论文标题
内部断层扫描,机器人扫描和深度学习授权的临床微观CT
Clinical Micro-CT Empowered by Interior Tomography, Robotic Scanning, and Deep Learning
论文作者
论文摘要
尽管Micro-CT系统在临床前研究中具有重要作用,但长期以来一直以人工耳蜗植入为主要示例,一直需要临床Micro-CT成像。人工耳蜗的结构细节和颞骨所需的图像分辨率明显高于当前医疗CT扫描仪提供的(约0.2 mm)。在本文中,我们提出了一种临床微CT(CMCT)系统设计,该系统整合了传统的螺旋锥束CT,当代室内断层扫描,深度学习技术和微焦点X射线源,光子计数探测器(PCD)和超级分辨率分辨率的局部层次范围(voi)的超级分辨率局部(VOI)的超级分辨率局部(VOI)的局部局部局部(voi)。整个系统由用于临床CT检查和VOI规范的标准CT扫描仪,以及一个基于机器人臂的Micro-CT扫描仪,用于在更高的空间和光谱分辨率下进行局部扫描以及大量降低的辐射剂量。全球扫描的先前信息也被充分利用用于背景补偿,以改善本地数据的内部层析成像,以进行准确稳定的VOI重建。我们的结果和分析表明,所提出的混合重建算法提供了较高的局部重建,对数据/图像注册中的同性恋位置的未对准和初始视角不敏感,而根据比例配置引起的衰减误差可以有效地解决偏见校正。这些发现证明了我们系统设计的可行性。我们设想可以利用深度学习技术来优化成像性能。通过高分辨率成像,高剂量效率和低系统成本协同成本,我们提出的CMCT系统在颞骨成像以及其他各种临床应用中具有巨大的潜力。
While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary example. The structural details of the cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 mm) provided by current medical CT scanners. In this paper, we propose a clinical micro-CT (CMCT) system design integrating conventional spiral cone-beam CT, contemporary interior tomography, deep learning techniques, and technologies of micro-focus X-ray source, photon-counting detector (PCD), and robotic arms for ultrahigh resolution localized tomography of a freely-selected volume of interest (VOI) at a minimized radiation dose level. The whole system consists of a standard CT scanner for a clinical CT exam and VOI specification, and a robotic-arm based micro-CT scanner for a local scan at much higher spatial and spectral resolution as well as much reduced radiation dose. The prior information from global scan is also fully utilized for background compensation to improve interior tomography from local data for accurate and stable VOI reconstruction. Our results and analysis show that the proposed hybrid reconstruction algorithm delivers superior local reconstruction, being insensitive to the misalignment of the isocenter position and initial view angle in the data/image registration while the attenuation error caused by scale mismatch can be effectively addressed with bias correction. These findings demonstrate the feasibility of our system design. We envision that deep learning techniques can be leveraged for optimized imaging performance. With high resolution imaging, high dose efficiency and low system cost synergistically, our proposed CMCT system has great potentials in temporal bone imaging as well as various other clinical applications.