Most of the existing wavelet image processing techniques are carried out in the form of single-scale reconstruction and multiple iterations. However, processing high-quality fMRI data presents problems such as mixed noise and excessive computation time. This project proposes the use of matrix operations by combining mixed noise elimination methods with wavelet analysis to replace traditional iterative algorithms. Functional magnetic resonance imaging (fMRI) of the auditory cortex of a single subject is analyzed and compared to the wavelet domain signal processing technology based on repeated times and the world's most influential SPM8. Experiments show that this algorithm is the fastest in computing time, and its detection effect is comparable to the traditional iterative algorithm. However, this has a higher practical value for the processing of FMRI data. In addition, the wavelet analysis method proposed signal processing to speed up the calculation rate.

通过矩阵操作结合混合噪声消除方法和小波分析，取代传统的迭代算法，实现功能性磁共振成像数据的处理，计算时间最快且检测效果可与传统迭代算法相媲美，提高了处理功能性磁共振成像数据的实用价值。

基于计算机深度学习的脑部疾病MRI特征提取数据处理系统研究