Evaluation of a novel elastic respiratory motion correction algorithm on quantification and image quality in abdomino-thoracic PET/CT

Joseph G Meier; Carol C Wu; Sonia L Betancourt Cuellar; Mylene T Truong; Jeremy R Erasmus; Samuel Einstein; Osama Mawlawi

doi:10.2967/jnumed.118.213884

Evaluation of a novel elastic respiratory motion correction algorithm on quantification and image quality in abdomino-thoracic PET/CT

J Nucl Med. 2018 Aug 16;60(2):279-284. doi: 10.2967/jnumed.118.213884. Online ahead of print.

Authors

Joseph G Meier¹, Carol C Wu¹, Sonia L Betancourt Cuellar¹, Mylene T Truong¹, Jeremy R Erasmus¹, Samuel Einstein¹, Osama Mawlawi¹

Affiliation

¹ MD Anderson Cancer Center, United States.

PMID: 30115689
DOI: 10.2967/jnumed.118.213884

Abstract

Our aim is to evaluate in phantom and patient studies a recently developed elastic motion debluring (EMDB) technique which makes use of all the acquired PET data and compare its performance to other conventional techniques such as phase based gating (PBG) and HDChest (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 ID=10- 28 mm) was scanned with 0, 1, 2, and 3 cm 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 SUV_max (RSmax), SUVpeak (RSpeak), and CNR (RCNR) were calculated with respect to SWB. Additionally, 46 patients with lung or liver tumors < 3 cm diameter were also studied. Measurements of SUV_max, SUVpeak, and contrast-to-noise ratio (CNR) were made for 46 lung and 19 liver lesions. To evaluate image noise, the SUV standard deviation was measured in healthy lung and liver tissue and in the phantom background. Finally, subjective image quality of patient exams was scored on a 5 point scale by four radiologists. Results: In the phantom, EMDB increased SUV_max/SUVpeak over SWB but to a lesser extent than the other reconstruction methodologies. The RCNR for EMDB however was higher than all other reconstructions (0.68 EMDB > 0.54 HDC > 0.41 PBGmax > 0.31 PBGave). Similar results were seen in patient studies. The SUV_max/SUVpeak were higher by 19.3/11.1% EMDB, 21.6/13.9% HDC, 22.8/12.8% PBGave, and 45.6/26.8% PBGmax compared to SWB. Lung/liver noise increased EMDB (3/15%), HDC (35/56%), PBGave (100/170%), and PBGmax (146/219%). CNR increased in lung/liver tumors only for EMDB (18/13%), and decreased for HDC (-14/-23%), PBGave (-39/-63%), and PBGmax (-18/-46%). Average radiologist scores of image quality were SWB (4.0 ± 0.8) > EMDB (3.7 ± 1.0) > HDC (3.1 ± 1.0) > PBG (1.5 ± 0.7). Conclusion: The EMDB algorithm had the least increase in image noise, improved lesion CNR, and had the highest overall image quality score.

Keywords: Image Processing; PET/CT; Quantification; Respiratory; Respiratory motion correction.