A Dynamic Contrast-enhanced MRI-based Numerical Simulation Technique for Early Detection of Chronic Liver Diseases
碩士 === 國立中央大學 === 數學系 === 106 === The liver is an important organ of human beings, it supports many functional mechanisms. Hepatic diseases are listed as top 10 life-threatening in many Asian countries. Generally speaking, there are three common hepatopathies for liver diseases: fibrosis, cirrhosis,...
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/f8an35 |
| Summary: | 碩士 === 國立中央大學 === 數學系 === 106 === The liver is an important organ of human beings, it supports many functional mechanisms. Hepatic diseases are listed as top 10 life-threatening in many Asian countries. Generally speaking, there are three common hepatopathies for liver diseases: fibrosis, cirrhosis,
cancer. Although a number of medical tests have developed, computer-aided diagnosis still keeps improving. We prefer to establish the non-invasive treatment of a diagnostic system for early detection. Magnetic Resonance Imaging (MRI) is a promising imaging
test nowadays. This technique provides an alternative with adding contrast agent can help to diagnose the liver diseases. The target of this research is to fit the signal enhancement curve of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) through mathematical modeling. Assume that the blood is Newtonian and viscous; tissue is treated as homogeneous, isotropic porous media and the governing equations are Darcy equation weakly coupled with unsteady convection-diffusion equation. The solution algorithm is proposed based on the concept of machine learning. As a result, we proposed an approach
to determine the fibrosis stage. The optimal value of porosity may be a useful index for early detection and obtained approximately 90% accuracy.
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