Multi-bed stitching tool for 3D computed tomography accelerated by GPU devices

In computed tomography (CT) systems it is common to acquire several consecutive datasets for different bed positions, which are subsequently combined to enlarge the field of view in the longitudinal direction. For this combination, the geometric calibration of the bed motion is necessary to avoid double edges in the overlaped area. This calibration is performed periodically using calibration phantom with markers to guide parameter esti-mation. This work presents a novel correlation-based automatic bed stitching tool for CT that avoids the need of the calibration step. Our approach exploits the massive parallelism offered by GPUs and features an optimized memory model that allows large volumes to be stitched in near-real time. Evaluation in rodent studies demonstrates not only that the offered implementation is able to paste tomographic studies in reduced time, but also that it reduces the memory footprint.