Motion estimation and compensation in dynamic spiral CT reconstruction

Respiratory and cardiac motion causes blurring in dynamic X-ray Computed Tomography (CT). Fast scans reduce this problem, but they require a higher radiation dose per time period to maintain the signal to noise ratio of the resulting images, thereby magnifying the health risk to the patient. As an alternative to increased radiation, our team has already developed a cone-beam reconstruction algorithm based on a dynamic particle model that estimates, predicts, and compensates for respiratory motion in circular X-ray CT. The current paper presents an extension of this method to spiral CT, applicable to modern multi-slice scanners that take advantage of the speed and dose benefits of helical trajectories. We adapted all three main areas of the algorithm: backprojection, prediction, and compensation/accumulation. In backprojection, we changed the longitudinal rebinning technique, filter direction, and the method of enforcing the data sufficiency requirements. For prediction, we had to be careful of objects appearing and disappearing as the scanner bed advanced. For compensation/accumulation, we controlled the reconstruction time and combined images to cover a greater longitudinal extent for each phase in the respiratory or cardiac cycle. Tests with moving numerical phantoms demonstrate that the algorithm successfully improves the temporal resolution of the images while keeping the dose and signal-to-noise benefits of a longer scan.