A multimodal approach to image-derived input functions for brain PET

Many methods have been proposed for generating an image-derived input function (IDIF) exclusively from PET images. The purpose of this study was to assess the viability of a multimodality approach utilizing registered MR images. 3T-MR and HRRT-PET data were acquired from human subjects. Segmentation of both the left and right carotid arteries was performed in MR images using a 3D level sets method. Vessel centerlines were extracted by parameterization of the segmented voxel coordinates with either a single polynomial curve or a B-spline curve fitted to the segmented data. These centerlines were subsequently re-registered to static PET data to maximize the accurate classification of PET voxels in the ROI. The accuracy of this approach was assessed by comparison of the area under the curve (AUC) of the IDIF to that measured from conventional automated arterial blood sampling. Our method produces curves similar in shape to that of blood sampling. The mean AUC ratio of the centerline region was 0.40 ± 0.19 before re-registration and 0.69 ± 0.26 after re-registration. Increasing the diameter of the carotid ROI produced a smooth reduction in AUC. Thus, even with the high resolution of the HRRT, partial volume correction is still necessary. This study suggests that the combination of PET information with MR segmented regions will demonstrate an improvement over regions based solely on MR or PET alone.