PT  - JOURNAL ARTICLE
AU  - Wang, Yiran
AU  - Spencer, Benjamin A.
AU  - Schmall, Jeffrey
AU  - Li, Elizabeth
AU  - Badawi, Ramsey D.
AU  - Jones, Terry
AU  - Cherry, Simon R.
AU  - Wang, Guobao
TI  - High-Temporal-Resolution Lung Kinetic Modeling Using Total-Body Dynamic PET with Time-Delay and Dispersion Corrections
AID  - 10.2967/jnumed.122.264810
DP  - 2023 Jul 01
TA  - Journal of Nuclear Medicine
PG  - 1154--1161
VI  - 64
IP  - 7
4099  - http://jnm.snmjournals.org/content/64/7/1154.short
4100  - http://jnm.snmjournals.org/content/64/7/1154.full
SO  - J Nucl Med2023 Jul 01; 64
AB  - Tracer kinetic modeling in dynamic PET has the potential to improve the diagnosis, prognosis, and research of lung diseases. The advent of total-body PET systems with much greater detection sensitivity enables high-temporal-resolution (HTR) dynamic PET imaging of the lungs. However, existing models may become insufficient for modeling the HTR data. In this paper, we investigate the necessity of additional corrections to the input function for HTR lung kinetic modeling. Methods: Dynamic scans with HTR frames of as short as 1 s were performed on 13 healthy subjects with a bolus injection of about of 18F-FDG using the uEXPLORER total-body PET/CT system. Three kinetic models with and without time-delay and dispersion corrections were compared for the quality of lung time–activity curve fitting using the Akaike information criterion. The impact on quantification of 18F-FDG delivery rate , net influx rate and fractional blood volume was assessed. Parameter identifiability analysis was also performed to evaluate the reliability of kinetic quantification with respect to noise. Correlation of kinetic parameters with age was investigated. Results: HTR dynamic imaging clearly revealed the rapid change in tracer concentration in the lungs and blood supply (i.e., the right ventricle). The uncorrected input function led to poor time–activity curve fitting and biased quantification in HTR kinetic modeling. The fitting was improved by time-delay and dispersion corrections. The proposed model resulted in an approximately 85% decrease in , an approximately 75% increase in , and a more reasonable (∼0.14) than the uncorrected model (∼0.04). The identifiability analysis showed that the proposed models had good quantification stability for , , and . The estimated by the proposed model with simultaneous time-delay and dispersion corrections correlated inversely with age, as would be expected. Conclusion: Corrections to the input function are important for accurate lung kinetic analysis of HTR dynamic PET data. The modeling of both delay and dispersion can improve model fitting and significantly impact quantification of , , and .