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Related Experiment Video

Updated: Jun 18, 2026

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment
10:14

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

Published on: May 10, 2024

Single-input-dual-output modeling of image-based input function estimation.

Yi Su1, Kooresh I Shoghi

  • 1Department of Radiology, Washington University School of Medicine, 510 South Kingshighway Boulevard, Campus Box 8225, St. Louis, MO 63110, USA.

Molecular Imaging and Biology
|December 2, 2009
PubMed
Summary
This summary is machine-generated.

A new simplified hybrid single-input-dual-output (HSIDO) algorithm accurately estimates the plasma input function (PIF) for small-animal PET imaging. This method improves quantification by reducing errors and enhancing precision in kinetic estimates.

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10:14

Motor Imagery Performance Through Embodied Digital Twins in a Virtual Reality-Enabled Brain-Computer Interface Environment

Published on: May 10, 2024

Area of Science:

  • Nuclear Medicine
  • Medical Imaging
  • Pharmacokinetics

Background:

  • Accurate quantification in small-animal positron emission tomography (PET) relies on precise plasma input function (PIF) determination.
  • Existing methods for PIF estimation may have limitations in accuracy and precision for dynamic PET studies.

Purpose of the Study:

  • To propose and validate a novel simplified hybrid single-input-dual-output (HSIDO) algorithm for estimating the PIF in small-animal PET imaging.
  • To improve the accuracy and reliability of quantitative analysis in small-animal dynamic PET studies.

Main Methods:

  • The HSIDO algorithm integrates the peak of the input function from two region-of-interest time-activity curves with a biexponential tail.
  • Simultaneous optimization of partial volume parameters was performed.
  • Validation involved simulated and real small-animal PET data, comparing HSIDO against a reference method using AUC error, bias, and precision of kinetic parameters.

Main Results:

  • The HSIDO method demonstrated significantly lower area under curve (AUC) error (P < 0.05) compared to the reference method.
  • HSIDO exhibited reduced bias and improved precision in the estimation of kinetic micro-parameters.
  • Validation using both simulated and real PET data confirmed the superior performance of the HSIDO algorithm.

Conclusions:

  • The HSIDO algorithm represents an advancement in modeling-based PIF estimation for small-animal PET.
  • This method offers improved accuracy and precision, making it suitable for quantitative analysis in dynamic small-animal PET studies.