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Total-Body Parametric Imaging Using Relative Patlak Plot.

Siqi Li, Yasser G Abdelhafez, Lorenzo Nardo

|August 7, 2024

Summary

This summary is machine-generated.

The Relative Patlak (RP) plot enables total-body parametric imaging from short 20-min dynamic PET scans without an input function. This method, combined with deep kernel noise reduction, yields high-quality images with improved lesion contrast and myocardial visualization.

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Area of Science:

Nuclear Medicine
Radiochemistry
Medical Imaging Physics

Background:

Standard Patlak plot analysis for dynamic PET imaging requires a full-time input function, limiting whole-body parametric imaging.
Short-duration dynamic PET scans (e.g., 20 minutes) are increasingly used clinically but present noise challenges for quantitative analysis.

Purpose of the Study:

To demonstrate the feasibility of Relative Patlak (RP) plot for total-body parametric imaging using short (20-min) dynamic PET scans.
To evaluate the RP plot's performance in lesion quantification, contrast enhancement, and myocardial visualization, with noise reduction using a deep kernel method.

Main Methods:

Developed and applied the Relative Patlak (RP) plot, eliminating the need for an early-time input function.
Utilized a self-supervised deep kernel method for noise reduction in parametric images from 20-min dynamic scans.
Evaluated RP plot performance against standard Patlak plots in 22 subjects and two clinical cancer patient scans.

Main Results:

RP plot generates high-quality parametric images from 20-min dynamic PET scans.
RP plot's quantitative parameter Ki' showed high correlation with standard Patlak Ki in lesions and organs.
Ki' images significantly improved lesion-to-background contrast and enabled myocardial visualization compared to conventional SUVs.

Conclusions:

The Relative Patlak plot is a feasible method for total-body parametric imaging from short (20-min) dynamic PET scans.
RP plot analysis, coupled with deep kernel noise reduction, offers quantitative potential and improved image quality for clinical applications.
This approach enhances lesion detection and characterization and facilitates cardiac assessment in total-body PET imaging.