Robustness of textural analysis features in quantitative 99 mTc and 177Lu SPECT-CT phantom acquisitions

The use of a uniform phantom to test volume dependency and a Revolver phantom to identify repeatable Textural Analysis features is possible for quantitative SPECT using <sup>99 m</sup>Tc or <sup>177</sup>Lu. Selection of such features is likely to be centre-dependent due to differences in camera performance as well as acquisition and reconstruction protocols.

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Publication Details

Journal
EJNMMI Physics
Published
2025-04-17
DOI
https://doi.org/10.1186/s40658-025-00749-0
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Robustness of textural analysis features in quantitative 99 mTc and 177Lu SPECT-CT phantom acquisitions

Surajit Ray, Alexander R. Small, Alastair J. Gemmell, Colin M Brown
EJNMMI Physics
Radiomics and Machine Learning in Medical Imaging
article

Robustness of textural analysis features in quantitative 99 mTc and 177Lu SPECT-CT phantom acquisitions

Surajit Ray, Alexander R. Small, Alastair J. Gemmell, Colin M Brown
article en

Abstract

The use of a uniform phantom to test volume dependency and a Revolver phantom to identify repeatable Textural Analysis features is possible for quantitative SPECT using <sup>99 m</sup>Tc or <sup>177</sup>Lu. Selection of such features is likely to be centre-dependent due to differences in camera performance as well as acquisition and reconstruction protocols.

EJNMMI PhysicsVol. 12(1)
NHS Greater Glasgow and Clyde (GB), Gartnavel General Hospital (GB), University of Glasgow (GB)
No poverty
Openalex Percentile: Top 88%
Radiomics and Machine Learning in Medical Imaging
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