Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T
Diffusion parameter mapping with the combined intravoxel incoherent motion and kurtosis model using artificial neural networks at 3 T
复制标题
DOI：
10.1002/nbm.3833
复制
发表时间：
2017-12-01
影响因子：
2.9
通讯作者：
Schad, Lothar R.
中科院分区：
文献类型：
--
作者：
Bertleff, Marco;Domsch, Sebastian;Schad, Lothar R.
人工神经网络（ANN）用于体素方式的参数估计与相结合的体素内非相干运动（IVIM）和峰度模型，促进强大的扩散参数映射在人脑中。在蒙特-卡罗模拟和活体实验中，将所提出的人工神经网络方法与传统的最小二乘回归（LSR）和最先进的多步拟合（LSR-MS）方法在估计精度和精度、异常值数量和区分灰色（GM）和白色（WM）物质的灵敏度方面进行了比较。所提出的ANN方法和LSR-MS都产生了视觉上增加的参数图质量。所有参数（灌注分数f、扩散系数D、伪扩散系数D*、峰度K）的估计值与使用ANN的文献吻合良好，而LSR-MS导致D* 高估，LSR产生f和D* 值增加以及K值降低。使用ANN，减少参数f（ANN，1%; LSR-MS，19%; LSR，8%）、D*（ANN，21%; LSR-MS，25%; LSR，23%）和K（ANN，0%; LSR-MS，0%; LSR，15%）的离群值。此外，人工神经网络能够显着区分GM和WM之间的所有参数的基础上，而LSR促进这种区分仅基于D和LSR-MS上的f，D和K。总体而言，所提出的人工神经网络方法被认为是上级传统的LSR，提出了一个强大的替代国家的最先进的方法LSR-MS的几个优势，在IVIM峰度参数的估计，这可能有助于增强扩散模型在临床扫描时间的适用性增加。
Artificial neural networks (ANNs) were used for voxel-wise parameter estimation with the combined intravoxel incoherent motion (IVIM) and kurtosis model facilitating robust diffusion parameter mapping in the human brain. The proposed ANN approach was compared with conventional least-squares regression (LSR) and state-of-the-art multi-step fitting (LSR-MS) in Monte-Carlo simulations and in vivo in terms of estimation accuracy and precision, number of outliers and sensitivity in the distinction between grey (GM) and white (WM) matter. Both the proposed ANN approach and LSR-MS yielded visually increased parameter map quality. Estimations of all parameters (perfusion fraction f, diffusion coefficient D, pseudo-diffusion coefficient D*, kurtosis K) were in good agreement with the literature using ANN, whereas LSR-MS resulted in D* overestimation and LSR yielded increased values for f and D*, as well as decreased values for K. Using ANN, outliers were reduced for the parameters f (ANN, 1%; LSR-MS, 19%; LSR, 8%), D* (ANN, 21%; LSR-MS, 25%; LSR, 23%) and K (ANN, 0%; LSR-MS, 0%; LSR, 15%). Moreover, ANN enabled significant distinction between GM and WM based on all parameters, whereas LSR facilitated this distinction only based on D and LSR-MS on f, D and K. Overall, the proposed ANN approach was found to be superior to conventional LSR, posing a powerful alternative to the state-of-the-art method LSR-MS with several advantages in the estimation of IVIM-kurtosis parameters, which might facilitate increased applicability of enhanced diffusion models at clinical scan times.