基于双流Transformer结构的多能计算机断层扫描成像投影数据去噪方法
Projection Domain Denoising Method for Multi-Energy Computed Tomography via Dual-Stream Transformer
欧阳顺馨 1史再峰 2孔凡宁 1张丽丽 1曹清洁3
作者信息
- 1. 天津大学微电子学院,天津 300072
- 2. 天津大学微电子学院,天津 300072;天津市成像与感知微电子技术重点实验室,天津 300072
- 3. 天津师范大学数学科学学院,天津 300387
- 折叠
摘要
多能计算机断层扫描(Computed tomography,CT)技术可以更加精确地分辨出人体组织对不同能量X射线光子的吸收情况,是医学成像领域的重要发展方向.为了解决因量子噪声等非理想效应加重导致重建图像质量急剧退化的问题,提出了一种基于移位窗口多头自注意力机制的双流Transformer网络结构.该结构利用移位窗口Transformer和局部增强窗口Transformer分别提取投影数据的全局和局部特征,充分利用投影数据的非局部自相似性以保留投影数据的内部结构;然后通过残差卷积融合提取的特征;最后使用带有非局部全变分的混合损失函数来监督网络模型的训练,提升该网络模型对投影数据内部细节的敏感程度.实验结果表明,所提方法处理后的重建图像峰值信噪比(PSNR)值、结构相似性(SSIM)值和特征相似度(FSIM)值分别达到37.7301 dB、0.9944和0.9961.与目前先进的多能CT去噪方法相比,所提方法在去除低剂量多能CT投影数据噪声的同时,可保留更多的细节特征,有利于后续的精确诊断.
Abstract
The multi-energy computed tomography(CT)technique can resolve the absorption rates of various energy X-ray photons in human tissues,representing a significant advancement in medical imaging.By addressing the challenge of swift degradation in reconstructed image quality,primarily due to non-ideal effects such as quantum noise,a dual-stream Transformer network structure is introduced.This structure utilises the shifted-window multi-head self-attention denoising approach for projection data.The shifted windows Transformer extracts the global features of the projection data,while the locally-enhanced window Transformer focuses on local features.This dual approach capitalizes on the non-local self-similarity of the projection data to maintain its inherent structure,subsequently merged by residual convolution.For model training oversight,a hybrid loss function incorporating non-local total variation is employed,which enhances the network model's sensitivity to the inner details of the projected data.Experimental results demonstrate that our method's processed projection data achieve a peak signal to noise ratio(PSNR)of 37.7301 dB,structure similarity index measurement(SSIM)of 0.9944,and feature similarity index measurement(FSIM)of 0.9961.Relative to leading denoising techniques,the proposed method excels in noise reduction while preserving more inner features,crucial for subsequent accurate diagnostics.
关键词
图像处理/计算机断层扫描成像/低剂量/卷积神经网络/Transformer网络Key words
image processing/computed tomography/low dose/convolutional neural network/Transformer network引用本文复制引用
出版年
2024
NETL
NSTL
万方数据