摘要
体素内不相干运动(Intravoxel Incoherent Motion,IVIM)模型利用扩散加权磁共振成像的原理(Diffusion-weighted Magnetic Resonance Imaging,DWI),能够无损获得生物活体组织的水分子扩散系数(D)和血液灌注信息(F,D^(*))。但是传统的IVIM参数估计方法对噪音敏感,特别是在肝脏等受呼吸运动影响的腹部器官,因此参数估计效果不佳。为了提高参数估计模型的噪音鲁棒性,提出一个先验驱动的神经网络(Prior-Driven Neural Network,PDNN),利用全监督训练自适应学习到的先验知识去指导无监督训练。使用均方误差根(Root Mean Square Errors,RMSE)在不同信噪比上评估模型的噪音鲁棒性,采用变异系数(Coefficient of Variation,CV)分布来区分肝脏健康组和肝硬化组之间的显著性差异,并与非线性最小二乘、基于体素的深度学习方法IVIM-NET optim和基于领域信息的2D卷积网络SSUN比较。结果表明,所提出的方法具有最好的噪音鲁棒性,拟合参数[D,F,D^(*)]在所有信噪比上的RMSE指标比次优方法分别低27.63%,23.72%,31.46%。此外,所提方法能更好地保存组织结构信息,有效区分了健康肝脏和肝硬化(CV分布具有显著性差异,P<0.05)。
Intravoxel incoherent motion(IVIM)model leverages diffusion-weighted magnetic resonance imaging(DWI)to non-invasively ascertain the diffusion coefficient of water molecules in living tissue(D)and to gather blood perfusion data(F,D^(*)).However,conventional methods for estimating IVIM parameters are particularly susceptible to noise,which poses a significant challenge in abdominal organs like the liver where respiratory motion is prevalent.This sensitivity often compromises the efficacy of parameter estimation.To enhance the robustness against noise,this study introduces a novel algorithm,the prior-driven neural network(PDNN).This approach harnesses prior knowledge derived from fully supervised training to inform and guide unsupervised learning phases.The robustness of PDNN model to noise is systematically assessed using root mean square errors(RMSE)across various signal-to-noise ratios.Additionally,the coefficient of variation(CV)distribution is employed to effectively differentiate between healthy and cirrhotic liver tissues,indicating significant variations(P<0.05)that underscore the model’s diagnostic capability.The performance of the PDNN algorithm is compared with other advanced methods,including the nonlinear least squares approach,the voxel-based deep learning method IVIM-NET_(optim),and SSUN,a 2D convolutional network grounded in domain-specific information.The results demonstrate that PDNN outperforms these methods in terms of noise robustness.Speci-fically,the RMSE values for the fitting parameters[D,F,D^(*)]in the proposed model are 27.63%,23.72%,and 31.46%lower,respectively,than those recorded by the sub-optimal method.Moreover,PDNN not only preserves the integrity of tissue structure information but also effectively distinguishes between healthy and cirrhotic livers,highlighting its potential as a superior tool for clinical diagnosis and evaluation.
作者
胡国栋
叶晨
HU Guodong;YE Chen(Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province(Guizhou University),Guiyang 550025,China;Engineering Research Center of Text Computing&Cognitive Intelligence,Ministry of Education(Guizhou University),Guiyang 550025,China;State Key Laboratory of Public Big Data(Guizhou University),Guiyang 550025,China;College of Computer Science and Technology,Guizhou University,Guiyang 550025,China)
出处
《计算机科学》
北大核心
2025年第6期211-218,共8页
Computer Science
基金
贵州省基础研究(自然科学)项目(黔科合基础-ZK[2023]一般058)
贵州大学博士基金(贵大人基合字(2021)17)
国家自然科学基金(62161004)
贵州省自然科学基金(黔科合基础[2020]1Y255)
贵州省科学技术基金重点项目(黔科合基础-ZK[2021]重点002)
贵州省科学项目(黔科合基础-ZK[2022]一般046)。
关键词
体素内不相干运动成像
参数估计
肝硬化
深度学习
Intravoxel incoherent motion imaging
Parameter estimation
Cirrhosis
Deep learning
