Quantitative validation of data-driven motion correction for brain PET using phantom with motion generator system

Head motion during brain positron emission tomography (PET) degrades image quality and quantitative accuracy. Therefore, a data-driven motion correction (MC) method utilizing ultrafast list-mode reconstruction technology has been proposed and shown to considerably improve image quality. However, reproducing accurate actual motions and motion-free images using clinical data alone remains challenging. This study aimed to quantitatively evaluate data-driven MC using a brain phantom for known tracer distributions and a custom-made motion generator system for variable known motions.

Hoffman 3D brain phantom was filled with 20 and 3 MBq of [¹⁸F]fluoro-2-deoxy-D-glucose (FDG) to simulate high- and low-radioactivity conditions corresponding to brain FDG PET and amyloid PET acquisitions, respectively. Two separate phantom measurements were performed accordingly. Motion simulation was conducted using a custom-designed motion generator, incorporating 15° and 30° rotations about the z-axis, 3° and 6° rotations about the x-axis, and 5 mm and 10 mm translations along the z-axis in the PET image coordinates. The data-driven MC was applied with frame durations of 1, 2, 5, 10, and 20 s for motion estimation. The estimated motions were compared with the motions measured using an external optical tracker system. %contrast and gray matter coefficient of variation (CV%) were calculated from the motion-corrected PET images.

The motion generator system successfully reproduced the designed motions. Motion estimation remained stable under high-radioactivity condition but showed reduced stability under low-radioactivity condition, particularly with shorter frame durations. Under both conditions, longer frame durations led to underestimation of continuous motion. The data-driven MC improved %contrast and gray matter CV% across all conditions, with shorter frame durations providing better correction for quick or continuous motions. However, shorter frame durations increased statistical noise, especially under low-radioactivity condition.

The data-driven MC effectively improved the quality of motion-affected PET images under both high- and low-radioactivity conditions, indicating its broad applicability. However, correction accuracy deteriorated under the lower-radioactivity condition.