Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei City, Taiwan.
Program for Precision Health and Intelligent Medicine, Graduate School of Advanced Technology, National Taiwan University, Taipei City, Taiwan.
NMR Biomed. 2024 Oct;37(10):e5201. doi: 10.1002/nbm.5201. Epub 2024 Jun 11.
Quantitative analysis of diffusion-weighted magnetic resonance imaging (DW-MRI) has been explored for many clinical applications since its development. In particular, the intravoxel incoherent motion (IVIM) model for DW-MRI has been commonly utilized in various organs. However, because of the presence of excessive noise, the IVIM parameter maps obtained from pixel-wise fitting are often unreliable. In this study, we propose a kernelized total difference-based curve-fitting method to estimate the IVIM parameters. Simulated DW-MRI data at five signal-to-noise ratios (i.e., 10, 20, 30, 50, and 100) and real abdominal DW-MRI data acquired on a 1.5-T MRI scanner with nine b-values (i.e., 0, 10, 25, 50, 100, 200, 300, 400, and 500 s/mm) and six diffusion-encoding gradient directions were used to evaluate the performance of the proposed method. The results were compared with those obtained by three existing methods: trust-region reflective (TRR) algorithm, Bayesian probability (BP), and deep neural network (DNN). Our simulation results showed that the proposed method outperformed the other three comparing methods in terms of root-mean-square error. Moreover, the proposed method could preserve small details in the estimated IVIM parameter maps. The experimental results showed that, compared with the TRR method, the proposed method as well as the BP (and DNN) method could reduce the overestimation of the pseudodiffusion coefficient and improve the quality of IVIM parameter maps. For all studied abdominal organs except the pancreas, both the proposed method and the BP method could provide IVIM parameter estimates close to the reference values; the former had higher precision. The kernelized total difference-based curve-fitting method has the potential to improve the reliability of IVIM parametric imaging.
自从扩散加权磁共振成像（DW-MRI）发展以来，其在许多临床应用中的定量分析已经得到了探索。特别是，DW-MRI 的体素内不相干运动（IVIM）模型已在各种器官中得到广泛应用。然而，由于存在过多的噪声，从像素级拟合获得的 IVIM 参数图通常是不可靠的。在本研究中，我们提出了一种基于核化总差的曲线拟合方法来估计 IVIM 参数。使用五种信噪比（即 10、20、30、50 和 100）的模拟 DW-MRI 数据和在 1.5-T MRI 扫描仪上采集的具有九个 b 值（即 0、10、25、50、100、200、300、400 和 500 s/mm 和六个扩散编码梯度方向）的真实腹部 DW-MRI 数据来评估所提出方法的性能。结果与通过三种现有方法获得的结果进行了比较：信任区域反射（TRR）算法、贝叶斯概率（BP）和深度神经网络（DNN）。我们的模拟结果表明，在所提出的方法在均方根误差方面优于其他三种比较方法。此外，所提出的方法可以在估计的 IVIM 参数图中保留小细节。实验结果表明，与 TRR 方法相比，所提出的方法以及 BP（和 DNN）方法可以减少假性扩散系数的高估并提高 IVIM 参数图的质量。对于除胰腺以外的所有研究腹部器官，所提出的方法和 BP 方法都可以提供接近参考值的 IVIM 参数估计；前者具有更高的精度。基于核化总差的曲线拟合方法有可能提高 IVIM 参数成像的可靠性。
