Vignon-Clementel Irene E, Jagiella Nick, Dichamp Jules, Kowalski Jérôme, Lederle Wiltrud, Laue Hendrik, Kiessling Fabian, Sedlaczek Oliver, Drasdo Dirk
Inria, Palaiseau, France.
Institute for Experimental Molecular Imaging (ExMI), University Clinic and Helmholtz Institute for Biomedical Engineering, RWTH Aachen University, Aachen, Germany.
Front Bioinform. 2023 Apr 13;3:977228. doi: 10.3389/fbinf.2023.977228. eCollection 2023.
Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed . Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models.
动态对比增强(DCE)灌注成像已显示出通过其特征性组织特征对癌症发展及其治疗进行无创评估的巨大潜力。不同的示踪剂动力学模型正被用于从DCE灌注成像中估计组织和肿瘤灌注参数。这项工作的目标是提供一个基于模型的流程,以评估这些DCE成像参数如何与真实组织参数相关。由于组织学数据提供了详细的微观结构参数而非功能参数,这项工作也有助于更好地解释此类数据。为此构建了血管系统,并模拟了造影剂在组织中的扩散。作为原理验证,我们展示了在团注后从造影剂灌注数据评估两种示踪剂动力学模型的过程。构建了代表性的微血管动脉和静脉树。计算不同血管中的血流。对供血动脉中的造影剂输入、血管内运输、血管外交换和间质空间内的扩散进行建模。从这个时空模型中计算强度图,从而得到动态灌注图像。研究了各种肿瘤血管生成(结构和功能),并显示出肿瘤形态类型的时空对比成像动态特征。然后应用DCE成像中常用的Brix II(也称为2CXM)和扩展的Tofts示踪剂动力学模型来恢复灌注参数,并将其与时空模型的真实参数进行比较。结果表明,在一定的渗透率范围内可以很好地识别肿瘤特征。这项工作的模拟结果表明,明确考虑空间因素来估计灌注参数可能会显著改善当前示踪剂动力学模型的灌注解释。