Abstract：
Objective. To improve intravoxel incoherent motion imaging (IVIM) magnetic resonance Imaging quality using a new image denoising technique and model-independent parameterization of the signal versusb-value curve.Approach. IVIM images were acquired for 13 head-and-neck patients prior to radiotherapy. Post-radiotherapy scans were also acquired for five of these patients. Images were denoised prior to parameter fitting using neural blind deconvolution, a method of solving the ill-posed mathematical problem of blind deconvolution using neural networks. The signal decay curve was then quantified in terms of several area under the curve (AUC) parameters. Improvements in image quality were assessed using blind image quality metrics, total variation (TV), and the correlations between parameter changes in parotid glands with radiotherapy dose levels. The validity of blur kernel predictions was assessed by the testing the method\'s ability to recover artificial \'pseudokernels\'. AUC parameters were compared with monoexponential, biexponential, and triexponential model parameters in terms of their correlations with dose, contrast-to-noise (CNR) around parotid glands, and relative importance via principal component analysis.Main results. Image denoising improved blind image quality metrics, smoothed the signal versusb-value curve, and strengthened correlations between IVIM parameters and dose levels. Image TV was reduced and parameter CNRs generally increased following denoising.AUCparameters were more correlated with dose and had higher relative importance than exponential model parameters.Significance. IVIM parameters have high variability in the literature and perfusion-related parameters are difficult to interpret. Describing the signal versusb-value curve with model-independent parameters like theAUCand preprocessing images with denoising techniques could potentially benefit IVIM image parameterization in terms of reproducibility and functional utility.
摘要：
目的：使用一种新的图像去噪技术和信号与b值曲线的模型无关参数化，以提高体素不相干运动成像（IVIM）磁共振成像（MRI）的质量。方法：在放疗前采集了13例头颈部患者的IVIM图像。还对五名患者进行了放疗后扫描。在参数拟合之前使用神经盲去卷积对图像进行去噪，用神经网络解决盲解卷积数学问题的方法。然后根据几个曲线下面积(AUC)参数对信号衰减曲线进行定量。使用盲图像质量度量来评估图像质量的改善，总变化(TV)，腮腺IVIM参数与放疗剂量水平的相关性强度。通过恢复已应用于去噪图像的人工“伪内核”的能力来评估模糊内核预测的准确性。AUC参数与表观扩散系数(ADC)比较,双指数，和三指数模型参数与剂量的相关性，腮腺内外对比噪声（CNR）比，以及通过主成分分析确定的它们的相对重要性。
主要结果：图像去噪改进了盲图像质量度量，平滑了信号对b值的曲线，并加强了IVIM参数与剂量之间的相关性。去噪后，图像TV降低，参数CNR通常增加。与传统IVIM参数相比，AUC参数变化与剂量具有更高的相关性和更高的相对重要性。意义：IVIM参数在文献中具有高度变异性，与灌注相关的参数难以解释。用与模型无关的参数（如AUC）描述信号与b值曲线，并用去噪技术预处理图像可能会在再现性和功能实用性方面使IVIM图像参数化受益。
