Sun Tao, Fulton Roger, Hu Zhanli, Sutiono Christina, Liang Dong, Zheng Hairong
Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Faculty of Medicine and Health and School of Physics, University of Sydney, Sydney, Australia.
Quant Imaging Med Surg. 2022 Jan;12(1):439-456. doi: 10.21037/qims-21-338.
Computed tomography perfusion imaging is commonly used for the rapid assessment of patients presenting with symptoms of acute stroke. Maps of perfusion parameters, such as cerebral blood volume (CBV), cerebral blood flow (CBF), and mean transit time (MTT) derived from the perfusion scan data, provide crucial information for stroke diagnosis and treatment decisions. Most CT scanners use singular value decomposition (SVD)-based methods to calculate these parameters. However, some known problems are associated with conventional methods.
In this work, we propose a Bayesian inference algorithm, which can derive both the perfusion parameters and their uncertainties. We apply the variational technique to the inference, which then becomes an expectation-maximization problem. The probability distribution (with Gaussian mean and variance) of each estimated parameter can be obtained, and the coefficient of variation is used to indicate the uncertainty. We perform evaluations using both simulations and patient studies.
In a simulation, we show that the proposed method has much less bias than conventional methods. Then, in separate simulations, we apply the proposed method to evaluate the impacts of various scan conditions, i.e., with different frame intervals, truncated measurement, or motion, on the parameter estimate. In one patient study, the method produced CBF and MTT maps indicating an ischemic lesion consistent with the radiologist's report. In a second patient study affected by patient movement, we showed the feasibility of applying the proposed method to motion corrected data.
The proposed method can be used to evaluate confidence in parameter estimation and the scan protocol design. More clinical evaluation is required to fully test the proposed method.
计算机断层扫描灌注成像常用于对出现急性中风症状的患者进行快速评估。从灌注扫描数据得出的灌注参数图，如脑血容量（CBV）、脑血流量（CBF）和平均通过时间（MTT），为中风诊断和治疗决策提供了关键信息。大多数CT扫描仪使用基于奇异值分解（SVD）的方法来计算这些参数。然而，传统方法存在一些已知问题。
在这项工作中，我们提出了一种贝叶斯推理算法，该算法可以得出灌注参数及其不确定性。我们将变分技术应用于推理，使其成为一个期望最大化问题。可以获得每个估计参数的概率分布（具有高斯均值和方差），并使用变异系数来表示不确定性。我们使用模拟和患者研究进行评估。
在一次模拟中，我们表明所提出的方法比传统方法的偏差小得多。然后，在单独的模拟中，我们应用所提出的方法来评估各种扫描条件，即不同的帧间隔、截断测量或运动，对参数估计的影响。在一项患者研究中，该方法生成的CBF和MTT图显示出与放射科医生报告一致的缺血性病变。在第二项受患者运动影响的患者研究中，我们展示了将所提出的方法应用于运动校正数据的可行性。
所提出的方法可用于评估参数估计的可信度和扫描方案设计。需要更多的临床评估来全面测试所提出的方法。
