Optimization of b-value distribution for four mathematical models of prostate cancer diffusion-weighted imaging using b values up to 2000 s/mm(2): simulation and repeatability study
To find optimal b-value distributions for monoexponential, stretched exponential, kurtosis, and biexponential models of prostate cancer (PCa) diffusion-weighted imaging (DWI) using simulations and repeated DWI examinations. Simulations aiming to minimize estimation accuracy error were performed. Ten...
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Veröffentlicht in: | Magnetic resonance in medicine 2015-05, Vol.73 (5), p.1954-1969 |
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Zusammenfassung: | To find optimal b-value distributions for monoexponential, stretched exponential, kurtosis, and biexponential models of prostate cancer (PCa) diffusion-weighted imaging (DWI) using simulations and repeated DWI examinations.
Simulations aiming to minimize estimation accuracy error were performed. Ten PCa patients underwent in total four repeated 3-tesla DWI examinations using 12 equally spaced b values (0-2000 s/mm(2) ). Normalized mean signal intensities of regions-of-interest placed in normal tissue and PCa were fitted. In total, 210 different b-value combinations consisting of six b values, 0 and 100 s/mm(2) included in every b-value distribution, were evaluated in terms of accuracy and repeatability.
The simulations and in vivo DWI data suggest the optimal b-value distribution for the monoexponential model consists of four to five equally distributed b values in the range of 0 to 1200 s/mm(2) . The parameters of the stretched exponential and kurtosis models are best estimated using five to seven b values in the ranges of 300 to 700 and close to 2000 s/mm(2) , in addition to low b value. B-value distribution consisting of eight to 10 b values in the ranges of 0 to 100, 800 to 1200, and 1800 to 2000 s/mm(2) is the preferred method for estimation of the biexponential model parameters of PCa DWI.
The optimized b-value distributions demonstrated improved estimation accuracy and repeatability of DWI signal decay-derived parameters. |
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ISSN: | 1522-2594 |
DOI: | 10.1002/mrm.25310 |