南方医科大学学报

南方医科大学学报 ›› 2023, Vol. 43 ›› Issue (7): 1224-1232.doi: 10.12122/j.issn.1673-4254.2023.07.19

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基于3D U-Net和DTI模型约束的扩散张量场估计方法

麦兆华，李嘉龙，冯衍秋，张鑫媛   

  1. 南方医科大学生物医学工程学院，广东省医学图像处理重点实验室，广东省医学成像与诊断技术工程实验室，广东 广州 510515
  • 出版日期:2023-07-20 发布日期:2023-07-20

Diffusion tensor field estimation based on 3D U-Net and diffusion tensor imaging model constraint

MAI Zhaohua, LI Jialong, FENG Yanqiu, ZHANG Xinyuan   

  1. School of Biomedical Engineering, Southern Medical University, Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Guangzhou 510515, China
  • Online:2023-07-20 Published:2023-07-20

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摘要： 目的 为从少量的低信噪比扩散加权（DW）图像中估计得到准确的扩散张量成像（DTI）量化参数，本文提出一种基于3D U-Net和DTI模型约束的扩散张量场估计网络（3D DTI-Unet）。方法 3D DTI-Unet的输入为有噪声的扩散磁共振成像（dMRI）数据（包含1幅非扩散加权图像与6幅不同扩散编码方向的DW图像），通过3D U-Net 网络预测得到降噪后的非扩散加权图像以及准确的扩散张量场，并通过DTI模型重建得到dMRI数据，将其与dMRI数据的真实值进行比较来优化网络，从而保证dMRI数据与扩散张量场的物理模型一致性。为验证所提方法的有效性，与Marchenko-Pastur主成分分析（MP-PCA）和基于全局指导下的局部高阶奇异值分解（GL-HOSVD）这两种扩散加权图像去噪算法进行实验对比。结果 从DW图像、扩散张量场以及DTI量化参数的定量分析结果以及视觉效果来看，所提方法均优于MP-PCA与GL-HOSVD。结论 本文所提方法能够从1幅非扩散加权图像和6幅DW图像得到准确的DTI量化参数，可减少临床采集时间，提高临床量化诊断的可靠性。

关键词: 扩散张量成像；张量场估计；3D U-Net；Rician噪声；图像去噪

Abstract: Objective 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. Methods 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. Results 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. Conclusion 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.

Key words: diffusion tensor imaging; tensor field estimation; 3D U-Net; Rician noise; image denoising