Characterized by wisdom and creativity, human beings are huge, complex, giant systems. Each person's life is
experienced the process of birth, growth, aging and death. The genetic stability keeps human beings no change, and the
mutation keeps the human beings in progress. The balance between stability and mutation are controlled by the nature
laws automatically. But the balance often broken because the body's biochemical processes is out of order in vivo, which
is scaled by quantitative concentrations for all molecular in human body. Now day, the biomedical imaging tools can
investigate these process quantitatively.
Dynamic contrast enhanced (DCE) MRI is the method of choice for clinical tumor detection. There are three categories
of DCE-MRI analysis methods including qualitative DCE-MRI, semi-quantitative DCE-MRI, and quantitative
DCE-MRI. Among these three categories, quantitative DCE-MRI is the only one that could provide pharmacokinetic
parameters and directly reveal physiological state of tumor. In this paper, we have made a brief review on quantitative
DCE-MRI. Firstly, physiological basis of DCE-MRI was described in details. Then, two kinds of contrast agent, low
molecular weighted contrast media (LMCM) and macromolecular contrast media (MMCM), are introduced respectively.
After that, several T1 weighted DCE-MRI data analysis methods are introduced, too. At last, possible further
developments of DCE-MRI have been discussed.
In this paper, we propose a coupled level-set framework for segmentation of bladder wall using T1-weighted magnetic
resonance (MR) images. The segmentation results will be used for non-invasive MR-based virtual cystoscopy (VCys).
The framework uses two level-set functions to segment inner and outer borders of the bladder wall respectively. Based
on Chan-Vese (C-V) model, a local adaptive fitting (LAF) image energy is introduced to capture local intensity contrast.
Comparing with previous work, our method has the following advantages. First of all, unlike most other work which
only segments the boundary of the bladder but not inner border and outer border respectively, our method extracts the
inner border as well as the outer border of bladder wall automatically. Secondly, we focus on T1-weighted MR images
which decrease the image intensity of the urine and therefore minimize the partial volume effect (PVE) on the bladder
wall for detection of abnormalities on the mucosa layer in contrast to others' work on CT images and T2-weighted MR
images which enhance the intensity of the urine and encounter the PVE. In addition, T1-weighted MR images provide
the best tissue contrast for detection of the outer border of the bladder wall. Since MR images tend to be inhomogeneous
and have ghost artifacts due to motion and other causes as compared to computer tomography (CT)-based VCys, our
framework is easy to control the geometric property of level-set functions to mitigate the influences of inhomogeneity
and ghosts. Finally, a variety of geometric parameters, such as the thickness of bladder wall, etc, can be measured easily
under the level-set framework. These parameters are clinically important for VCys. The segmentation results were
evaluated by experienced radiologists, whose feedback strongly demonstrated the usefulness of such coupled level-set
framework for VCys.