Consistency Conditions for Artifact Reduction in Cone-beam CT

Consistency Conditions for Artifact Reduction in Cone-beam CT

(Third Party Funds Single)

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Start date: January 1, 2015
End date:
Funding source: DFG-Einzelförderung / Sachbeihilfe (EIN-SBH)


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.