Mai Z, Li J, Feng Y, Zhang X
School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Guangzhou 510515, China.
Nan Fang Yi Ke Da Xue Xue Bao. 2023 Jul 20;43(7):1224-1232. doi: 10.12122/j.issn.1673-4254.2023.07.19.
To propose a diffusion tensor field estimation network based on 3D U-Net and diffusion tensor imaging (DTI) model constraint (3D DTI-Unet) to accurately estimate DTI quantification parameters from a small number of diffusion-weighted (DW) images with a low signal-to-noise ratio.
The input of 3D DTI-Unet was noisy diffusion magnetic resonance imaging (dMRI) data containing one non-DW image and 6 DW images with different diffusion coding directions. The noise-reduced non-DW image and accurate diffusion tensor field were predicted through 3D U-Net. The dMRI data were reconstructed using the DTI model and compared with the true value of dMRI data to optimize the network and ensure the consistency of the dMRI data with the physical model of the diffusion tensor field. We compared 3D DTI-Unet with two DW image denoising algorithms (MP-PCA and GL-HOSVD) to verify the effect of the proposed method.
The proposed method was better than MP-PCA and GL-HOSVD in terms of quantitative results and visual evaluation of DW images, diffusion tensor field and DTI quantification parameters.
The proposed method can obtain accurate DTI quantification parameters from one non-DW image and 6 DW images to reduce image acquisition time and improve the reliability of quantitative diagnosis.
提出一种基于三维U-Net和扩散张量成像（DTI）模型约束的扩散张量场估计网络（三维DTI-Unet），以便从少量信噪比较低的扩散加权（DW）图像中准确估计DTI量化参数。
三维DTI-Unet的输入是包含一幅非DW图像和6幅具有不同扩散编码方向的DW图像的有噪声扩散磁共振成像（dMRI）数据。通过三维U-Net预测去噪后的非DW图像和准确的扩散张量场。使用DTI模型对dMRI数据进行重建，并与dMRI数据的真实值进行比较，以优化网络并确保dMRI数据与扩散张量场物理模型的一致性。我们将三维DTI-Unet与两种DW图像去噪算法（MP-PCA和GL-HOSVD）进行比较，以验证所提方法的效果。
在所提方法的定量结果以及对DW图像、扩散张量场和DTI量化参数的视觉评估方面，该方法优于MP-PCA和GL-HOSVD。
所提方法可以从一幅非DW图像和6幅DW图像中获得准确的DTI量化参数，以减少图像采集时间并提高定量诊断的可靠性。
