用多样化数据增强提升超低场MRI图像质量，效果媲美高场标准。
Augment to Augment: Diverse Augmentations Enable Competitive Ultra-Low-Field MRI Enhancement
- 设计多种任务自适应增强策略，利用高场数据辅助训练
- 在50对3D数据上实现脑部区域结构相似性达第三名
- 适合医疗影像增强、低场MRI重建方向的研究者参考
超低场（ULF）MRI虽具更广可及性，但存在信噪比低、空间分辨率差及对比度偏离高场标准的问题。图像到图像的转换方法可将ULF图像映射为高场外观，但受限于配对训练数据稀少。在遵循ULF-EnC挑战要求（仅50对3D体积数据，不可使用外部数据）的前提下，我们研究了任务适配型数据增强对标准深度模型在ULF图像增强中的影响。结果表明，强而多样的增强策略（包括在高场数据上引入辅助任务）显著提升了重建保真度。我们的方案在公开验证集上以脑部掩码SSIM排名第三，在最终测试集上官方评分排名第四。代码已开源：https://github.com/fzimmermann89/low-field-enhancement。
原文摘要 · Abstract (English)
Ultra-low-field (ULF) MRI promises broader accessibility but suffers from low signal-to-noise ratio (SNR), reduced spatial resolution, and contrasts that deviate from high-field standards. Image-to-image translation can map ULF images to a high-field appearance, yet efficacy is limited by scarce paired training data. Working within the ULF-EnC challenge constraints (50 paired 3D volumes; no external data), we study how task-adapted data augmentations impact a standard deep model for ULF image enhancement. We show that strong, diverse augmentations, including auxiliary tasks on high-field data, substantially improve fidelity. Our submission ranked third by brain-masked SSIM on the public validation leaderboard and fourth by the official score on the final test leaderboard. Code is available at https://github.com/fzimmermann89/low-field-enhancement.
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