X-ray cone-beam (CB) projection data often contain high amounts of scattered radiation, which must be properly modeled in order to produce accurate computed tomography (CT) reconstructions. A well known correction technique is the scatter kernel superposition (SKS) method that involves deconvolving projection data with kernels derived from pencil beam-generated scatter point-spread functions. The method has the advantages of being practical and computationally efficient but can suffer from inaccuracies. We show that the accuracy of the SKS algorithm can be significantly improved by replacing the symmetric kernels that traditionally have been used with nonstationary asymmetric kernels. We also show these kernels can be well approximated by combinations of stationary kernels thus allowing for efficient implementation of convolution via FFT. To test the new algorithm, Monte Carlo simulations and phantom experiments were performed using a table-top system with geometry and components matching those of the Varian On-Board Imager (OBI). The results show that asymmetric kernels produced substantially improved scatter estimates. For large objects with scatter-to-primary ratios up to 2.0, scatter profiles were estimated to within 10% of measured values. With all corrections applied, including beam hardening and lag, the resulting accuracies of the CBCT reconstructions were within ±25 Hounsfield Units (±2.5%).

The reconstruction algorithms including at least one iterative step can reduce the 4 CBCT specific artifacts. Nevertheless, the algorithms that use the full data set, at least for initialization, such as MKB and PICCS in the authors' implementation, are only a trade-off and may not fully achieve the optimal temporal resolution. A predictable image quality as seen in conventional reconstruction methods, i.e., without total variation minimization, is a desirable property for reconstruction algorithms. Fast, iterative approaches such as the MKB can therefore be seen as a suitable tradeoff.

Using the artifact model allows to accurately estimate and compensate for patient motion, even if the initial reconstructions are of very low image quality. Using our approach together with a cyclic registration algorithm yields a combination which shows almost no sensitivity to sparse-view artifacts and thus ensures both high spatial and high temporal resolution.

The cyclic motion compensation approach provides respiratory-correlated volumes with high image quality. The cyclic motion estimation is of such low sensitivity to sparse-view artifacts, that it is capable to determine high quality motion vector fields based on reconstructions of low sampled data.

Purpose: Kilovoltage cone-beam computed tomography ͑CBCT͒ is widely used in image-guided radiation therapy for exact patient positioning prior to the treatment. However, producing time series of volumetric images ͑4D CBCT͒ of moving anatomical structures remains challenging. The presented work introduces a novel method, combining high temporal resolution inside anatomical regions with strong motion and image quality improvement in regions with little motion. Methods: In the proposed method, the projections are divided into regions that are subject to motion and regions at rest. The latter ones will be shared among phase bins, leading thus to an overall reduction in artifacts and noise. An algorithm based on the concept of optical flow was developed to analyze motion-induced changes between projections. The technique was optimized to distinguish patient motion and motion deriving from gantry rotation. The effectiveness of the method is shown in numerical simulations and patient data. Results: The images reconstructed from the presented method yield an almost the same temporal resolution in the moving volume segments as a conventional phase-correlated reconstruction, while reducing the noise in the motionless regions down to the level of a standard reconstruction without phase correlation. The proposed simple motion segmentation scheme is yet limited to rotation speeds of less than 3°/ s. Conclusions:The method reduces the noise in the reconstruction and increases the image quality. More data are introduced for each phase-correlated reconstruction, and therefore the applied dose is used more efficiently.