Abstract
Respiratory motion degrades the detection and quantification capabilities of PET/CT imaging. Moreover, mismatch between a fast helical CT and a time-averaged PET image due to respiratory motion results in additional attenuation correction (AC) artifacts and inaccurate localization. Current motion compensation (MC) approaches typically have three limitations: 1) the mismatch among respiratory-gated PET images and the CTAC map can introduce artifacts in the gated PET reconstructions that can subsequently affect the accuracy of the motion estimation; 2) sinogram-based correction approaches do not correct for intra-gate motion due to intra-cycle and inter-cycle breathing variations; 3) the mismatch between the PET MC-reference gate and CT can cause additional CT-mismatch artifact. In this study, we established a motion correction framework to address these limitations. Methods: In the proposed framework, the combined emission/transmission reconstruction algorithm was used for phase-matched gated-PET reconstructions to facilitate the motion model building. Event-by-event non-rigid respiratory MC method with correlations between internal organ motion and external respiratory signals was used to correct both intra-cycle and inter-cycle breathing variations. The PET reference gate is automatically determined by a newly proposed CT-matching algorithm. We applied the new framework to 13 human datasets with 3 different radiotracers and in total 323 lesions, and compared its performance with CTAC-based and Non-AC-based (NAC) approaches. Validation using 4D CT was performed for one lung cancer dataset. Results: For the ten 18F-FDG studies, the proposed method outperformed (p<0.006) both CTAC- and NAC-based methods in terms of ROI-based SUVmean, SUVmax and SUV-ratio improvements over no motion correction (NMC) (SUVmean: 19.9% vs. 14.0% vs. 13.2%; SUVmax: 15.5% vs. 10.8% vs. 10.6%; SUV-ratio: 24.1% vs. 17.6% vs. 16.2%, for the proposed, CTAC- and NAC-based methods, respectively). The proposed method increased SUV-ratio values over NMC for 94.4% lesions compared to 84.8% and 86.4% using CTAC- and NAC-based methods, respectively. For the two 18F-FPDTBZ studies, the proposed method reduced the CT-mismatch artifacts in the lower lung where the CTAC-based approach failed, and maintained the quantification accuracy of bone marrow where the NAC-based approach failed. For the 18F-FMISO study, the proposed method outperformed both CTAC- and NAC-based methods in terms of motion estimation accuracy at two lung lesion locations. Conclusion: The proposed PET/CT respiratory event-by-event motion-correction framework with motion information derived from matched attenuation corrected PET data provides superior image quality than CTAC- and NAC-based methods for multiple tracers.
- Image Reconstruction
- PET
- PET/CT
- PET
- event-by-event
- matched attenuation correction
- non-rigid
- respiratory motion correction
- Copyright © 2018 by the Society of Nuclear Medicine and Molecular Imaging, Inc.