Automatic 3D registration of dynamic stress and rest 82Rb and flurpiridaz F 18 myocardial perfusion PET data for patient motion detection and correction

• Published in: Medical physics (Lancaster) 2011-11, Vol.38 (11), p.6313-6326
• Main Authors: Woo, Jonghye, Tamarappoo, Balaji, Dey, Damini, Nakazato, Ryo, Le Meunier, Ludovic, Ramesh, Amit, Lazewatsky, Joel, Germano, Guido, Berman, Daniel S., Slomka, Piotr J.
• Format: Article
• Language: English
• Subjects: cardiac PET myocardial perfusion imaging; Computed tomography; Flow visualization; flurpiridaz F 18; Heart; image registration; Medical image artifacts; Medical image noise; medical image processing; Medical image quality; Medical imaging; motion compensation; motion correction and detection; Motion detection; Nuclear Medicine Physics; positron emission tomography; Positron emission tomography (PET); Registration
• ISSN: 0094-2405; 2473-4209; 0094-2405
• DOI: 10.1118/1.3656951

Purpose: The authors aimed to develop an image-based registration scheme to detect and correct patient motion in stress and rest cardiac positron emission tomography (PET)/CT images. The patient motion correction was of primary interest and the effects of patient motion with the use of flurpiridaz F 18 and82Rb were demonstrated. Methods: The authors evaluated stress/rest PET myocardial perfusion imaging datasets in 30 patients (60 datasets in total, 21 male and 9 female) using a new perfusion agent (flurpiridaz F 18) (n = 16) and 82Rb (n = 14), acquired on a Siemens Biograph-64 scanner in list mode. Stress and rest images were reconstructed into 4 (82Rb) or 10 (flurpiridaz F 18) dynamic frames (60 s each) using standard reconstruction (2D attenuation weighted ordered subsets expectation maximization). Patient motion correction was achieved by an image-based registration scheme optimizing a cost function using modified normalized cross-correlation that combined global and local features. For comparison, visual scoring of motion was performed on the scale of 0 to 2 (no motion, moderate motion, and large motion) by two experienced observers. Results: The proposed registration technique had a 93% success rate in removing left ventricular motion, as visually assessed. The maximum detected motion extent for stress and rest were 5.2 mm and 4.9 mm for flurpiridaz F 18 perfusion and 3.0 mm and 4.3 mm for82Rb perfusion studies, respectively. Motion extent (maximum frame-to-frame displacement) obtained for stress and rest were (2.2 ± 1.1, 1.4 ± 0.7, 1.9 ± 1.3) mm and (2.0 ± 1.1, 1.2 ±0 .9, 1.9 ± 0.9) mm for flurpiridaz F 18 perfusion studies and (1.9 ± 0.7, 0.7 ± 0.6, 1.3 ± 0.6) mm and (2.0 ± 0.9, 0.6 ± 0.4, 1.2 ± 1.2) mm for 82Rb perfusion studies, respectively. A visually detectable patient motion threshold was established to be ≥2.2 mm, corresponding to visual user scores of 1 and 2. After motion correction, the average increases in contrast-to-noise ratio (CNR) from all frames for larger than the motion threshold were 16.2% in stress flurpiridaz F 18 and 12.2% in rest flurpiridaz F 18 studies. The average increases in CNR were 4.6% in stress 82Rb studies and 4.3% in rest 82Rb studies. Conclusions: Fully automatic motion correction of dynamic PET frames can be performed accurately, potentially allowing improved image quantification of cardiac PET data.