Research Of Tumor Treatment Effect Based On PET/CT Image Anabsis

Positron emission tomography/X-ray computed tomography imaging (PET/CT) is the world’s most advanced, most high-end medical imaging equipment. It really opens the door to human medical molecular imaging era. PET/CT is actually an organic combination of PET and CT: PET takes the positron radionuclide as tracer which is taken up by the body. The PET image reflects the organs tissues and lesions’metabolic state though the uptake values of tracer. Generally the distribution of the tracer displays the physiological and pathological features of various organs.PET/CT makes it easier to detect foci of infection remain; CT provides the morphological characteristics of the lesions. The two makes complement for each other so that it is comprehensive. PET/CT greatly improves the diagnostic accuracy and clinical value. The trend which PET/CT imaging becomes the mainstream of medical imaging technology has become more and more apparent in the field of medical images. Therefore, the research of PET/CT image has a very important theoretical and practical value.PET imaging, especially the whole body imaging, can detect lesion transfer which is accurate and sensitive, comprehensive, a glance to reflect the severity of the disease. It has a unique value in terms of staging, cancer efficacy evaluation, treatment prediction. PET/CT images have been widely applied in tumor detection, imaging and the development of cancer radiation plan. Addition, many researches try to demonstrate the function of PET/CT image in the prediction of tumor treatment effect and assessment the effect. For the potential value of PET/CT in the evaluation and prediction of the effect of tumor treatment, this paper focuses on the tumor geometry, grayscale and texture characteristics of the three aspects of PET/CT images to analyze this issue.The assessment of tumor treatment is conducted by an experienced oncology doctor who observes the changes of the patient PET/CT images of the tumor region to determine the effect. Of course there will be occasional human factors in such a decision. And greater error may also be involved due to the limit of human eye resolution to the discrimination of local small region. There is no uniform standard to determine in the process. To address this issue, we compare and analyze the changes of two lung cancer patients’PET and CT images which are collected from the clinical. The statistics are extracted from the images which are before and after treatment. Statistics are based on the geometry, texture features and image gray-scale changes in the treatment. By contrast we can further study the different characteristics of patient outcomes make a reasonable forecast. We can reduce the subjectivity of the diagnosis and the assessment of treatment results through quantified statistics to improve the correct rate of diagnosis and treatment. Also we can make a reasonable treatment effect prediction.