Consistency Conditions for Artifact Reduction in Cone-beam CT

层析成像重建是一种广泛的基于传输的3D成像技术，最著名的是x射线计算机断层扫描（CT）。上世纪70年代制造的第一台CT扫描仪使用的是平行几何结构。为了加快采集速度，系统很快转向了扇形波束几何形状和更快的旋转速度。目前的连续油管系统每秒旋转4次，采用锥束几何结构。这个速度足以覆盖复杂的器官运动，比如跳动的心脏。然而，目前有大量的专用CT系统无法进行如此快速的扫描。使用平板探测器的系统，如用于c臂血管造影系统、放射治疗中的车载成像系统或移动c臂系统，面临机械方面的挑战，因为它们主要用于执行2D成像。大约15年前，平面探测器扫描仪已经能够获取三维数据。然而，在这些系统上进行3D成像具有挑战性，因为它们的采集速度较慢，在5秒到1分钟之间，并且视场（FOV）直径较小，为25到40厘米。这些缺点与扫描仪设计为高度专业化的模式有关。与平板探测器的其他缺点（如x射线散射增加和动态范围有限）相比，它们在可预见的未来将无法通过硬件进化来弥补，例如，由于手术室中的碰撞风险，更快的运动是不可能的。因此，由于运动和截断，平面检测器计算机断层扫描（FDCT）将继续更容易受到重建图像中的伪影的影响。该项目的目标是扩展现有的数据一致性条件，这些条件可以实际用于FDCT，以弥补FDCT成像的固有弱点，最重要的是运动和截断。我们的目标是临床数据的实用性。因此，新算法将在我们项目合作伙伴的真实FDCT扫描仪上获得的物理幻影和患者数据上进行测试。我们在这个项目背后的长期愿景是为一般轨迹的FDCT投影数据中的所有冗余找到一个简洁而完整的公式，并在重建过程中充分利用它们。在今天的临床实践中，每一次FDCT扫描都存在冗余，但它们作为一种信息来源却被完全忽视了。数据一致性条件在获取过程中不需要任何额外的努力，只需要很少的先验知识，例如对象范围，甚至不需要对底层对象进行假设，这与迭代重建中的总变分正则化不同。因此，它们完全依赖于数据中自然存在的信息。

Tomographic reconstruction is the enabling technology for a wide range of transmission-based 3D imaging modalities, most notably X-ray Computed Tomography (CT). The first CT scanners built in the 70ies used parallel geometries. In order to speed up acquisition, the systems soon moved to fan-beam geometries and a much faster rotation speed. Today´s CT systems rotate four times per second and use a cone-beam geometry. This is fast enough to cover even complex organ motion such as the beating heart. However, there exists a large class of specialized CT systems that are not able to perform such fast scans. Systems using a flat-panel detector, as they are employed in C-arm angiography systems, on-board imaging systems in radiation therapy, or mobile C-arm systems, face mechanical challenges as they were mainly built to perform 2D imaging. About 15 years ago flat-detector scanners have been enabled to acquire three dimensional data. 3D imaging on these systems, however, is challenging due to their slower acquisition speed between five seconds and one minute and a small field-of-view (FOV) with a diameter of 25 to 40 cm. These drawbacks are related to the scanners´ design as highly specialized modalities. In contrast to other disadvantages of flat panel detectors like increased X-ray scattering and limited dynamic range, they will not be remedied by hardware evolution in the foreseeable future, e.g. faster motion is impossible because of the risk of collisions in the operation room. As a result, Flat-Detector Computed Tomography (FDCT) will continue to be more susceptible to artifacts in the reconstructed image due to motion and truncation. The goal of this project is to extend existing data consistency conditions, which can be practically used for FDCT to remedy intrinsic weaknesses of FDCT imaging, most importantly motion and truncation. Our goal is the practical applicability on clinical data. Thus, the new algorithms will be tested on physical phantom and patient data acquired on real FDCT scanners of our project partners. Our long-term vision behind this project is to find a concise and complete formulation for all redundancies within FDCT projection data for general trajectories and fully exploit them in the reconstruction process. Redundancies are inherent to every FDCT scan done in today´s clinical practice, but they are ignored entirely as a source of information. Data consistency conditions do not require any additional effort during the acquisitions and only little prior knowledge such as the object extent or even no assumptions about the underlying object, unlike for example total variation regularization in iterative reconstruction. Hence, they rely solely on information which is naturally present in the data.