The Study Of Quantitative Analysis Methods Of Cervical Spondylotic Myelopathy Based On DTI

Diffusion tensor imaging (DTI) technique developed in recent years based onmagnetic resonance (MRI) technologies is the non-invasive diagnosis of medicalimaging new methods. It uses tissue internal water molecular diffusion movementof the anisotropy to image, and it can reflect the space of living tissue compositioninformation and pathologic state organizations water molecules between exchangesituations. Using DTI data of white matter in the nerve fibers3D reconstruction andquantitative analysis is a hot topic are the important and hot topic in the currentmagnetic resonance diffusion imaging (DT-MRI) research area. This paper does thepreliminary research on how to evaluate the disorder degree of the fiber and thefiber tracking quantitatively.This paper analyzes the main quantitative characteristic parameters used foranalysis of the cervical spinal cord DTI data, such as FA, ADC and orientationentropy. Based on the information entropy theory, we put forward the orientationentropy algorithm about the principle eigenvector. The algorithm uses the basicideas of the information entropy to judge fiber information disorder degree, andmake use of the least squares method and linear regression analysis method toestimate the feature of data. What’s more, this paper compares the DTI analysisresults with clinical diagnosis and MRI measurements on the pathogenesis levels. Itfigures out the consistency among these three methods. It turns out that DTI hashigh validity and reliability.In fiber cross or fusion area, fiber orientation is not obvious. Based on thissignificant problem, this paper puts forward a kind of backtracking algorithm basedon streamline continuous fiber tracking algorithm. This algorithm can well describethe fiber orientation, avoid, fiber go done uncertainty problems in the anisotropydegree low area, and has good robustness for noise. We also use fiber density andlength to evaluate the tracking effect briefly.This paper applies the orientation entropy and its corresponding estimation andfiber tracking algorithm to the patients with cervical spondylotic myelopathy(CSM)to diagnosis main pathogenic segmental of multi-compression level. In this study,the orientation entropy based on the eigenvector was used to analyze the changes ofcervical spinal cord. Generally, due to the spinal cord bundle of directionaluniformity, this kind of method used on spinal cord is more likely to detect thechanges of the direction than in brain. Orientation entropy as a kind of method to measure the fiber direction disorder degree is easy to detect single levelcompression characteristics. In addition, multi-level compression in the clinical isvery much common and there is not a clear method to evaluate pathogenic segmentand the segmental compression degree. This method put forward for clinicaldiagnosis and how to aim at the surgical decompression in order to reduce thepostoperative complications in patients will be a great challenge. The results of thisstudy show DTI technology in estimating main pathogenic multi-level compressionin CSM have obvious advantages. This paper also realizes the improved algorithmon the simulated data and clinical data. The results turn out that the improvedalgorithm has high accuracy and effectiveness.