Three-dimensional Visualization Research Of Neural Structures In Brain Based On Medical Image Fusion

Background:Brain is the most delicate organ in human body, understanding and mastering of morphology and function of brain is crucial to the disease treatment. So the position and morphology of the neural structures in brain must be investigated completely and scientifically. At past, imaging devices such as CT or MRI is used to acquire the images of neural structures two-dimensionally. The doctors have to imagine the shape and position of the structures according to these images. As the development of computer technology, the computer aided three dimensional reconstruction is widely used in medical area. The sectional images such as CT, MRI are used to reconstruct the neural structures three dimensionally. Due to the limitation of these devices imaging principle, much of the important information in skull, especially some important deep nucleuses, cannot be acquired by these equipments. Thus, their applications in clinic practice are restricted. To solve this problem, some people try the atlas, i.e., superimpose the atlas on the images after registration of the two images. But it has shortcoming: most atlases are drawn by hand and is subjected to individuals; some atlases are based on MRI images. So, the amount of information is limited by the resource data. The visible human dataset is acquired by postmortem cryosectioning technology, has the advantage of high accuracy and large amount of information, can display neural structures accurately. But it only express common information of human body, and has little value in clinical practice. Medical fusion technology provides new method to solve the problem. Recently, there are many reports about medical image fusion. But all those fusions are aimed at CT, MRI, PET and SPECT of the same patient. Although the technology and devices of imaging is improved gradually, as to much important clinical information,cannot be acquired or displayed clearly by these devices. It is seldom reported as to how to acquire these important information at present.Objective:Try to solve the problem of information shortage of radiological data in clinical diagnosis and treatment.Methods:In this research, Chinese Virtual Human dataset was segmented using automatic segmentation based on gray threshold and manual segmentation based on anatomic knowledge. In order to remove the error result from the manual segmentation, image-transparency method was used at first, then each image was smoothed by eroding and dilating, nuclear structures are reconstructed and displayed using surface rendering and volume rendering lastly. Circumscribing-circle theory is raised to improve the reconstrion speed.We registered and fused the CVH dataset with MRI. In the process of registration, Chamfer Matching method and Maximization of Mutual Information method were used separately. A changeable-step method was raised to improve the Chamfer Matching. In the registration of Mutual information, rigid registration was first used to make the two images coincide basically. Aimed at the difference between individuals, the nonrigid registration was used to revise it. Thus, the aim of accurately registration was achieved, and laid a foundation for the problem of neural structures shortage in patients' radiological data.Results:The reconstructed nuclear structures are smooth, natural and realistic. Their shapes and positions are clearly displayed after the surface of the brain is set as transparency, and can be rotated, observed in any directions. After rigid and nonrigid registration the CVH and MRI images coincide with each other basically. The neural structures in visible human data and pathological area in MRI can be shown at the same image. After reconstruction, pathological area and neural structures can be clearly displayed three dimensionally.Conclusion:The reconstructed structures have great reference value to the clinical diagnosis and treatment and anatomic teaching and learning. Our research indicates that the fusion of the CVH and patient's MRI can solve the problem of shortage of neural information and lay a foundation for the clinical use of CVH dataset.