Zhao Kai, Pang Kaifeng, Hung Alex LingYu, Zheng Haoxin, Yan Ran, Sung Kyunghyun
Department of Radiological Sciences, University of California, Los Angeles, CA 92521, USA.
Department of Electrical and Computer Engineering, University of California, Los Angeles, CA 92521, USA.
Cancers (Basel). 2024 Aug 27;16(17):2983. doi: 10.3390/cancers16172983.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) measures microvascular perfusion by capturing the temporal changes of an MRI contrast agent in a target tissue, and it provides valuable information for the diagnosis and prognosis of a wide range of tumors. Quantitative DCE-MRI analysis commonly relies on the nonlinear least square (NLLS) fitting of a pharmacokinetic (PK) model to concentration curves. However, the voxel-wise application of such nonlinear curve fitting is highly time-consuming. The arterial input function (AIF) needs to be utilized in quantitative DCE-MRI analysis. and in practice, a population-based arterial AIF is often used in PK modeling. The contribution of intravascular dispersion to the measured signal enhancement is assumed to be negligible. The MR dispersion imaging (MRDI) model was recently proposed to account for intravascular dispersion, enabling more accurate PK modeling. However, the complexity of the MRDI hinders its practical usability and makes quantitative PK modeling even more time-consuming. In this paper, we propose fast MR dispersion imaging (fMRDI) to effectively represent the intravascular dispersion and highly accelerated PK parameter estimation. We also propose a deep learning-based, two-stage framework to accelerate PK parameter estimation. We used a deep neural network (NN) to estimate PK parameters directly from enhancement curves. The estimation from NN was further refined using several steps of NLLS, which is significantly faster than performing NLLS from random initializations. A data synthesis module is proposed to generate synthetic training data for the NN. Two data-processing modules were introduced to improve the model's stability against noise and variations. Experiments on our in-house clinical prostate MRI dataset demonstrated that our method significantly reduces the processing time, produces a better distinction between normal and clinically significant prostate cancer (csPCa) lesions, and is more robust against noise than conventional DCE-MRI analysis methods.
动态对比增强磁共振成像（DCE-MRI）通过捕捉目标组织中MRI造影剂的时间变化来测量微血管灌注，并为多种肿瘤的诊断和预后提供有价值的信息。定量DCE-MRI分析通常依赖于药代动力学（PK）模型对浓度曲线的非线性最小二乘（NLLS）拟合。然而，这种非线性曲线拟合在体素层面的应用非常耗时。在定量DCE-MRI分析中需要使用动脉输入函数（AIF），而在实际应用中，基于群体的动脉AIF常用于PK建模。血管内弥散对测量信号增强的贡献被认为可以忽略不计。最近提出了MR弥散成像（MRDI）模型来考虑血管内弥散，从而实现更准确的PK建模。然而，MRDI的复杂性阻碍了其实际可用性，并且使定量PK建模更加耗时。在本文中，我们提出了快速MR弥散成像（fMRDI），以有效表示血管内弥散并实现高度加速的PK参数估计。我们还提出了一种基于深度学习的两阶段框架来加速PK参数估计。我们使用深度神经网络（NN）直接从增强曲线估计PK参数。通过NLLS的几个步骤进一步细化来自NN的估计，这比从随机初始化执行NLLS要快得多。提出了一个数据合成模块来为NN生成合成训练数据。引入了两个数据处理模块以提高模型对噪声和变化的稳定性。在我们内部的临床前列腺MRI数据集上进行的实验表明，我们的方法显著减少了处理时间，能更好地区分正常和具有临床意义的前列腺癌（csPCa）病变，并且比传统的DCE-MRI分析方法对噪声更具鲁棒性。
