Parameters Optimization of Dynamic CT Pulmonary Perfusion Imaging Analysis Technology Based on Artificial Intelligence

In recent years, with the rapid development and popularization of artificial intelligence technology, CT has been more and more widely used in the field of clinical medicine as a non-volume micro body diagnostic method. In this paper, the artificial intelligence software is used to build a dynamic CTR lung scanning image based on the wavelet neural network model, and the genetic algorithm is used to improve the traditional visual lung image quality analysis model to improve the diagnostic efficiency. At the same time, the matrix representation weight value of the relationship between different types of disease maps is obtained by changing the threshold value to calculate the acoustic difference between the same CT tissue to determine its membership function and carry out joint optimization, After that, the effect of this technology was tested, and the results showed that the artificial intelligence based dynamic CT pulmonary perfusion imaging technology had faster generation time and higher quality than traditional algorithms in image processing, so as to achieve accurate mining of results and reduce the number of misdiagnosis. As a result, this method achieved 15 s for imaging test which comparatively lesser than Traditional imaging algorithms.