Evaluation of Data-Driven Rigid Motion Correction in Clinical Brain PET Imaging

Abstract

Head motion during brain PET imaging can cause significant degradation of the quality of the reconstructed image, leading to reduced diagnostic value and inaccurate quantitation. A fully data-driven motion correction approach was recently demonstrated to produce highly accurate motion estimates (< 1 mm) with high temporal resolution (≥ 1 Hz), which can then be used for a motion corrected reconstruction. This can be applied retrospectively with no impact on the clinical image acquisition protocol. We present a reader-based evaluation and an atlas-based quantitative analysis of this motion correction approach within a clinical cohort. Methods: Clinical patient data were collected over 2019–2020 and processed retrospectively. Motion estimation was performed using image-based registration on reconstructions of ultra-short frames (0.6–1.8 s), after which fully motion corrected list-mode reconstructions were performed. Two readers graded the motion corrected and uncorrected reconstructions. An atlas-based quantitative analysis was performed. Paired Wilcoxon tests were used to test for significant differences in the reader scores and standard uptake values between the reconstructions. Levene’s test was used to test whether motion correction had a greater impact on the quantitation in the presence of motion than when low motion was observed. Results: 50 standard clinical ¹⁸F-fluorodeoxyglucose brain PET data sets (age range 13–83 years, mean age ± standard deviation 59 ± 20 years, 27 women) from 3 scanners were collected. The reader study showed a significantly different, diagnostically relevant improvement by motion correction for cases where motion was present (P = 0.02) and no impact in low motion cases. 8% of all data sets improved from diagnostically “unacceptable” to “acceptable”. The atlas-based analysis demonstrated a significant difference between the motion corrected and uncorrected reconstructions in cases of high motion for 7 of 8 ROIs (P < 0.05). Conclusion: The proposed data-driven motion estimation and correction approach demonstrated a clinically significant impact on brain PET image reconstruction.