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MRI对比剂在目标组织中的时间变化来测量微血管灌注，为多种肿瘤的诊断和预后提供了宝贵信息。定量DCE-MRI分析通常依赖于药代动力学（PK）模型对浓度曲线进行非线性最小二乘（NLLS）拟合。然而，这种非线性曲线拟合的逐体素应用非常耗时。定量DCE-MRI分析需要使用动脉输入函数（AIF），实践中常采用基于人群的动脉AIF进行PK建模，并假设血管内弥散对测量信号增强的贡献可忽略不计。近期提出的磁共振弥散成像（MRDI）模型考虑了血管内弥散效应，可实现更精确的PK建模。但MRDI的复杂性限制了其实际应用，并使定量PK建模更加耗时。本文提出快速磁共振弥散成像（fMRDI）方法，以有效表征血管内弥散并大幅加速PK参数估计。同时，我们提出基于深度学习的两阶段框架来加速PK参数估计：首先使用深度神经网络（NN）直接从增强曲线估计PK参数，再通过若干步NLLS对神经网络估计结果进行细化优化，该方法相比随机初始化的NLLS计算显著提速。我们还设计了数据合成模块来生成神经网络的合成训练数据，并引入两个数据处理模块以提升模型对噪声和变异的稳定性。在我们内部临床前列腺MRI数据集上的实验表明：与传统DCE-MRI分析方法相比，本方法显著缩短了处理时间，能更好地区分正常组织与临床显著性前列腺癌（csPCa）病灶，且对噪声具有更强的鲁棒性。
A Deep Learning-Based Framework for Highly Accelerated Prostate MR Dispersion Imaging
肿瘤(癌症)患者之家