Total-Body Parametric Imaging Using Relative Patlak Plot

• Published in: Journal of Nuclear Medicine Vol. 66; no. 4; pp. 654 - 661
• Main Authors: Li, Siqi, Abdelhafez, Yasser G., Nardo, Lorenzo, Cherry, Simon R., Badawi, Ramsey D., Wang, Guobao
• Format: Journal Article
• Language: English
• Published: United States 01.04.2025
• Subjects: Adult; Female; Fluorodeoxyglucose F18; Humans; Image Processing, Computer-Assisted - methods; Male; Middle Aged; Positron-Emission Tomography - methods; Whole Body Imaging - methods; relative Patlak plot; standard clinical scan; total-body PET parametric imaging; self-supervised deep learning; kinetic modeling
• ISSN: 0161-5505; 2159-662X; 1535-5667
• DOI: 10.2967/jnumed.124.268496

The standard Patlak plot, a simple yet efficient model, is widely used to describe irreversible tracer kinetics for dynamic PET imaging. Its widespread application to whole-body parametric imaging remains constrained because of the need for a full-time-course input function (e.g., 1 h). In this paper, we demonstrate the relative Patlak (RP) plot, which eliminates the need for the early-time input function, for total-body parametric imaging and its application to 20-min clinical scans acquired in list mode. We conducted a theoretic analysis to indicate that the RP intercept ' is equivalent to a ratio of the SUV relative to the plasma concentration, whereas the RP slope ' is equal to the standard Patlak (net influx rate) multiplied by a global scaling factor for each subject. One challenge in applying RP to a short scan duration (e.g., 20 min) is the resulting high noise in the parametric images. We applied a self-supervised deep-kernel method for noise reduction. Using the standard Patlak plot as the reference, the RP method was evaluated for lesion quantification, lesion-to-background contrast, and myocardial visualization in total-body parametric imaging in 22 human subjects (12 healthy subjects and 10 cancer patients) who underwent a 1-h dynamic F-FDG scan. The RP method was also applied to the dynamic data reconstructed from a clinical standard 20-min list-mode scan either at 1 or 2 h after injection for 2 cancer patients. We demonstrated that it is feasible to obtain high-quality parametric images from 20-min scans using RP parametric imaging with a self-supervised deep-kernel noise-reduction strategy. The RP slope ' was highly correlated with the standard Patlak in lesions and major organs, demonstrating its quantitative potential across subjects. Compared with conventional SUVs, the ' images significantly improved lesion contrast and enabled visualization of the myocardium for potential cardiac assessment. The application of the RP parametric imaging to the 2 clinical scans also showed similar benefits. Using total-body PET with the RP approach, it is feasible to generate parametric images using data from a 20-min clinical list-mode scan.