Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.
Deep learning quantification of vascular pharmacokinetic parameters in mouse brain tumor models.
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DOI：
10.31083/j.fbl2703099
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发表时间：
2022-03-16
影响因子：
3.1
通讯作者：
Zhao, Dawen
中科院分区：
文献类型：
--
作者：
Arledge, Chad A.;Sankepalle, Deeksha M.;Crowe, William N.;Liu, Yang;Wang, Lulu;Zhao, Dawen
关键词：
动态对比增强（DCE）MRI被广泛用于评估癌症的血管灌注和渗透性。在小动物应用中，来自DCE MRI图像的药代动力学（PK）参数的常规建模是复杂且耗时的。本研究旨在开发一种深度学习方法，以完全自动生成动力学参数图Ktranss（体积传递系数）和Vp（血浆体积比），作为基于DCE MRI的小鼠脑肿瘤模型中常规PK建模的潜在替代。使用7T MRI，在裸鼠原位生长的U87胶质瘤异种移植物中进行DCE MRI。使用经典Tofts模型以及扩展Tofts模型生成血管渗透性Ktranss和Vp图。然后将这些血管渗透性图作为目标图像处理到二十四层卷积神经网络（CNN）。CNN在T1加权的DCE图像上进行训练作为源图像，并设计了并行的双通道来捕获多尺度特征。此外，我们在乳腺癌脑转移（BCBM）小鼠模型上进行了这种胶质瘤训练CNN的转移研究，以评估该网络用于替代性脑肿瘤的潜力。我们的数据显示，在靶PK参数图和神经胶质瘤的相应CNN图之间生成的Ktranss和Vp图都具有良好的匹配。逐像素分析揭示了肿瘤内的异质渗透性，这在CNN和PK模型之间是一致的。在BCBM的迁移研究中进一步证明了深度学习方法的实用性。由于其直接从DCE动态图像中快速准确地估计血管PK参数，而无需复杂的数学建模，因此深度学习方法可以作为评估肿瘤血管通透性的有效工具，以促进小动物脑肿瘤研究。
Dynamic contrast-enhanced (DCE) MRI is widely used to assess vascular perfusion and permeability in cancer. In small animal applications, conventional modeling of pharmacokinetic (PK) parameters from DCE MRI images is complex and time consuming. This study is aimed at developing a deep learning approach to fully automate the generation of kinetic parameter maps, Ktrans (volume transfer coefficient) and Vp (blood plasma volume ratio), as a potential surrogate to conventional PK modeling in mouse brain tumor models based on DCE MRI. Using a 7T MRI, DCE MRI was conducted in U87 glioma xenografts growing orthotopically in nude mice. Vascular permeability Ktrans and Vp maps were generated using the classical Tofts model as well as the extended-Tofts model. These vascular permeability maps were then processed as target images to a twenty-four layer convolutional neural network (CNN). The CNN was trained on T1-weighted DCE images as source images and designed with parallel dual pathways to capture multiscale features. Furthermore, we performed a transfer study of this glioma trained CNN on a breast cancer brain metastasis (BCBM) mouse model to assess the potential of the network for alternative brain tumors. Our data showed a good match for both Ktrans and Vp maps generated between the target PK parameter maps and the respective CNN maps for gliomas. Pixel-by-pixel analysis revealed intratumoral heterogeneous permeability, which was consistent between the CNN and PK models. The utility of the deep learning approach was further demonstrated in the transfer study of BCBM. Because of its rapid and accurate estimation of vascular PK parameters directly from the DCE dynamic images without complex mathematical modeling, the deep learning approach can serve as an efficient tool to assess tumor vascular permeability to facilitate small animal brain tumor research.
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影响因子：
2.6
作者：
Crowe, William;Wang, Lulu;Zhao, Dawen
通讯作者：
Zhao, Dawen
影响因子：
4.4
作者：
Cheng, Hai-Ling Margaret
通讯作者：
Cheng, Hai-Ling Margaret
影响因子：
7.5
作者：
Nalepa, Jakub;Lorenzo, Pablo Ribalta;Hayball, Michael P.
通讯作者：
Hayball, Michael P.
DOI：
10.1007/978-3-030-33128-3_1
发表时间：
2020-01-01
期刊：
DEEP LEARNING IN MEDICAL IMAGE ANALYSIS: CHALLENGES AND APPLICATIONS
影响因子：
--
作者：
Chan, Heang-Ping;Samala, Ravi K.;Zhou, Chuan
通讯作者：
Zhou, Chuan
影响因子：
3.5
作者：
Dujardin, Martine I.;Sourbron, Steven P.;Luypaert, Robert
通讯作者：
Luypaert, Robert