We propose an algorithm for periodic motion estimation and compensation in the case of a slowly rotating gantry, e.g., as is the case in cone beam CT. The main target application is abdomen imaging, which is quite challenging because of the absence of high-contrast features. The algorithm is based on minimizing a cost functional, which consists of the data fidelity term, the optical flow constraint term, and regularization terms. To find the appropriate solution we change the constraint strength and regularization strength parameters during the minimization. Results of experiments with simulated and clinical data demonstrate promising performance.
We propose an FBP reconstruction algorithm for a stationary gantry CT scanner with distributed sources. The sources are fired in quasi-random order to improve data completeness across the field of view (FOV). The downsides of that are two-fold. The neighboring sources are fired non-sequentially, so the view derivative should be avoided. Second, the angular distribution of rays through each voxel is non-uniform and varies across the FOV. To overcome these challenges we incorporate a weight function into an FDK-type reconstruction algorithm, and integrate by parts to avoid view differentiation. Results of experiments with simulated data confirm that a properly selected weight significantly reduces irregular view sampling streaks.
We present details of a real-time implementation of a new algorithm designed to reduce streak artifacts in switched-source stationary gantry CT reconstruction. The algorithm is of the filtered back-projection type, and uses a voxel-specific weighting function to account for the non-uniform distribution of illumination angles caused by such a scanning geometry. The main challenge in developing a real-time implementation is the storage and memory bandwidth requirements imposed by the weighting function. This has been addressed by storing weights at a low precision and reduced resolution, and using interpolation to recover weights at the full resolution. Results demonstrate real-time performance of the algorithm at a realistic problem size, running on a low-cost consumer grade laptop.