Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models.
Dynamic contrast enhanced (DCE) MRI estimation of vascular parameters using knowledge-based adaptive models.
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DOI：
10.1038/s41598-023-36483-9
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发表时间：
2023-06-14
影响因子：
4.6
通讯作者：
中科院分区：
文献类型：
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我们介绍并验证了四个自适应模型（AM），以执行基于生理学的嵌套模型选择（NMS）估计等微血管参数，如前向体积传输常数，Ktranss，血浆体积分数，VP，血管外，细胞外空间，VE，直接从动态对比度增强（DCE）MRI原始信息，而不需要动脉输入功能（AIF）。在66只植入人U-251癌细胞的免疫受损RNU大鼠中，DCE-MRI研究使用组平均放射性AIF和扩展的基于Patlak的NMS范例估计药代动力学（PK）参数。从原始DCE-MRI信息中提取的190个特征用于构建和验证（嵌套交叉验证，NCV）4个AM，以估计基于模型的区域及其3个PK参数。一个基于NMS的先验知识被用来微调的AM，以提高其性能。与传统分析相比，AM产生了血管参数的稳定图和受AIF分散影响较小的嵌套模型区域。对于嵌套模型区域、vp、Ktranss和ve的预测，AM的性能（NCV测试队列的相关系数和校正R平方）分别为：0.914/0.834、0.825/0.720、0.938/0.880和0.890/0.792。本研究证明了AM的应用，其相对于常规方法加快并改善了基于DCE-MRI的肿瘤和正常组织的微血管特性的量化。
We introduce and validate four adaptive models (AMs) to perform a physiologically based Nested-Model-Selection (NMS) estimation of such microvascular parameters as forward volumetric transfer constant, Ktrans, plasma volume fraction, vp, and extravascular, extracellular space, ve, directly from Dynamic Contrast-Enhanced (DCE) MRI raw information without the need for an Arterial-Input Function (AIF). In sixty-six immune-compromised-RNU rats implanted with human U-251 cancer cells, DCE-MRI studies estimated pharmacokinetic (PK) parameters using a group-averaged radiological AIF and an extended Patlak-based NMS paradigm. One-hundred-ninety features extracted from raw DCE-MRI information were used to construct and validate (nested-cross-validation, NCV) four AMs for estimation of model-based regions and their three PK parameters. An NMS-based a priori knowledge was used to fine-tune the AMs to improve their performance. Compared to the conventional analysis, AMs produced stable maps of vascular parameters and nested-model regions less impacted by AIF-dispersion. The performance (Correlation coefficient and Adjusted R-squared for NCV test cohorts) of the AMs were: 0.914/0.834, 0.825/0.720, 0.938/0.880, and 0.890/0.792 for predictions of nested model regions, vp, Ktrans, and ve, respectively. This study demonstrates an application of AMs that quickens and improves DCE-MRI based quantification of microvasculature properties of tumors and normal tissues relative to conventional approaches.
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影响因子：
3.3
作者：
Aryal, Madhava P.;Nagaraja, Tavarekere N.;Keenan, Kelly A.;Bagher-Ebadian, Hassan;Panda, Swayamprava;Brown, Stephen L.;Cabral, Glauber;Fenstermacher, Joseph D.;Ewing, James R.
通讯作者：
Ewing, James R.
影响因子：
3.1
作者：
Arledge, Chad A.;Sankepalle, Deeksha M.;Crowe, William N.;Liu, Yang;Wang, Lulu;Zhao, Dawen
通讯作者：
Zhao, Dawen
DOI：
10.1002/jmri.24469
发表时间：
2014-11
期刊：
Journal of magnetic resonance imaging : JMRI
影响因子：
--
作者：
Chwang WB;Jain R;Bagher-Ebadian H;Nejad-Davarani SP;Iskander AS;VanSlooten A;Schultz L;Arbab AS;Ewing JR
通讯作者：
Ewing JR
影响因子：
3.3
作者：
Buckley, David L.;Kershaw, Lucy E.;Stanisz, Greg J.
通讯作者：
Stanisz, Greg J.
影响因子：
3.3
作者：
Bagher-Ebadian, Hassan;Nagaraja, Tavarekere N.;Ewing, James R.
通讯作者：
Ewing, James R.