An analysis method of dynamic contrast-enhanced mri

1:

• An analysis method for dynamic contrast-enhanced magnetic resonance images (DCE-MRI), which is characterized by the analysis steps described below:
• (1) obtaining the biological individual's vascular contrast agent concentration as a function of time from the time-series DCE-MRI data;
• (2) according to the time-series signal of vascular contrast agent concentration in step (1), fitting the DCE-MRI time-series signal of each pixel by the nonlinear least sum of square algorithm using the Full Shutter-Speed model (SSMfull) and the Simplified Shutter-Speed model (SSMvas) respectively, and obtaining the DCE-MRI signal fitting results of SSMfull model and SSMvas model of each pixel;
• (3) using corrected Akaike information criterion (AICc) to score and compare the DCE-MRI signal fitting results of the SSMfull model and the SSMvas model in each pixel, according to the score from the corrected Akaike information criterion evaluating the SSMfull model and the SSMvas model in each pixel, selecting the optimal model from the SSMfull model and the SSMvas model for each pixel;
• (4) carrying out fitting according to the optimal model selected in step (3);
• if the optimal model being SSMfull model, producing distribution maps of five groups of physiological parameters produced after fitting;
• the five groups of physiological parameters being the contrast agent (CA) volume transfer constant between blood plasma and extravascular-extracellular space (Ktrans), intravascular water mole fraction (pb), extravascular-extracellular water mole fraction (po), the vascular water efflux rate constant (kbo) and the cellular water efflux rate constant (kio);
• if the optimal model being SSMvas model, due to po and kio not being considered as estimated parameters, obtaining only distribution maps of three groups of physiological parameters after fitting;
• the three groups of physiological parameters being Ktrans, pb and kbo.
• (5) performing error analysis on the kio and kbo obtained in step (4) and only reserving the pixel results with 95% confidence interval in the range of [0 s−1 20 s−1] or the lower limit of 95% confidence interval greater than 5 s−1, resulting the final kio and kbo parametric distribution maps and the Ktrans, pb, po parametric distribution map.