用T1 MRI生成高精度3D FA图，省去额外扫描
Revolutionizing Brain Tumor Imaging: Generating Synthetic 3D FA Maps from T1-Weighted MRI using CycleGAN Models
- 用无配对数据训练的CycleGAN模型，从T1图像合成FA图
- 肿瘤区域SSIM达0.82，PSNR超30dB，保真度优异
- 临床可用，可减少患者做额外DTI扫描的需求
分数各向异性（FA）和方向编码色（DEC）图在神经影像中对评估白质完整性和结构连接至关重要。然而，FA图与纤维追踪图谱间的空间错位限制了其在预测模型中的有效应用。为此，我们提出一种基于CycleGAN的方法，直接从T1加权磁共振成像生成FA图，首次将该技术应用于健康及肿瘤组织。模型在未配对数据上训练，生成的图像保真度高，经结构相似性指数（SSIM）和峰值信噪比（PSNR）严格评估，在肿瘤区域表现尤为稳健。放射科评估进一步证实，该方法具备提升临床流程的潜力，可作为减少额外扫描的AI替代方案。
原文摘要 · Abstract (English)
Fractional anisotropy (FA) and directionally encoded colour (DEC) maps are essential for evaluating white matter integrity and structural connectivity in neuroimaging. However, the spatial misalignment between FA maps and tractography atlases hinders their effective integration into predictive models. To address this issue, we propose a CycleGAN based approach for generating FA maps directly from T1-weighted MRI scans, representing the first application of this technique to both healthy and tumour-affected tissues. Our model, trained on unpaired data, produces high fidelity maps, which have been rigorously evaluated using Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), demonstrating particularly robust performance in tumour regions. Radiological assessments further underscore the model's potential to enhance clinical workflows by providing an AI-driven alternative that reduces the necessity for additional scans.
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