Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
Institute of Clinical Radiology and Nuclear Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany.
Sci Rep. 2017 Sep 20;7(1):12036. doi: 10.1038/s41598-017-12194-w.
In this work, the two compartment exchange model and two compartment uptake model were applied to obtain quantitative perfusion parameters in rectum carcinoma and the results were compared to those obtained by the deconvolution algorithm. Eighteen patients with newly diagnosed rectal carcinoma underwent 3 T MRI of the pelvis including a T weighted dynamic contrastenhanced (DCE) protocol before treatment. Mean values for Plasma Flow (PF), Plasma Volume (PV) and Mean Transit Time (MTT) were obtained for all three approaches and visualized in parameter cards. For the two compartment models, Akaike Information Criterion (AIC) and [Formula: see text] were calculated. Perfusion parameters determined with the compartment models show results in accordance with previous studies focusing on rectal cancer DCE-CT (PF = 68 ± 44 ml/100 ml/min, PF = 55 ± 36 ml/100 ml/min) with similar fit quality (AIC:169 ± 81/179 ± 77, [Formula: see text]:10 ± 12/9 ± 10). Values for PF are overestimated whereas PV and MTT are underestimated compared to results of the deconvolution algorithm. Significant differences were found among all models for perfusion parameters as well as between the AIC and [Formula: see text] values. Quantitative perfusion parameters are dependent on the chosen tracer kinetic model. According to the obtained parameters, all approaches seem capable of providing quantitative perfusion values in DCE-MRI of rectal cancer.
在这项工作中，应用两室交换模型和两室摄取模型来获得直肠癌的定量灌注参数，并将结果与反卷积算法的结果进行比较。18 名新诊断为直肠癌的患者在治疗前接受了 3T 盆腔 MRI 检查，包括 T1 加权动态对比增强（DCE）方案。所有三种方法均获得了血浆流量（PF）、血浆容积（PV）和平均传输时间（MTT）的平均值，并在参数卡中可视化。对于两室模型，计算了赤池信息量准则（AIC）和 [Formula: see text]。用隔间模型确定的灌注参数的结果与以前关注直肠癌 DCE-CT 的研究结果一致（PF=68±44ml/100ml/min，PF=55±36ml/100ml/min），拟合质量相似（AIC：169±81/179±77，[Formula: see text]：10±12/9±10）。与反卷积算法的结果相比，PF 的值被高估，而 PV 和 MTT 的值被低估。所有模型的灌注参数以及 AIC 和 [Formula: see text] 值均存在显著差异。定量灌注参数取决于所选示踪动力学模型。根据获得的参数，所有方法似乎都能够在直肠癌的 DCE-MRI 中提供定量灌注值。
