Evaluation of a Novel Elastic Respiratory Motion Correction Algorithm on Quantification and Image Quality in Abdominothoracic PET/CT

Our aim was to evaluate in phantom and patient studies a recently developed elastic motion deblurring (EMDB) technique that makes use of all the acquired PET data and compare its performance with other conventional techniques such as phase-based gating (PBG) and HD⋅Chest (HDC), both of which use fractions of the acquired data. Comparisons were made with respect to static whole-body (SWB) images with no motion correction. Methods: A phantom simulating respiratory motion of the thorax with lung lesions (5 spheres with internal diameters of 10–28 mm) was scanned with 0, 1, 2, and 3 cm of motion. Four reconstructions were performed: SWB, PBG, HDC, and EMDB. For PBG, the average (PBGave) and maximum bin (PBGmax) were used. To compare the reconstructions, the ratios of SUVmax, SUVpeak, and contrast-to-noise ratio (CNR) were calculated with respect to SWB. Additionally, 46 patients with lung or liver tumors less than 3 cm in diameter were studied. Measurements of SUVmax, SUVpeak, and CNR were made for 46 lung and 19 liver lesions. To evaluate image noise, the SUV SD was measured in healthy lung and liver tissue and in the phantom background. Finally, the subjective image quality of patient examinations was scored on a 5-point scale by 4 radiologists. Results: In the phantom, EMDB increased SUVmax and SUVpeak over SWB but to a lesser extent than the other reconstruction methodologies. The ratio of CNR with respect to SWB for EMDB, however, was higher than all other reconstructions (0.68 with EMDB > 0.54 with HDC > 0.41 with PBGmax > 0.31 with PBGave). Similar results were seen in patient studies. SUVmax and SUVpeak were higher by, respectively, 19.3% and 11.1% with EMDB, 21.6% and 13.9% with HDC, 22.8% and 12.8% with PBGave, and 45.6% and 26.8% with PBGmax, compared with SWB. Lung and liver noise increased with EMDB by, respectively, 3% and 15%, with HDC by 35% and 56%, with PBGave by 100% and 170%, and with PBGmax by 146% and 219%. CNR increased in lung and liver tumors only with EMDB (18% and 13%, respectively) and decreased with HDC (−14% and −23%), PBGave (−39% and −63%), and PBGmax (−18% and −46%). The average radiologist scores of image quality were 4.0 ± 0.8 with SWB, 3.7 ± 1.0 with EMDB, 3.1 ± 1.0 with HDC, and 1.5 ± 0.7 with PBG. Conclusion: The EMDB algorithm had the least increase in image noise, improved lesion CNR, and had the highest overall image quality score.