Model Multi-Component Perfusion Of Human Tissue In Intravoxel Incoherent Motion Imaging

According to physiology, perfusion is the blood circulation in the tissue or organ. Specifically, it is the blood flows in the micro-vessel network. Statistically, most of human diseases is directly or indirectly caused by the abnormal perfusion. However, the perfusion imaging techniques that are widely applied in clinical practice are invasive and a few of them are characterized by radioactivity throughout the imaging procedure. Some frail patients can not withstand the detection from these imaging techniques and thus miss the best treatment chance. To overcome this difficulty, Le Bihan et al proposed the Intravoxel Incoherent Motion(IVIM) imaging technique. This technique is completely noninvasive since it need not the agents or contrasts. Moreover, the IVIM imaging technique can obtain high resolution image. In addition, there is no involvement of radioactive materials throughout the imaging procedure. But the classical IVIM technique did not take the multi-component perfusion into account, which seriously affect the mearments for perfusion and to some extent limit its application in clinics.On basis of the theme using the IVIM imaging technique to mearsure multi-component perfusion, the thesis works on establishing the new IVIM models capable of characterizing the multi-component perfusion and the complex microcirculatory network perfusion in order to improve the accuracy of the IVIM imaging technique. Indeed, this accuracy relies heavily on the model that is used to characterize the tissue perfusion.So, it is very essential to establish a appropriate model to replace the classical bi-exponential IVIM model to describe the tisse’s multi-component perfusion. Note that,the number of introduced parameters in the new model should be limited since the more unknown parameters need longer imaging time. In addition, an appropriate model capable of characterizing the complex microcirculatory network perfusion is also very necessary to improve the accuracy of IVIM imaging technique. Generally, the complex microcirculatory network perfusion is also the multi-component perfusion. However, since the distribution of partial micro-vessels is very unique in the microcirculatory network, it is difficulity to estabish an appropriate model to describe the complicated network. This thesis conducts research on establishing the new IVIM model capable of characterizing the multi-component perfusion and the complex microcirculatory network perfusion to overcome the limitations of the existing models.The main research works of this thesis are as follows:(1) Investigate the effect of multiple perfusion components on the parameters of the bi-exponential IVIM model and the effect degree for each parameter. The numberical simulation experiments show that the variances of IVIM parameters tend to increase when the number of perfusion components is increased or when the difference between perfusion components becomes large. The in vivo experiments based on the real data of human abdominal tissues present that the variance of the hepatic IVIM parameters is largest among the abdominal tissues since its multi-component perfusion feature is most prominent. In addition, the pseudodiffusion coefficient is most significantly affected by the multi-component perfusion.(2) For the problem that the bi-exponential IVIM model cannot characterize the multi-component perfusion, a self-adaptive multi-exponential model is proposed. Since the perfusion components presenting high pseudo-diffusivity can only be reflected by low b-value DW signals, the number of exponential terms in the self-adaptive model can be determined using the increasing exponential terms to fit the DW signals extending toward low b-values. The qualitative and quantitative evaluation experiments show that the self-adaptive multi-exponential IVIM model can to some extent reflect the characteristics of tissue’s multi-component perfusion and can more accurately describe the DW attenuation signals produced by the multiple perfusion components in comparison with the bi-exponential IVIM model.(3) For the problem that the self-adaptive multi-exponential model is overwhelmingly dependent on the b-value samples and the SNR of DW signals, a generalized IVIM model is proposed. The model uses a continuous pseudo-diffusion variable to replace a series of pseudo-diffusion coefficients in the multi-exponential IVIM model, and establish a perfusion volume fraction density function based on the variable to characterize the tissue’s multi-component perfusion. The perfusion volume fraction density function replaces a series of discrete exponential terms in the multi-exponential model in the same way as the continuous pseudo-diffusion variable. Through instantiating the function, the hepatic generalized IVIM model can be estabished. The experiments using the flow compensated technique show that the hepatic generalized IVIM model can not only reflect the perfusion change in the liver, but also can more accurately describe the hepatic DW attenuation signals. In addition, the hepatic generalized IVIM model reduces the dependence on the b-value samples and the SNR of DW signals.(4) For the problem that the existing IVIM models cannot characterize the complex microcirculatory network perfusion in the myocardial tissue, an intravoxel partially coherent and incoherent motion(IVPCIM) model is proposed. The model is the combination of the bi-exponential IVIM model and the IVPCM model. It can describe the DW attenuation signals produced together by the isotropic small vessels(e.g. arterioles and venules) and the anisotropic capillaries. The simulation experiments based on a virtual myocardial microcirculatory network model and the in vivo human heart experiments show that the IVPCIM model can reflect accurately and completely the complex microcirculatory network perfusion in the myocardial tissue. Moreover, the IVPCIM model can more accurately describe the cardiac DW attenuation signals in comparison with the bi-exponential IVIM model and the IVPCM model.