FSformer:用于LDCT消除噪音的联合频率分离网络和变压器
Jiaqi Kang1, Yi Liu1, Pengcheng Zhang1
1State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, 030051, China; School of Information and Communication Engineering, North University of China, Taiyuan, 030051, China.
Computers in biology and medicine
|March 30, 2024
概括
一个新的FSformer深度学习模型通过分离频率有效地消除低剂量计算机断层扫描 (LDCT) 图像. 这种方法显著提高了图像清晰度和诊断实用性,同时保留了关键细节.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 图像处理 图像处理
背景情况:
- 低剂量计算机断层扫描 (LDCT) 提供了减少的辐射暴露,但遭受了显著的噪音和文物.
- 在LDCT中图像质量的恶化妨碍了准确的医学诊断.
- 现有的无声化方法难以平衡降低噪声与保存细节.
研究的目的:
- 开发一种先进的深度学习模型,用于优越的低剂量计算机断层扫描 (LDCT) 图像消毒.
- 为了提高LDCT图像的清晰度和诊断准确度.
- 引入一种结合频率分离和变压器机制的新型网络架构.
主要方法:
- 提出了一个频率分离网络和变压器 (FSformer) 模型,用于LDCT的无声化.
- 使用频率分隔块将图像分解为低频和高频组件.
- 使用了变压器阶段用于噪声估计和重建预测块用于图像增强.
- 实现了一个复合损失函数,包括频率和Charbonnier损失.
主要成果:
- 在AAPM Mayo,Piglet和临床数据集上,FSformer实现了最先进的性能.
- 在梅奥医院的数据集上证明了最佳指标,PSNR为33.77dB,SSIM为0.9254.
- 展示了有效的噪音/文物抑制,同时保持了图像纹理和组织.
- 测试时间为1.825秒,表明计算效率.
结论:
- FSformer 代表了 LDCT 图像质量提升的重大进步.
- 该模型有效地抑制噪音和文物,这对于提高诊断准确度至关重要.
- FSformer表现出强度和在医学成像中广泛临床应用的潜力.
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