This poster is published under an open license
. Please read the disclaimer
for further details.
Abdomen, Liver, Oncology, MR-Diffusion/Perfusion, Statistics, Metastases
G. C. Manikis1, K. Marias1, K. Nikiforaki1, N. Kartalis2, N. Albiin2, N. Papanikolaou2; 1Iraklion/GR, 2Stockholm/SE
Methods and materials
10 patients with pancreatic cysts and no evidence of metastatic liver disease (group A) and 10 consecutive patients with liver metastases (group B) underwent free breathing diffusion weighted imaging using 10 b values (0,
2000 and 3000).
ROIs in the first group were drawn οn normal liver parenchyma avoiding vessels while for the second on liver metastatic tissue.
Four different models including mono-exponential (Gaussian and non-Gaussian), as well as,
bi-exponential (Gaussian and non-Gaussian) were applied.
Mono-exponential and bi-exponential diffusion models were used both in Gaussian and non-Gaussian form to fit the data and extract the relevant diffusion imaging biomarkers.
ADC was calculated based on the mono-exponential Gaussian model,
while two components were used in the bi-exponential Gaussian model,
the true Diffusion and Pseudo-Diffusion,
leading to the quantification of f (microperfusion fraction),
D* (pseudo diffusion coefficient) and D (true diffusion coefficient).
D(K) (non-Gaussian Diffusion coefficient),
and K (kurtosis) were calculated based on the mono-exponential non-Gaussian model,
while the bi-exponential non-Gaussian model was used to extract f(k),
D(K) and K.
Four different statistical parameters including adjusted R-square (adj-R2),
root mean square error (RMSE),
sum of squares due to error (SSE) and corrected Akaike information criterion (cAIC)  were calculated for each model.
Wilcoxon signed-rank and Dunn's non-parametric statistical tests were used to disclose any significant differences between all four models.