Literature DB >> 35925432

Information theoretic evaluation of Lorentzian, Gaussian, Voigt, and symmetric alpha-stable models of reversible transverse relaxation in cervical cancer in vivo at 3 T.

Pelin Ciris1.   

Abstract

OBJECTS: To better characterize cervical cancer at 3 T. MRI transverse relaxation patterns hold valuable biophysical information about cellular scale microstructure. Lorentzian modeling is typically used to represent intravoxel frequency distributions, resulting in mono-exponential decay of reversible transverse relaxation. However, deviations from mono-exponential decay are expected theoretically and observed experimentally.
MATERIALS AND METHODS: We compared the information content of four models of signal attenuation with reversible transverse relaxation. Biological phantoms and six women with cervical squamous cell carcinoma were imaged using a gradient-echo sampling of the spin-echo (GESSE) sequence. Lorentzian, Gaussian, Voigt, and Symmetric α-Stable (SAS) models were ranked using Akaike's Information Criterion (AIC), and the model retaining the highest information content was identified at each voxel as the best model.
RESULTS: The Lorentzian model resulted in information loss in large fractions of the phantoms and cervix. Gaussian and SAS models frequently had higher information content than the Lorentzian in much of the areas of interest. The Voigt model rarely surpassed the three other models in terms of information content. DISCUSSION: Gaussian and SAS models provide better fitting of data in much of the human cervix at 3 T. Minimizing information loss through improved tissue modeling may have important implications for identifying reliable biomarkers of tumor hypoxia and iron deposition.
© 2022. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).

Entities:  

Keywords:  Biophysical modeling; Cervical cancer; Gaussian; Lorentzian; Relaxometry

Year:  2022        PMID: 35925432     DOI: 10.1007/s10334-022-01035-1

Source DB:  PubMed          Journal:  MAGMA        ISSN: 0968-5243            Impact factor:   2.533


  37 in total

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Authors:  Günter Steidle; Fritz Schick
Journal:  Magn Reson Med       Date:  2020-09-30       Impact factor: 4.668

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Journal:  Magn Reson Med       Date:  1998-06       Impact factor: 4.668

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