Coronary computed tomography angiography (CCTA) does not allow the quantification of reduced blood flow due to coronary artery disease (CAD). In response, numerical methods based on the CCTA image have been developed to compute coronary blood flow and assess the impact of disease. However to compute blood flow in the coronary arteries, numerical methods require specification of boundary conditions that are difficult to estimate accurately in a patient-specific manner. We describe herein a new noninvasive flow estimation method, called Advection Diffusion Flow Estimation (ADFE), to compute coronary artery flow from CCTA to use as boundary conditions for numerical models of coronary blood flow. ADFE uses image contrast variation along the tree-like structure to estimate flow in each vessel. For validating this method we used patient specific software phantoms on which the transport of contrast was simulated. This controlled validation setting enables a direct comparison between estimated flow and actual flow and a detailed investigation of factors affecting accuracy. A total of 10 CCTA image data sets were processed to extract all necessary information for simulating contrast transport. A spectral element method solver was used for computing the ground truth simulations with high accuracy. On this data set, the ADFE method showed a high correlation coefficient of $0.998$ between estimated flow and the ground truth flow together with an average relative error of only $1\%$. Comparing the ADFE method with the best method currently available (TAFE) for image-based blood flow estimation, which showed a correlation coefficient of $0.752$ and average error of $20\%$, it can be concluded that the ADFE method has the potential to significantly improve the quantification of coronary artery blood flow derived from contrast gradients in CCTA images.

中文翻译：

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使用计算患者特定冠状动脉树模型对平流扩散流量估计方法进行临床前验证

冠状动脉计算机断层扫描血管造影 (CCTA) 无法量化冠状动脉疾病 (CAD) 导致的血流量减少。为此，人们开发了基于 CCTA 图像的数值方法来计算冠状动脉血流量并评估疾病的影响。然而，为了计算冠状动脉中的血流量，数值方法需要指定边界条件，而这些边界条件很难以特定于患者的方式准确估计。我们在此描述了一种新的无创流量估计方法，称为平流扩散流量估计（ADFE），用于计算来自 CCTA 的冠状动脉流量，以用作冠状动脉血流数值模型的边界条件。ADFE 使用沿树状结构的图像对比度变化来估计每个容器中的流量。为了验证该方法，我们使用了患者特定的软件模型，在该模型上模拟了造影剂的传输。这种受控验证设置可以直接比较估计流量和实际流量，并对影响准确性的因素进行详细调查。总共处理了 10 个 CCTA 图像数据集，以提取模拟对比度传输所需的所有信息。谱元法求解器用于高精度计算地面实况模拟。在此数据集上，ADFE 方法显示出较高的相关系数$0.998$估计流量和地面真实流量之间的平均相对误差仅为$1\%$。将 ADFE 方法与目前基于图像的血流估计的最佳方法 (TAFE) 进行比较，显示相关系数为$0.752$和平均误差$20\%$，可以得出结论，ADFE 方法有可能显着改善从 CCTA 图像中的对比度梯度得出的冠状动脉血流的量化。