肝臓腫瘍に対する呼吸同期PET/CTの非線形レジストレーション処理の有用性

抄録

Objective: The present study aimed to determine the qualitative and quantitative accuracy of the Q.Freeze algorithm in PET/CT images of liver tumors. Methods: A body phantom and hot spheres representing liver tumors contained 5.3 and 21.2 kBq/mL of a solution containing ¹⁸F radioactivity, respectively. The phantoms were moved in the superior-inferior direction at a motion displacement of 20 mm. Conventional respiratory-gated (RG) and Q.Freeze images were sorted into 6, 10, and 13 phase-groups. The SUV_ave was calculated from the background of the body phantom, and the SUV_max was determined from the hot spheres of the liver tumors. Three patients with four liver tumors were also clinically assessed by whole-body and RG PET. The RG and Q.Freeze images derived from the clinical study were also sorted into 6, 10 and 13 phase-groups. Liver signal-to-noise ratio (SNR) and SUV_max were determined from the RG and Q.Freeze clinical images. Results: The SUV_ave of Q.Freeze images was the same as those derived from the body phantom using RG. The liver SNR improved with Q.Freeze, and the SUVs_max was not overestimated when Q.Freeze was applied in both the phantom and clinical studies. Q.Freeze did not degrade the liver SNR and SUV_max even though the phase number was larger. Conclusions: Q.Freeze delivered qualitative and quantitative motion correction than conventional RG imaging even in 10-phase groups.