Li Mengzhou, Fang Zheng, Cong Wenxiang, Niu Chuang, Wu Weiwen, Uher Josef, Bennett James, Rubinstein Jay T, Wang G E
Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA.
Department of Instrumental and Electrical Engineering, Xiamen University, Xiamen 361102, China.
IEEE Access. 2020;8:229018-229032. doi: 10.1109/access.2020.3046187. Epub 2020 Dec 21.
While micro-CT systems are instrumental in preclinical research, clinical micro-CT imaging has long been desired with cochlear implantation as a primary application. The structural details of the cochlear implant and the temporal bone require a significantly higher image resolution than that (about 0.2 ) 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 the technologies of a micro-focus X-ray source, a 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 micro-CT scanner for a local scan of high spatial and spectral resolution at minimized radiation dose. The prior information from the global scan is also fully utilized for background compensation of the local scan data for accurate and stable VOI reconstruction. Our results and analysis show that the proposed hybrid reconstruction algorithm delivers accurate high-resolution local reconstruction, and is insensitive to the misalignment of the isocenter position, initial view angle and scale mismatch in the data/image registration. 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 promise in temporal bone imaging as well as various other clinical applications.
虽然微型计算机断层扫描（micro-CT）系统在临床前研究中发挥着重要作用，但长期以来人们一直期望将其应用于临床，特别是以人工耳蜗植入为主要应用场景。人工耳蜗和颞骨的结构细节需要比当前医学CT扫描仪提供的分辨率（约0.2 ）高得多的图像分辨率。在本文中，我们提出了一种临床微型计算机断层扫描（CMCT）系统设计，该设计集成了传统的螺旋锥束CT、当代内部断层扫描、深度学习技术，以及微焦点X射线源、光子计数探测器（PCD）和机器人手臂技术，用于在最小化辐射剂量水平下对自由选择的感兴趣体积（VOI）进行超高分辨率局部断层扫描。整个系统由一台用于临床CT检查和VOI指定的标准CT扫描仪，以及一台用于在最小化辐射剂量下进行高空间和光谱分辨率局部扫描的机器人微型CT扫描仪组成。全局扫描的先验信息也被充分用于局部扫描数据的背景补偿，以实现准确、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能够实现准确的高分辨率局部重建，并且对数据/图像配准中的等中心位置、初始视角和尺度不匹配不敏感。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。我们提出的CMCT系统具有高分辨率成像、高剂量效率和低系统成本的协同优势，在颞骨成像以及各种其他临床应用中具有很大的前景。
