SPECT RECONSTRUCTION ALGORITHMS AND PARALLEL COMPUTING

Project: Research project

Project Details

Description

This application is for investigation of image-reconstruction algorithms
and parallel-computing hardware for use in nuclear medicine and other areas
of medical imaging. Also included is investigation of new ways of
assessing and predicting image quality. The long-term goals of the
algorithmic research are improvements in quantitative accuracy in the
reconstructed images and improved diagnostic performance when human
observers use the images. The objective of the hardware component of the
research is to produce an economical computer system, well suited to image
reconstruction, with a performance approaching that a supercomputers. The
image-quality studies are aimed at development of model observers from
which clinically meaningful figures of merit for imaging systems may be
derived. The algorithmic studies will investigate Bayesian reconstruction, with
different forms for the data model (exact Poisson or least-squares
approximations) and different forms of prior information about the object
being reconstructed. New approaches to compensation for scattered
radiation and attenuation in SPECT will be studied, and new methods for
region-of-interest quantitation will be developed. Software for the existing parallel computer (TRIMM) will be developed,
allowing a detailed exploration of the algorithms developed on this grant,
as well as more conventional algorithms. An upgraded parallel computer,
with 1-2 GFLOPS capability, will also be designed and constructed. The figures of merit for image quality are based on performance of human or
model observers on realistic, clinically relevant tasks. Particular
emphasis will be placed on the optimum linear observer, but nonlinear
models will also be investigated. The use of model observers will be
validated by extensive psychophysical studies. When valid models are
found, they will be used to address many long-standing problems in image
reconstruction, including the optimum stopping point and regularizing
function in iterative algorithm and the effects of various forms of prior
information.
StatusFinished
Effective start/end date7/1/905/31/01

Funding

  • National Institutes of Health
  • National Institutes of Health: $404,565.00
  • National Institutes of Health: $472,176.00
  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health: $488,414.00
  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health
  • National Institutes of Health

ASJC

  • Medicine(all)

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