直接从C臂CT原始数据重建脑灌注图像，提升卒中救治效率。
Reconstructing Quantitative Cerebral Perfusion Images Directly From Measured Sinogram Data Acquired Using C-arm Cone-Beam CT
- 将图像重建与灌注参数估计融合为联合优化，直接从原始sinogram数据输出结果
- 在低时间分辨率下仍能准确还原脑动脉和组织衰减变化，精度优于传统两步法
- 适合介入手术室快速评估急性缺血性卒中患者，无需额外设备
为缩短急性缺血性卒中患者的“门到穿刺”时间，亟需在介入导管室的C臂锥束计算机断层扫描（CBCT）设备上实现定量脑灌注成像。然而，受制于旋转速度慢，典型C臂CBCT的时间分辨率和时间采样密度远低于诊断用多排螺旋CT。当前定量灌注成像采用时间分辨图像重建与灌注参数估计两个串联步骤：前者因时间分辨率低、采样稀疏导致脑动脉及组织衰减值动态变化量化不准；后者则面临手工设计正则化项以求解卷积反演问题的挑战。这两方面限制使得现有方法难以在C臂CBCT上获得定量准确的灌注图像。本文提出一种新方法——直接脑灌注参数图像重建（TRAINER），将上述两个步骤合并为单一联合优化问题，直接从测量的sinogram数据重建定量灌注图像。该方法将定量灌注图像建模为受时间分辨CT前向模型、灌注卷积模型及个体实测sinogram数据约束的特定受试者条件生成模型。实验结果表明，使用TRAINER可在介入导管室的C臂CBCT上准确获取定量脑灌注图像。
原文摘要 · Abstract (English)
To shorten the door-to-puncture time for better treating patients with acute ischemic stroke, it is highly desired to obtain quantitative cerebral perfusion images using C-arm cone-beam computed tomography (CBCT) equipped in the interventional suite. However, limited by the slow gantry rotation speed, the temporal resolution and temporal sampling density of typical C-arm CBCT are much poorer than those of multi-detector-row CT in the diagnostic imaging suite. The current quantitative perfusion imaging includes two cascaded steps: time-resolved image reconstruction and perfusion parametric estimation. For time-resolved image reconstruction, the technical challenge imposed by poor temporal resolution and poor sampling density causes inaccurate quantification of the temporal variation of cerebral artery and tissue attenuation values. For perfusion parametric estimation, it remains a technical challenge to appropriately design the handcrafted regularization for better solving the associated deconvolution problem. These two challenges together prevent obtaining quantitatively accurate perfusion images using C-arm CBCT. The purpose of this work is to simultaneously address these two challenges by combining the two cascaded steps into a single joint optimization problem and reconstructing quantitative perfusion images directly from the measured sinogram data. In the developed direct cerebral perfusion parametric image reconstruction technique, TRAINER in short, the quantitative perfusion images have been represented as a subject-specific conditional generative model trained under the constraint of the time-resolved CT forward model, perfusion convolutional model, and the subject's own measured sinogram data. Results shown in this paper demonstrated that using TRAINER, quantitative cerebral perfusion images can be accurately obtained using C-arm CBCT in the interventional suite.
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