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.

翻译：为缩短门到穿刺时间以更好地治疗急性缺血性卒中患者，利用介入手术室配备的C型臂锥束计算机断层扫描（CBCT）获取定量脑灌注图像具有迫切需求。然而，受限于机架旋转速度较慢，典型C型臂CBCT的时间分辨率与时间采样密度远低于诊断影像室的多排探测器CT。当前定量灌注成像包含两个级联步骤：时间分辨图像重建与灌注参数估计。在时间分辨图像重建中，时间分辨率与采样密度不足导致脑血管及组织衰减值随时间变化的量化不准确。在灌注参数估计方面，如何恰当设计人工正则化以更好地求解相关反卷积问题仍是技术挑战。这两大挑战共同阻碍了利用C型臂CBCT获取定量准确的灌注图像。本研究旨在通过将两个级联步骤整合为单一联合优化问题，直接从测量正弦图数据重建定量灌注图像，以同步解决这两项挑战。在所开发的直接脑灌注参数图像重建技术（简称TRAINER）中，定量灌注图像被表示为受时间分辨CT前向模型、灌注卷积模型及受试者自身测量正弦图数据约束训练的主体特异性条件生成模型。本文展示的结果表明，利用TRAINER技术可在介入手术室中通过C型臂CBCT准确获取定量脑灌注图像。