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Magn Reson Med. 2015 May;73(5):1988-98. doi: 10.1002/mrm.25323. Epub 2014 Jul 12.

Evaluation of different mathematical models for diffusion-weighted imaging of normal prostate and prostate cancer using high b-values: a repeatability study.

Author information

1
Department of Diagnostic Radiology, University of Turku, Turku, Finland.

Abstract

PURPOSE:

To evaluate monoexponential, stretched exponential, kurtosis, and biexponential models for diffusion-weighted imaging (DWI) of normal prostate and prostate cancer (PCa), using b-values up to 2000 s/mm(2) , in terms of fitting quality and repeatability.

METHODS:

Eight healthy volunteers and 16 PCa patients underwent a total of four repeated 3T DWI examinations using 16 and 12 b-values, respectively. The highest b-value was 2000 s/mm(2) . The normalized mean signal intensities of regions of interest, placed in normal tissue and PCa using anatomical images and prostatectomy sections, were fitted using the four models. The fitting quality was evaluated using Akaike information criteria and F-ratio. Repeatability of the fitted parameters was evaluated using intraclass correlation coefficient ICC(3,1).

RESULTS:

The biexponential model provided the best fit to normal prostate and PCa DWI data. The parameters of the monoexponential, kurtosis, and stretched exponential (with the exception of the α parameter) models had higher ICC(3,1) values compared with the biexponential model. The kurtosis model provided a better fit to DWI data of normal prostate and PCa than the monoexponential model, whereas these models had comparable reliability and repeatability based on ICC(3,1) values.

CONCLUSION:

Considering the model fit and repeatability, the kurtosis model seems to be the preferred model for characterization of normal prostate and PCa DWI using b-values up to 2000 s/mm(2) .

KEYWORDS:

Akaike information criteria; diffusion-weighted imaging; intraclass correlation coefficient; normal prostate; prostate cancer; repeatability

PMID:
25046482
DOI:
10.1002/mrm.25323
[Indexed for MEDLINE]

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