Depiction of the perfusion components’ volume fraction distribution in generalized intravoxel incoherent motion by using Gaussian mixture model

Abstract

AbstractGaussian mixture model (GMM) was proposed to depict the perfusion volume fraction distribution in the generalized intravoxel incoherent motion model (GIVIM) to improve GIVIM's ability of describing complex perfusion conditions and their changes. Different hepatic perfusion conditions were accounted for by performing different combinations of imaging sequence and diffusion time on six normal livers. In order to evaluate GIVIM‐GMM's reliability in perfusion condition analysis, the fitting to diffusion‐weighted (DW) data and the consistency between diffusion‐related parameters’ change and the data's change were tested and the recent GIVIM and the triexponential models were chosen for comparison. The difference of the fitting results was evaluated by performing the extra‐sum‐of‐squares F test and information criteria on normal human DW data. The difference of the consistency was assessed by using two‐tailed paired Student's t test. In the extra‐sum‐of‐squares F test, the relative difference ratio F values derived from the GIVIM and GIVIM‐GMM and that derived from the triexponential model and the GIVIM‐GMM are respectively 25.334 and 27.976, which indicates that significant difference existed and that the GIVIM‐GMM provides better fit to the normal human liver DW data. In information criteria test, the evidence ratio values were determined by dividing the GIVIM's or triexponential model's correct probability by the GIVIM‐GMM's. Both evidence ratio values (2.3942 × 10−10, 8.6167 × 10−9, respectively) are much smaller than 1, which also expresses that the best model used to fit the normal human liver DW data was the GIVIM‐GMM. In two‐tailed paired student's t test, the GIVIM‐GMM provides more parameters to give a finer description of perfusion than the triexponential model or GIVIM. In short, all the results demonstrated that the GIVIM‐GMM provides better performance than the existing IVIM models for depicting the signal attenuation in DW imaging.