Accelerated Cross-Reference Maximum Likelihood Estimates for PET Image Reconstruction
Date Issued
1999-07-31
Date
1999-07-31
Author(s)
DOI
882213E002016
Abstract
Positron Emission Tomography (PET) is
an imaging modality giving distribution of
positron-emitting isotope-labeled chemicals in
the human body. Unlike X-ray CT and
MRI, which provide anatomical data, PET
reveals functional information on in vivo
physiology and metabolism of the human
body. Clinically, early detection of a disease
before morphologically distinguishable may
be achieved through PET by studying
physiological or metabolic disorders. Hence,
PET has become one of the most important
imaging tools in modern diagnosis. The
intensity of metabolic activity is indirectly
observed through the scintillation detectors
outside a human body. The reconstruction
from indirect observations to a target image is
a typical problem in statistical inverse
problem. Due to the inherent ill-posedness
of statistical inverse problems, the
reconstructed images of positron emission
tomography (PET) without regularization will
have noise and edge artifacts. This is the
limit of PET, which can not be resolved from
the improvement of instrumental designs. In
order to have better reconstructed images, it is
necessary to borrow the strength from the
related information from expertise or other
tomography systems, such as X-ray CT scan,
MRI, and so forth.
The correlated boundary information
may offer the useful information in reducing
the noise and edge artifacts. However, the
boundary information may be incomplete or
incorrect since the anatomy boundaries are
different from the functional ones. Thus,
cross-reference is important to make use the
boundary information wisely. In this project,
we will study the cross-reference
reconstruction methods for the maximum
likelihood estimate with the adapted EMalgorithm for PET in the presence of
accidental coincidence (AC) events and
attenuation. In particular, fast reconstruction
algorithms for both sequential and parallel
approaches will be investigated, which is very
important for the practical use of the proposed
PET reconstruction algorithms. In this
project, we will use a cluster of computers as
the platform of the parallel reconstruction
algorithms. The aim is to find the fast,
efficient and reliable approaches that can
reconstruct the PET images with the related
but incomplete boundary information with
single or multiple computers. The proposed
approaches will not only improve the quality
of the reconstructed PET images but also
establish a bridge to an expert system for
various tomography systems.
Subjects
positron emission tomography
(PET)
(PET)
statistical inverse problems
maximum
likelihood estimator
likelihood estimator
EM algorithm
regularization
parallel algorithms
SDGs
Publisher
臺北市：國立臺灣大學醫學工程學研究所
Type
report
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