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Related Experiment Video

Updated: Jun 28, 2025

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Optimization-derived blood input function using a kernel method and its evaluation with total-body PET for brain

Yansong Zhu1, Quyen Tran1, Yiran Wang2

  • 1Department of Radiology, University of California Davis Medical Center, Sacramento, CA 95817, USA.

Neuroimage
|April 21, 2024
PubMed
Summary

A new kernel SIME method accurately estimates the blood input function for dynamic PET imaging. This approach improves kinetic parameter estimation in brain scans, overcoming limitations of previous methods.

Keywords:
Input function estimationKernel methodTotal-body PETTracer kinetic modeling

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Area of Science:

  • Nuclear medicine
  • Medical imaging
  • Pharmacokinetics

Background:

  • Dynamic PET enables physiological parameter quantification via tracer kinetic modeling.
  • Conventional PET scanners face challenges with image-derived input functions (ID-IF) due to partial volume effects, particularly for brain and head/neck cancer imaging.
  • Optimization-derived input function (OD-IF) methods like SIME avoid ID-IF but can be ill-posed.

Purpose of the Study:

  • To develop and evaluate a novel kernel SIME method combining OD-IF and ID-IF concepts.
  • To enhance the accuracy of blood input function and kinetic parameter estimation in dynamic PET.
  • To validate the method using simulations and human subject data on a total-body PET scanner.

Main Methods:

  • Proposed a kernel SIME method using the carotid artery ID-IF as prior information to stabilize the SIME approach.
  • Evaluated the method using 18F-fluorodeoxyglucose (FDG) studies with computer simulations and 20 human subject scans on the uEXPLORER scanner.
  • Investigated the impact of the number of regions of interest (ROIs) on kernel SIME performance.

Main Results:

  • Kernel SIME demonstrated accurate OD-IF estimation, closely matching the reference input function.
  • Kernel SIME provided significantly more accurate kinetic parameter estimation compared to CA ID-IF and conventional SIME in human subjects (R=0.97, MAE=10.5%).
  • Increasing the number of ROIs enhanced the overall performance of the kernel SIME method.

Conclusions:

  • The kernel SIME method offers a promising solution for accurate blood input function estimation in dynamic PET.
  • This approach facilitates precise kinetic parameter quantification for brain PET parametric imaging.
  • The method effectively addresses limitations of conventional ID-IF and SIME techniques.