Study on the Relaxation Time Characteristics of Brain Tissue Based on Multi-Parametric Quantitative Magnetic Resonance Imaging

Abstract

Traditional magnetic resonance imaging (MRI) is qualitative imaging, and doctors need to rely on experience to diagnose diseases, which cannot meet the current needs of precision medicine. As a new quantitative magnetic resonance imaging technology, magnetic resonance fingerprint imaging can obtain a variety of human tissue parameters at the same time through a data acquisition, which greatly improves the imaging speed and improves the impact of noise on image quality. Several pattern matching algorithms are compared, including direct matching method, Bloch response iterative projection method, covering tree and approximate nearest neighbor search method, and improved methods. Absolute error image, mean absolute error (MAE), normalized root means square error (RMSE) and running time are counted in the experimental results. The results show that the improved method is better than the traditional method, which can greatly improve the quality of MR fingerprint multi-parameter images (T1, T2, B0, PD), and make the running time within an acceptable range. In addition, the improved algorithm is insensitive to random additive noise.