Respiratory Motion Compensation for PET/CT with Motion Information Derived from Matched Attenuation-Corrected Gated PET Data

Abstract

Respiratory motion degrades the detection and quantification capabilities of PET/CT imaging. Moreover, mismatch between a fast helical CT image and a time-averaged PET image due to respiratory motion results in additional attenuation correction artifacts and inaccurate localization. Current motion compensation approaches typically have 3 limitations: the mismatch among respiration-gated PET images and the CT attenuation correction (CTAC) map can introduce artifacts in the gated PET reconstructions that can subsequently affect the accuracy of the motion estimation; sinogram-based correction approaches do not correct for intragate motion due to intracycle and intercycle breathing variations; and the mismatch between the PET motion compensation reference gate and the CT image can cause an 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. An event-by-event nonrigid respiratory motion compensation method with correlations between internal organ motion and external respiratory signals was used to correct both intracycle and intercycle breathing variations. The PET reference gate was automatically determined by a newly proposed CT-matching algorithm. We applied the new framework to 13 human datasets with 3 different radiotracers and 323 lesions and compared its performance with CTAC and non-attenuation correction (NAC) approaches. Validation using 4-dimensional CT was performed for one lung cancer dataset. Results: For the 10 ¹⁸F-FDG studies, the proposed method outperformed (P < 0.006) both the CTAC and the NAC methods in terms of region-of-interest-based SUV_mean, SUV_max, and SUV ratio improvements over no motion correction (SUV_mean: 19.9% vs. 14.0% vs. 13.2%; SUV_max: 15.5% vs. 10.8% vs. 10.6%; SUV ratio: 24.1% vs. 17.6% vs. 16.2%, for the proposed, CTAC, and NAC methods, respectively). The proposed method increased SUV ratios over no motion correction for 94.4% of lesions, compared with 84.8% and 86.4% using the CTAC and NAC methods, respectively. For the 2 ¹⁸F-fluoropropyl-(+)-dihydrotetrabenazine studies, the proposed method reduced the CT-mismatch artifacts in the lower lung where the CTAC approach failed and maintained the quantification accuracy of bone marrow where the NAC approach failed. For the ¹⁸F-FMISO study, the proposed method outperformed both the CTAC and the NAC methods in terms of motion estimation accuracy at 2 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 image quality superior to that of the CTAC and NAC methods for multiple tracers.

Keywords: PET; event-by-event; matched attenuation correction; non-rigid; respiratory motion correction.

FIGURE 1.

(A) Inspiration-phase (left) and expiration-phase (right) gated PET reconstructions superimposed with CT for sample ¹⁸F-FPDTBZ study. Arrows in A point to attenuation mismatch. (B) Inspiration-phase (left) and expiration-phase (right) PET reconstruction of kidneys.

FIGURE 2.

Flowchart of AIM framework, consisting of reference-phase determination, motion model building, and event-by-event motion correction.

FIGURE 3.

End-inspiration gated reconstructions using different AC methods (top) and end-inspiration–end-inspiration motion vectors derived from each method, superimposed on end-inspiration reconstructions (bottom) for ¹⁸F-FPDTBZ study 1.

FIGURE 4.

Difference images between μ_4DCT and μ_Syn at end-inspiration gate. Right 3 columns represent results of CTAC-based, NAC-based, and AIM methods, respectively. AC map is shown as reference. (A) Coronal slice at right-lung region with large tumor. Arrows point to top of tumor. (B) Coronal slice of right-lung region, where arrows point to liver–lung border.

FIGURE 5.

Sample slices of final motion-corrected reconstructions of ¹⁸F-FDG studies: coronal slices from study 2 (A and B), image from study 3 (C), and image from study 9 (D). PET and CT fused images are shown for whole field of view. Zoomed-in images of local ROIs show results for NMC and different correction methods.

FIGURE 6.

Final reconstruction examples of ¹⁸F-FPDTBZ studies with different correction methods. (A) Coronal liver–lung region for study 1. Arrows point to lung–liver border. (B) Coronal right-kidney region for study 2. Arrows point to right-kidney cortex. (C) Sagittal spine region for study 1. Arrows point to bone marrow gap.