基于成像物理模型与流形结构的自监督磁共振指纹参数量化方法

李晓迪, 纪雨萍, 胡悦

Self-supervised Magnetic Resonance Fingerprint Parameter Quantization Method Based on Imaging Physical Model and Manifold Structure

LI Xiaodi, JI Yuping, HU Yue

表1 不同方法在BrainWeb测试集上的参数量化性能对比（均值±标准差）

Table 1 Comparison of parameter quantization performance of different methods on the BrainWeb test set (mean ± standard deviation)

方法 T1 T2 NMSE SSIM PSNR NMSE SSIM PSNR SCQ[16] 0.0071±0.0006 0.9809±0.0044 30.91±0.7194 0.0264±0.0039 0.9345±0.0168 25.37±0.7681 SPR[15] 0.0081±0.0007 0.9800±0.0043 30.32±0.4961 0.0181±0.0046 0.9526±0.0147 27.24±1.1641 CONV-ICA[29] 0.0060±0.0005 0.9842±0.0029 31.63±0.5046 0.0179±0.0025 0.8704±0.0502 27.21±0.3463 EI[30] 0.0144±0.0058 0.9783±0.0036 28.15±1.7734 0.0385±0.0088 0.9019±0.0168 22.19±2.1771 NLEI[19] 0.0147±0.0038 0.9755±0.0036 27.87±1.0816 0.0184±0.0016 0.9725±0.0031 27.06±0.4581 Distilled SPQ （本文方法） 0.0099±0.0030 0.9800±0.0030 29.60±1.2653 0.0172±0.0021 0.9003±0.0512 27.54±0.3693