乳腺癌定量动态对比增强磁共振成像中参考区域建模技术的评估
Evaluation of reference region modelling techniques in quantitative dynamic contrast-enhanced magnetic resonance imaging of breast cancer
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中文总结 AI 辅助
该研究评估参考区域模型技术在乳腺癌低时间分辨率DCE-MRI定量灌注分析中的有效性，将多种模型应用于患者图像数据集，结果显示RRMs与AIF驱动模型性能相似且对AIF依赖性低，或可替代提供可靠定量分析。
中文摘要 AI 辅助
目的：评估参考区域模型（RRM）技术在乳腺病变低时间分辨率动态对比增强磁共振成像（DCE-MRI）定量灌注分析中的有效性。方法：将Tofts模型（TM）、扩展Tofts模型（ETM）、RRM和扩展RRM（ERRM）应用于10例确诊乳腺癌患者在一个化疗周期前后采集的图像数据集，已知病理治疗反应。使用每个模型生成估计灌注参数的定量图，并比较各模型在一个化疗周期前后中位灌注参数的变化。使用参考区域和输入函数尾部（RRIFT）技术将参考区域模型的相对参数转换为绝对参数。还记录了治疗前后肿瘤区域的体素计数。结果：与AIF驱动模型（TM和ETM）相比，RRMs产生了相似的绝对定量分析，ETM和ERRM在Ktrans参数估计上存在比例差异，在vp参数估计上存在分歧。RRMs和AIF驱动模型同样能够根据中位Ktrans参数的变化区分完全缓解者，而肿瘤体素计数的变化不能预测治疗反应。两个RRMs也能够使用相对Ktrans参数估计完全区分完全缓解者，无需AIF信息。结论：RRMs在乳腺病变的定量分析中表现出与标准模型相似的性能，对AIF的依赖性降低。结果表明，RRMs可能是低时间分辨率下提供可靠的、患者特异性定量分析的合适替代方法。
英文摘要
Purpose: To assess the validity of reference region model (RRM) techniques for quantitative perfusion analysis of low temporal resolution DCE-MRI of breast lesions. Methods: Tofts model (TM), extended Tofts model (ETM), RRM, and extended RRM (ERRM) were applied to a dataset of images from ten patients with confirmed breast cancer collected before and after one cycle of chemotherapy, with known pathologic treatment response. Quantitative maps of estimated perfusion parameters were produced using each model, and changes in median perfusion parameters before and after a cycle of chemotherapy were compared across models. Relative parameters from reference region models were made absolute using the reference region and input function tail (RRIFT) technique. Tumour region voxel count before and after treatment was also recorded. Results: RRMs produced similar absolute quantitative analysis compared to AIF-driven models (TM and ETM), with scaling dicerences in Ktrans parameter estimates and disagreement in vp parameter estimates between ETM and ERRM. RRMs and AIF-driven models were equally capable of separating complete responders based on change in median Ktrans parameter, while change in tumour voxel count was not predictive of treatment response. Both RRMs were also able to fully separate complete responders using relative Ktrans parameter estimates, with no AIF information. Conclusions: RRMs show similar performance as standard models in quantitative analysis of breast lesions, with reduced reliance on the AIF . Results imply that RRMs may be a suitable alternative at low temporal resolutions to provide reliable, patient-specific quantitative analysis.
