Istituto di Tecnologie Biomediche, Consiglio Nazionale delle Ricerche, Segrate, Italy.
Department of Radiology, Northwestern University, Chicago, Illinois, USA.
NMR Biomed. 2022 Oct;35(10):e4774. doi: 10.1002/nbm.4774. Epub 2022 Jun 6.
Extraction of intravoxel incoherent motion (IVIM) parameters from noisy diffusion-weighted (DW) images using a biexponential fitting model is computationally challenging, and the reliability of the estimated perfusion-related quantities represents a limitation of this technique. Artificial intelligence can overcome the current limitations and be a suitable solution to advance use of this technique in both preclinical and clinical settings. The purpose of this work was to develop a deep neural network (DNN) approach, trained on numerical simulated phantoms with different signal to noise ratios (SNRs), to improve IVIM parameter estimation. The proposed approach is based on a supervised fully connected DNN having 3 hidden layers, 18 inputs and 3 targets with standardized values. 14 × 10 simulated DW images, based on a Shepp-Logan phantom, were randomly generated with varying SNRs (ranging from 10 to 100). 7 × 10 images (1000 for each SNR) were used for training. Performance accuracy was assessed in simulated images and the proposed approach was compared with the state-of-the-art Bayesian approach and other DNN algorithms. The DNN approach was also evaluated in vivo on a high-field MRI preclinical scanner. Our DNN approach showed an overall improvement in accuracy when compared with the Bayesian approach and other DNN methods in most of the simulated conditions. The in vivo results demonstrated the feasibility of the proposed approach in real settings and generated quantitative results comparable to those obtained using the Bayesian and unsupervised approaches, especially for D and f, and with lower variability in homogeneous regions. The DNN architecture proposed in this work outlines two innovative features as compared with other studies: (1) the use of standardized targets to improve the estimation of parameters, and (2) the implementation of a single DNN to enhance the IVIM fitting at different SNRs, providing a valuable alternative tool to compute IVIM parameters in conditions of high background noise.
从存在噪声的扩散加权（DW）图像中提取双指数拟合模型的体素内不相干运动（IVIM）参数在计算上具有挑战性，并且所估计的与灌注相关的量的可靠性是该技术的一个局限性。人工智能可以克服当前的局限性，是推进该技术在临床前和临床环境中应用的合适解决方案。这项工作的目的是开发一种基于深度神经网络（DNN）的方法，该方法经过不同信噪比（SNR）的数值模拟体模训练，以提高 IVIM 参数估计的准确性。所提出的方法基于具有 3 个隐藏层的监督全连接 DNN，具有 18 个输入和 3 个标准化值的目标。基于 Shepp-Logan 体模，随机生成了 14×10 个具有不同 SNR（范围从 10 到 100）的 DW 模拟图像。使用了 7×10 个图像（每个 SNR 为 1000 个）进行训练。在模拟图像中评估了性能准确性，并将所提出的方法与最先进的贝叶斯方法和其他 DNN 算法进行了比较。还在高场 MRI 临床前扫描仪上对 DNN 方法进行了体内评估。与贝叶斯方法和其他 DNN 方法相比，所提出的 DNN 方法在大多数模拟条件下的准确性都有了整体提高。体内结果证明了所提出的方法在实际环境中的可行性，并生成了与使用贝叶斯和无监督方法获得的结果相当的定量结果，特别是对于 D 和 f，并且在同质区域的变异性更低。与其他研究相比，这项工作中提出的 DNN 架构具有两个创新特点：（1）使用标准化目标来提高参数估计的准确性，（2）实施单个 DNN 来增强不同 SNR 下的 IVIM 拟合，为在高背景噪声条件下计算 IVIM 参数提供了有价值的替代工具。
