PT  - JOURNAL ARTICLE
AU  - Yunhan Yu
AU  - Chung Chan
AU  - Tianyu Ma
AU  - Yaqiang Liu
AU  - Jean-Dominique Gallezot
AU  - Mika Naganawa
AU  - Olivia J. Kelada
AU  - Mary Germino
AU  - Albert J. Sinusas
AU  - Richard E. Carson
AU  - Chi Liu
TI  - Event-by-Event Continuous Respiratory Motion Correction for Dynamic PET Imaging
AID  - 10.2967/jnumed.115.167676
DP  - 2016 Jul 01
TA  - Journal of Nuclear Medicine
PG  - 1084--1090
VI  - 57
IP  - 7
4099  - http://jnm.snmjournals.org/content/57/7/1084.short
4100  - http://jnm.snmjournals.org/content/57/7/1084.full
SO  - J Nucl Med2016 Jul 01; 57
AB  - Existing respiratory motion-correction methods are applied only to static PET imaging. We have previously developed an event-by-event respiratory motion-correction method with correlations between internal organ motion and external respiratory signals (INTEX). This method is uniquely appropriate for dynamic imaging because it corrects motion for each time point. In this study, we applied INTEX to human dynamic PET studies with various tracers and investigated the impact on kinetic parameter estimation. Methods: The use of 3 tracers—a myocardial perfusion tracer, 82Rb (n = 7); a pancreatic β-cell tracer, 18F-FP(+)DTBZ (n = 4); and a tumor hypoxia tracer, 18F-fluoromisonidazole (18F-FMISO) (n = 1)—was investigated in a study of 12 human subjects. Both rest and stress studies were performed for 82Rb. The Anzai belt system was used to record respiratory motion. Three-dimensional internal organ motion in high temporal resolution was calculated by INTEX to guide event-by-event respiratory motion correction of target organs in each dynamic frame. Time–activity curves of regions of interest drawn based on end-expiration PET images were obtained. For 82Rb studies, K1 was obtained with a 1-tissue model using a left-ventricle input function. Rest–stress myocardial blood flow (MBF) and coronary flow reserve (CFR) were determined. For 18F-FP(+)DTBZ studies, the total volume of distribution was estimated with arterial input functions using the multilinear analysis 1 method. For the 18F-FMISO study, the net uptake rate Ki was obtained with a 2-tissue irreversible model using a left-ventricle input function. All parameters were compared with the values derived without motion correction. Results: With INTEX, K1 and MBF increased by 10% ± 12% and 15% ± 19%, respectively, for 82Rb stress studies. CFR increased by 19% ± 21%. For studies with motion amplitudes greater than 8 mm (n = 3), K1, MBF, and CFR increased by 20% ± 12%, 30% ± 20%, and 34% ± 23%, respectively. For 82Rb rest studies, INTEX had minimal effect on parameter estimation. The total volume of distribution of 18F-FP(+)DTBZ and Ki of 18F-FMISO increased by 17% ± 6% and 20%, respectively. Conclusion: Respiratory motion can have a substantial impact on dynamic PET in the thorax and abdomen. The INTEX method using continuous external motion data substantially changed parameters in kinetic modeling. More accurate estimation is expected with INTEX.