Automated least-squares calibration of the coregistration parameters for a micro PET-CT system

A previously developed method derives co-registration parameters from PET and CT images of a four-point-source calibration phantom by manually adjusting the offsets and orientation of the CT image to achieve alignment with the PET image in a graphic viewer. This manual process is tedious and can be inaccurate, especially when rotational offsets exist. An automated segmentation method has been developed, based on thresholding and application of constraints on the sizes of point sources in the images. After point sources are identified on PET and CT images, co-registration is performed using an analytic rigid-body registration algorithm which is based on singular value decomposition and minimization of the co-registration error. The co-registration parameters thus derived can then be applied to co-register other PET and CT images from the same system. Twenty PET-CT images of the calibration phantom at various locations and/or orientations were obtained on a Siemens Inveon^® Multi-Modality scanner. We tested the use of from 1 to 10 data sets to derive the co-registration parameters, and found that the co-registration accuracy improves with increasing number of data sets until it stabilizes. Co-registration of PET-CT images with an accuracy of 0.33±0.11 mm has been achieved by this method on the Inveon Multi-Modality scanner.