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Abdomen, Colon, Gastrointestinal tract, MR-Diffusion/Perfusion, Technical aspects, Cancer
G. C. Manikis1, K. Marias1, K. Nikiforaki1, D. M. J. Lambregts2, M. V. Heeswijk2, R. G. H. Beets-Tan2, N. Papanikolaou3; 1Heraklion/GR, 2Maastricht/NL, 3Stockholm/SE
Diffusion data were post processed by an in house software (ICS,
FORTH) which is able to produce pixel based parametric maps of a number of model related parameters.
All pixel values belonging to the tumor,
as marked by an expert radiologist,
were used as input for signal intensity curves as a function of b-value.
Model specific curves were grafically overlaid on the data in order to gain insight into each model performance qualitatively.
For visual and quantitative evaluation of each approach,
statistical metrics permitted direct comparison of the fitting outcome ( Fig. 1 ).
An indicative analysis result,
when the goodness of fit of the four models was assessed by the adjusted R-square,
is outlined in Fig. 2.
Boxplots were generated according to the adj-R2 value in every pixel in the examined region,
showing a clear evidence that the bi-exponential models in general better fit the data of each patient.
bi-exponential non-Gaussian model was proved to fit the experimental data better than any other model according to 3 out of 4 statistical measures (adj-R2,
RMSE and SSE,
p<0.01 in all pairwise comparisons).
Fig. 4 and Fig. 5 highlight this observation in a qualitative way.
standard deviation and square error of each statistical measure is presented in Table 1,
Table 2 and Table 3 respectively.
The cAIC showed no clear differences except that the non-Gaussian bi-exponential model was less accurate than the other 3 models (Fig. 6 and Table 4).
The imaging biomarkers derived from the four models are quantitatively presented in Table 5,
Table 6 and Table 7.