MRI (magnetic resonance imaging) technology aims to map the internal structure image of organisms. It is an important application scenario of Non-Uniform Fast Fourier Transform (NUFFT), which can help doctors quickly locate the lesion site of patients. However, in practical application, it has disadvantages such as large computation and difficulty in parallel. Under the architecture of multi-core shared memory, using block pretreatment, color block scheduling NUFFT convolution interpolation offers a parallel solution, and then using a static linked list solves the problem of large memory requirements after the parallel solution on the basis of multithreading to cycle through more source code versions. Then, manual vectorization, such as processing, using short vector components, further accelerates the process. Through a series of optimizations, the final Random, Radial, and Spiral dataset obtained an acceleration effect of 273.8×, 291.8× and 251.7×, respectively.