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Automated Analysis of Dynamic Ca2+ Signals in Image Sequences
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A new automated method for analysis of rCBF-SPECT images based on the active-shape algorithm: normal values.

Douglas Hägerström1, David Jakobsson, Erik Stomrud

  • 1Clinical Neurophysiology Unit, Department of Clinical Sciences, Lund University, Sweden. Douglas.Hagerstrom@skane.se

Clinical Physiology and Functional Imaging
|February 3, 2012
PubMed
Summary

A new automated method quantifies regional cerebral blood flow (rCBF) from SPECT images, establishing normal values. This tool aids clinicians in interpreting rCBF-SPECT scans more effectively.

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Area of Science:

  • Nuclear Medicine
  • Medical Imaging Analysis
  • Neuroscience

Background:

  • Current interpretation of regional cerebral blood flow (rCBF) from single-photon emission computed tomography (SPECT) predominantly relies on visual assessment by nuclear medicine clinicians.
  • There is a need for an objective and user-friendly method for quantifying rCBF from SPECT images in clinical practice.

Purpose of the Study:

  • To develop an automated, easy-to-use method for quantifying rCBF from SPECT images.
  • To establish normal rCBF values using the developed method in a healthy population.

Main Methods:

  • A novel 3-dimensional method was created utilizing a brain-shaped model and the active-shape algorithm.
  • The algorithm defines brain surface shape and projects maximum counts to designated surface points, dividing them into cortical regions.
  • (99m)Tc-HMPAO SPECT was performed on 30 healthy volunteers (mean age 74 years).

Main Results:

  • The active-shape algorithm demonstrated satisfactory performance in defining brain shape.
  • Quantification revealed normal rCBF values in frontal, temporal, and parietal lobes ranging from 87-88% relative to the cerebellum.
  • No significant gender-based differences were found in normal rCBF values, with only a weak correlation to age.

Conclusions:

  • The developed automated method successfully quantifies rCBF-SPECT images and establishes expected normal value ranges.
  • Further research is required to validate the clinical utility of this automated quantification method and its derived normal values.