Xiao Yao, Liu Peng, Liang Yun, Stolte Skylar, Sanelli Pina, Gupta Ajay, Ivanidze Jana, Fang Ruogu
J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States.
Department of Radiology, Weill Cornell Medical College, New York, NY, United States.
Front Neurol. 2019 Jun 26;10:647. doi: 10.3389/fneur.2019.00647. eCollection 2019.
Computed Tomography Perfusion (CTP) imaging is a cost-effective and fast approach to provide diagnostic images for acute stroke treatment. Its cine scanning mode allows the visualization of anatomic brain structures and blood flow; however, it requires contrast agent injection and continuous CT scanning over an extended time. In fact, the accumulative radiation dose to patients will increase health risks such as skin irritation, hair loss, cataract formation, and even cancer. Solutions for reducing radiation exposure include reducing the tube current and/or shortening the X-ray radiation exposure time. However, images scanned at lower tube currents are usually accompanied by higher levels of noise and artifacts. On the other hand, shorter X-ray radiation exposure time with longer scanning intervals will lead to image information that is insufficient to capture the blood flow dynamics between frames. Thus, it is critical for us to seek a solution that can preserve the image quality when the tube current and the temporal frequency are both low. We propose STIR-Net in this paper, an end-to-end spatial-temporal convolutional neural network structure, which exploits multi-directional automatic feature extraction and image reconstruction schema to recover high-quality CT slices effectively. With the inputs of low-dose and low-resolution patches at different cross-sections of the spatio-temporal data, STIR-Net blends the features from both spatial and temporal domains to reconstruct high-quality CT volumes. In this study, we finalize extensive experiments to appraise the image restoration performance at different levels of tube current and spatial and temporal resolution scales.The results demonstrate the capability of our STIR-Net to restore high-quality scans at as low as 11% of absorbed radiation dose of the current imaging protocol, yielding an average of 10% improvement for perfusion maps compared to the patch-based log likelihood method.
计算机断层扫描灌注（CTP）成像是一种经济高效且快速的方法，可为急性中风治疗提供诊断图像。其电影扫描模式能够可视化解剖学脑结构和血流；然而，它需要注射造影剂并在较长时间内进行连续CT扫描。实际上，患者累积的辐射剂量会增加健康风险，如皮肤刺激、脱发、白内障形成，甚至癌症。减少辐射暴露的解决方案包括降低管电流和/或缩短X射线辐射暴露时间。然而，在较低管电流下扫描的图像通常伴随着更高水平的噪声和伪影。另一方面，较短的X射线辐射暴露时间和较长的扫描间隔会导致图像信息不足以捕捉帧间血流动力学。因此，对我们来说，寻求一种在管电流和时间频率都较低时能够保持图像质量的解决方案至关重要。我们在本文中提出了STIR-Net，一种端到端的时空卷积神经网络结构，它利用多方向自动特征提取和图像重建模式有效地恢复高质量的CT切片。通过时空数据不同横截面的低剂量和低分辨率补丁作为输入，STIR-Net融合空间和时间域的特征来重建高质量的CT体积。在本研究中，我们完成了广泛的实验，以评估在不同管电流水平以及空间和时间分辨率尺度下的图像恢复性能。结果表明，我们的STIR-Net能够在低至当前成像协议吸收辐射剂量11%的情况下恢复高质量扫描，与基于补丁的对数似然方法相比，灌注图平均提高了10%。
