Brain PET motion correction using 3D face-shape model: the first clinical study
Yuma Iwao, Go Akamatsu, Hideaki Tashima, M. Takahashi, Taiga Yamaya
Abstract
Abstract Objective Head motions during brain PET scan cause degradation of brain images, but head fixation or external-maker attachment become burdensome on patients. Therefore, we have developed a motion correction method that uses a 3D face-shape model generated by a range-sensing camera (Kinect) and by CT images. We have successfully corrected the PET images of a moving mannequin-head phantom containing radioactivity. Here, we conducted a volunteer study to verify the effectiveness of our method for clinical data. Methods Eight healthy men volunteers aged 22–45 years underwent a 10-min head-fixed PET scan as a standard of truth in this study, which was started 45 min after 18 F-fluorodeoxyglucose (285 ± 23 MBq) injection, and followed by a 15-min head-moving PET scan with the developed Kinect based motion-tracking system. First, selecting a motion-less period of the head-moving PET scan provided a reference PET image. Second, CT images separately obtained on the same day were registered to the reference PET image, and create a 3D face-shape model, then, to which Kinect-based 3D face-shape model matched. This matching parameter was used for spatial calibration between the Kinect and the PET system. This calibration parameter and the motion-tracking of the 3D face shape by Kinect comprised our motion correction method. The head-moving PET with motion correction was compared with the head-fixed PET images visually and by standard uptake value ratios (SUVRs) in the seven volume-of-interest regions. To confirm the spatial calibration accuracy, a test–retest experiment was performed by repeating the head-moving PET with motion correction twice where the volunteer’s pose and the sensor’s position were different. Results No difference was identified visually and statistically in SUVRs between the head-moving PET images with motion correction and the head-fixed PET images. One of the small nuclei, the inferior colliculus, was identified in the head-fixed PET images and in the head-moving PET images with motion correction, but not in those without motion correction. In the test–retest experiment, the SUVRs were well correlated (determinant coefficient, r 2 = 0.995). Conclusion Our motion correction method provided good accuracy for the volunteer data which suggested it is useable in clinical settings.