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Related Concept Videos

Positron Emission Tomography01:29

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body being...

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Related Experiment Video

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Radiotracer Administration for High Temporal Resolution Positron Emission Tomography of the Human Brain: Application to FDG-fPET
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Voxel-based classification of FDG PET in dementia using inter-scanner normalization.

Frank Thiele1, Stewart Young, Ralph Buchert

  • 1Molecular Imaging Systems, Philips Research, Aachen, Germany. frank.o.thiele@philips.com

Neuroimage
|April 2, 2013
PubMed
Summary

This study shows that automated classification of dementia using FDG PET brain scans is feasible. A novel ratio-image normalization method improves accuracy when using data from different PET scanners.

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Area of Science:

  • Neuroimaging
  • Medical Informatics

Background:

  • Statistical mapping of FDG PET brain images is crucial for diagnosing dementia.
  • Variations between PET scanner databases can impact classification accuracy.

Purpose of the Study:

  • To evaluate the impact of different PET scanner databases on classification accuracy.
  • To assess a ratio-image normalization method for improving inter-scanner classification.

Main Methods:

  • A voxel-based classification system using partial least squares (PLS) was developed.
  • Brain FDG PET databases from three scanners (normal controls, Alzheimer's disease, frontotemporal dementia) were used.
  • Ratio-image normalization was applied to mitigate inter-scanner variations.

Main Results:

  • In-scanner classification achieved 94% accuracy.
  • Cross-scanner classification accuracy decreased to 79-91%.
  • Ratio-image normalization improved cross-scanner accuracy to 85-92%.

Conclusions:

  • Automated FDG PET classification for dementia diagnosis is feasible.
  • Scanner and acquisition characteristics influence classification accuracy.
  • Ratio-image normalization effectively moderates inter-scanner effects.