Linear least squares compartmental-modelindependent parameter identification in PET

Joseph A. Thie, Gary T. Smith, Karl Hubner

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

A simplified approach involving linear-regression straight-line parameter fitting of dynamic scan data is developed for both specific and nonspecific models. Where compartmentalmodel topologies apply, the measured activity may be expressed in terms of: its integrals, plasma activity and plasma integrals-all in a linear expression with macroparameters as coefficients. Multiple linear regression, as in spreadsheet software, determines parameters for best data fits. Positron emission tomography (PET)-acquired gray-matter images in a dynamic scan are analyzed: both by this method and by traditional iterative nonlinear least squares. Both patient and simulated data were used. Regression and traditional methods are in expected agreement. MonteCarlo simulations evaluate parameter standard deviations, due to data noise, and much smaller noise-induced biases. Unique straight-line graphical displays permit visualizing data influences on various macroparameters as changes in slopes. Advantages of regression fitting are: simplicity, speed, ease of implementation in spreadsheet software, avoiding risks of convergence failures or false solutions in iterative least squares, and providing various visualizations of the uptake process by straight line graphical displays. Multiparameter model-independent analyses on lesser understood systems is also made possible.

Original languageEnglish (US)
Pages (from-to)11-16
Number of pages6
JournalIEEE Transactions on Medical Imaging
Volume16
Issue number1
DOIs
StatePublished - Jan 1 1997

Fingerprint

Positron emission tomography
Spreadsheets
Least-Squares Analysis
Linear regression
Positron-Emission Tomography
Linear Models
Identification (control systems)
Software
Display devices
Plasmas
Noise
Thermodynamic properties
Visualization
Topology
Gray Matter

All Science Journal Classification (ASJC) codes

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

Cite this

Linear least squares compartmental-modelindependent parameter identification in PET. / Thie, Joseph A.; Smith, Gary T.; Hubner, Karl.

In: IEEE Transactions on Medical Imaging, Vol. 16, No. 1, 01.01.1997, p. 11-16.

Research output: Contribution to journalArticle

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