PURPOSE The purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test. METHODS DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV). RESULTS The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of Ktrans, when considering regions with a CV < 20%, ≈ 25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable. CONCLUSIONS The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.

中文翻译：

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在原发性脑肿瘤中动态对比增强磁共振成像数据的可靠性：Tofts和快门速度模型的比较。

目的本研究的目的是研究DCE-MRI脑肿瘤数据的药代动力学建模的鲁棒性，并通过模型选择过程和稳定性测试确定可靠的灌注参数。方法使用Tofts模型（TM），扩展Tofts模型（ETM），快门速度模型（SSM）和扩展快门速度模型（ESSM）分析14例原发性脑肿瘤患者的DCE-MRI数据。实施了无效应模型（NEM）来评估其他模型对数据的过度拟合。对于每个病变，使用Akaike信息标准（AIC）来构建3D模型选择图。从该图提取的每个药代动力学参数的变异性通过噪声传播程序进行评估，从而得出变异系数（CV）的体素分布。结果所有患者的模型选择图显示，NEM在35.5％的体素中最合适，其次是ETM（32％），TM（28.2％），SSM（4.3％）和ESSM（<0.1％）。在分析Ktrans的可靠性时，考虑CV <20％的区域时，发现所有患者中≈25％的体素是稳定的。其余75％的体素被认为不可靠。结论大多数量化脑肿瘤DCE-MRI数据的研究仅考虑单一模型和整个肿瘤统计数据作为输出参数。考虑组织生物学及其对血脑屏障通透性和交换条件的影响，进行适当的模型选择，以及对所计算参数的可靠性和稳定性进行分析，对于处理可靠的脑肿瘤DCE-MRI数据至关重要。