Consistency Conditions for Artifact Reduction in Cone-beam CT

断层重建是一种广泛的基于传输的3D成像方式的使能技术，最著名的是X射线计算机断层扫描(CT)。70年代建造的第一台CT扫描仪使用的是平行几何结构。为了加快采集速度，系统很快转向扇束几何结构和更快的旋转速度。今天的S CT系统每秒旋转四次，使用锥束几何结构。这一速度足以覆盖复杂的器官运动，如心脏跳动。然而，有一大类专门的CT系统不能执行如此快速的扫描。使用平板探测器的系统，因为它们被用于C-臂血管成像系统、放射治疗的车载成像系统或移动C-臂系统，面临着机械挑战，因为它们主要是为了执行2D成像而构建的。大约15年前，平板探测器扫描仪已经能够获取三维数据。然而，这些系统上的3D成像是具有挑战性的，因为它们的采集速度在5秒到1分钟之间较慢，并且视野(FOV)很小，直径在25到40厘米之间。这些缺陷与扫描仪的高度专门化设计有关。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)形成对比的是，在可预见的未来，它们不会通过硬件发展来弥补，例如，由于手术室中发生碰撞的风险，不可能实现更快的运动。因此，平板探测器计算机断层扫描(FDCT)将继续更容易受到运动和截断导致的重建图像中的伪影的影响。这个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。我们的目标是在临床数据上的实际适用性。因此，新算法将在物理模型和我们项目合作伙伴的真实FDCT扫描仪上获取的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FDCT投影数据中的所有冗余找到一个简洁和完整的公式，并在重建过程中充分利用它们。冗余是当今S临床实践中每一次FDCT扫描所固有的，但作为一种信息来源，它们完全被忽视了。数据一致性条件在采集期间不需要任何额外的工作，并且只需要很少的先验知识，例如对象范围，或者甚至不需要关于底层对象的假设，这与例如迭代重建中的总变化正则化不同。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.