsoo Lee

摘要

Assessment of white matter integrity is critical for predicting functional recovery after ischemic stroke. However, conventional magnetic resonance imaging (MRI) cannot capture tract-specific microstructural changes, and diffusion tensor imaging (DTI) is limited by prolonged acquisition times. This study aimed to synthesize fractional anisotropy (FA) maps from routinely acquired T1-weighted (T1) MRI using 2.5D inputs within a generative adversarial network (GAN) framework. Specifically, our primary objective was to evaluate the relative efficacy of a proposed transfer learning strategy compared to single-domain training approaches. T1–FA paired data from 375 cognitively normal participants (832 images) from the Alzheimer’s Disease Neuroimaging Initiative served as the non-lesion dataset, while longitudinal MRI data from 69 ischemic stroke patients (236 images) were from a single-center cohort. Three models were evaluated: the non-lesion-trained (NLT) model trained on non-lesion data, the lesion-trained (LT) model trained on stroke data, and the NLT model further fine-tuned on the stroke dataset (NLT + LF). Model performance was evaluated using voxel-wise errors (mean absolute error (MAE) and root mean square error (RMSE)), structural similarity (peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM)), spatial overlap (Dice coefficient (Dice)), and distributional similarity (Kullback–Leibler divergence). Bonferroni-corrected paired t-tests showed that NLT + LF showed significantly better performance than NLT across all evaluated regions, including the whole brain, white matter, and lesions (all p < 0.001). Compared with LT, NLT + LF showed superior performance for all metrics at p < 0.001, except for lesion-region Dice and RMSE, which remained significant at p < 0.01. The preservation of lesion-relevant features and anatomical fidelity, together with the capture of degeneration patterns, accompanied these gains. Overall, the proposed NLT + LF approach improved lesion-specific representation and established that high-fidelity FA maps can be reliably synthesized from T1 MRI. This transfer learning framework offers a practical alternative to DTI for clinical stroke assessment.