Strategies to improve neuroreceptor parameter estimation by linear regression analysis

Masanori Ichise, Hiroshi Toyama, Robert B. Innis, Richard E. Carson

Research output: Contribution to journalArticlepeer-review

281 Citations (Scopus)


In an attempt to improve neuroreceptor distribution volume (V) estimates, the authors evaluated three alternative linear methods to Logan graphical analysis (GA): GA using total least squares (TLS), and two multilinear analyses, MA1 and MA2, based on mathematical rearrangement of GA equation and two-tissue compartments, respectively, using simulated and actual PET data of two receptor tracers, [18F]FCWAY and [11C]MDL 100,907. For simulations, all three methods decreased the noise-induced GA bias (up to 30%) at the expense of increased variability. The bias reduction was most pronounced for MA1, moderate to large for MA2, and modest to moderate for TLS. In addition, GA, TLS, and MA1, methods that used only a portion of the data (T >t*, chosen by an automatic process), showed a small V underestimation for [11C]MDL 100,907 with its slow kinetics, due to selection of t* before the true point of linearity. These noniterative methods are computationally simple, allowing efficient pixelwise parameter estimation. For tracers with kinetics that permit t* to be accurately identified within the study duration, MA1 appears to be the best. For tracers with slow kinetics and low to moderate noise, however, MA2 may provide the lowest bias while maintaining computational ease for pixelwise parameter estimation.

Original languageEnglish
Pages (from-to)1271-1281
Number of pages11
JournalJournal of Cerebral Blood Flow and Metabolism
Issue number10
Publication statusPublished - 01-10-2002
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neurology
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine


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