Zhou Liangdong, Zhang Qihao, Spincemaille Pascal, Nguyen Thanh D, Morgan John, Dai Weiying, Li Yi, Gupta Ajay, Prince Martin R, Wang Yi
Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA.
Meinig School of Biomedical Engineering, Cornell University, Ithaca, New York, USA.
Magn Reson Med. 2021 Apr;85(4):2247-2262. doi: 10.1002/mrm.28584. Epub 2020 Nov 18.
Proof-of-concept study of mapping renal blood flow vector field according to the inverse solution to a mass transport model of time resolved tracer-labeled MRI data.
To determine tissue perfusion according to the underlying physics of spatiotemporal tracer concentration variation, the mass transport equation is integrated over a voxel with an approximate microvascular network for fitting time-resolved tracer imaging data. The inverse solution to the voxelized transport equation provides the blood flow vector field, which is referred to as quantitative transport mapping (QTM). A numerical microvascular network modeling the kidney with computational fluid dynamics reference was used to verify the accuracy of QTM and the current Kety's method that uses a global arterial input function. Multiple post-label delay arterial spin labeling (ASL) of the kidney on seven subjects was used to assess QTM in vivo feasibility.
Against the ground truth in the numerical model, the error in flow estimated by QTM (18.6%) was smaller than that in Kety's method (45.7%, 2.5-fold reduction). The in vivo kidney perfusion quantification by QTM (cortex: 443 ± 58 mL/100 g/min and medulla: 190 ± 90 mL/100 g/min) was in the range of that by Kety's method (482 ± 51 mL/100 g/min in the cortex and 242 ± 73 mL/100 g/min in the medulla), and QTM provided better flow homogeneity in the cortex region.
QTM flow velocity mapping is feasible from multi-delay ASL MRI data based on inverting the transport equation. In a numerical simulation, QTM with deconvolution in space and time provided more accurate perfusion quantification than Kety's method with deconvolution in time only.
根据时间分辨示踪剂标记的MRI数据的质量传输模型的逆解,对肾血流矢量场进行映射的概念验证研究。
为了根据时空示踪剂浓度变化的基础物理学来确定组织灌注,质量传输方程在具有近似微血管网络的体素上进行积分,以拟合时间分辨示踪剂成像数据。体素化传输方程的逆解提供了血流矢量场,称为定量传输映射(QTM)。使用具有计算流体动力学参考的数值微血管网络对肾脏进行建模,以验证QTM和当前使用全局动脉输入函数的凯蒂方法的准确性。对7名受试者的肾脏进行多次标记后延迟动脉自旋标记(ASL),以评估QTM在体内的可行性。
与数值模型中的真实情况相比,QTM估计的血流误差(18.6%)小于凯蒂方法(45.7%,降低了2.5倍)。通过QTM进行的体内肾脏灌注定量(皮质:443±58 mL/100 g/min,髓质:190±90 mL/100 g/min)在凯蒂方法的范围内(皮质为482±51 mL/100 g/min,髓质为242±73 mL/100 g/min),并且QTM在皮质区域提供了更好的血流均匀性。
基于传输方程的反演,从多延迟ASL MRI数据进行QTM流速映射是可行的。在数值模拟中,时空去卷积的QTM比仅时间去卷积的凯蒂方法提供了更准确的灌注定量。