Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Reducing the noise effects in Logan graphic analysis for PET receptor
  measurements   Logan's graphical analysis (LGA) is a widely-used approach for quantification
of biochemical and physiological processes from Positron emission tomography
(PET) image data. A well-noted problem associated with the LGA method is the
bias in the estimated parameters. We recently systematically evaluated the bias
associated with the linear model approximation and developed an alternative to
minimize the bias due to model error. In this study, we examined the noise
structure in the equations defining linear quantification methods, including
LGA. The noise structure conflicts with the conditions given by the
Gauss-Markov theorem for the least squares (LS) solution to generate the best
linear unbiased estimator. By carefully taking care of the data error
structure, we propose to use structured total least squares (STLS) to obtain
the solution using a one-dimensional optimization problem. Simulations of PET
data for [11C] benzothiazole-aniline (Pittsburgh Compound-B [PIB]) show that
the proposed method significantly reduces the bias. We conclude that the bias
associated with noise is primarily due to the unusual structure of he
correlated noise and it can be reduced with the proposed STLS method.

20
The categories of this paper are: acc-phys
19
The categories of this paper are: eess
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
1
The categories of this paper are: cs
29
The categories of this paper are: funct-an
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
28
The categories of this paper are: alg-geom