Evaluation of a motion correction algorithm in lung cancer PET/CT: Phantom validation and patient studies

Data-driven gating (DDG) is an emerging technology that can reduce the respiratory motion artifacts in positron emission tomography (PET) images.

The aim of this study is to use phantom and patient data to validate the performance of DDG with a motion correction algorithm based on the reconstruct, register, and average (RRA) method.

A customized motion platform drove the phantom (five spheres with diameters of 10-28 mm) using a periodic motion that had a duration of 3-5 s and amplitudes of 2-4 cm. Normalized ratio of ungated and RRA PET relative to the ground-truth static PET was calculated for RSUVmax, RSUVmean, RSUVpeak, RVolume, and relative contrast-to-noise ratio (RCNR). Additionally, 30 lung cancer patients with 76 lung lesions less than 3 cm in diameter were prospectively studied. The overall image quality of patient examination was scored using a 5-point scale by two radiologists. SUVmax, SUVmean, SUVpeak, volume, and CNR of lesions measured in ungated and RRA PET were compared, and subgroup analysis was conducted.

In RRA PET images, motion artifacts of the spheres in the phantom were effectively mitigated, regardless of changes in movement amplitudes or duration. For all spheres with different ranges of motion and cycles, RSUVmax, RSUVmean, RSUVpeak, and RCNR increased significantly (p ≤ 0.001) and RVolume decreased significantly (p < 0.001) in RRA PET images. The average radiologist scores of image quality were 3.90 ± 0.86 with RRA PET, and 3.03 ± 1.19 with ungated PET. In RRA PET images, the SUVmax (p < 0.001), SUVmean (p < 0.001), SUVpeak (p < 0.001), and CNR (p < 0.001) of the lesions increased, while the volume (p < 0.001) of the lesions decreased. Δ%SUVmax, Δ%SUVmean, Δ%SUVpeak, and Δ%CNR of the lesions increased by 3.9%, 6.5%, 5.6%, and 4.3%, respectively, while Δ%Volume of the lesions decreased by 18.4%. Subgroup analysis showed that in lesions in the upper and middle lobes, only SUVpeak (p < 0.001) significantly increased by 5.6% in RRA PET, while their volume (p < 0.001) notably decreased by 12.4% (p < 0.001).

DDG integrated with RRA motion correction algorithm can effectively mitigate motion artifacts, thus enhancing the quantification accuracy and visual quality of images in lung cancer PET/CT.