Improved Liver R2 Mapping by Pixel-Wise Curve Fitting With Adaptive Neighborhood Regularization
Improved Liver R2 Mapping by Pixel-Wise Curve Fitting With Adaptive Neighborhood Regularization
复制标题
通过自适应邻域正则化的逐像素曲线拟合改进肝脏 R2 映射
DOI：
10.1002/mrm.27071
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发表时间：
2018
影响因子：
3.3
通讯作者：
Feng YQ
中科院分区：
文献类型：
--
作者：
Wang Changqing;Liu Xiaoyun;Chen Wufan;Wang Changqing;Zhang Xinyuan;Chen Wufan;Feng Qianjin;Feng Yanqiu;Wang Changqing;He Taigang;He Taigang;He Taigang;Feng YQ
目的通过将自适应邻域正则化纳入逐像素曲线拟合来改进肝脏制图。方法由于序列图像的信噪比较低，磁共振成像作图仍然具有挑战性。在本研究中，我们提出利用相邻像素作为正则化项，并根据像素间信号相似度自适应确定正则化参数。该算法被称为基于自适应邻域正则化（PCANR）的逐像素曲线拟合（pixel - wise curve fitting with adaptive neighborhood regularization），并在模拟、模拟和活体数据上与传统的非线性最小二乘（NLS）和基于非局部均值滤波的NLS算法进行了比较。结果从视觉上看，PCANR算法生成的地图具有显著降低的噪声和保存良好的微小结构。定量地说，与NLS和基于非局部均值滤波的NLS算法相比，PCANR算法在不同值和信噪比水平下产生的地图具有更低的均方根误差。对于低信噪比水平下的高值，PCANR算法在准确度和精密度方面分别优于NLS和基于非局部均值滤波的NLS算法，即在所选兴趣区域内测量的平均值和标准差。结论PCANR算法可以降低噪声对肝脏测图的影响，提高测量精度，有利于临床对肝铁的评估。中华医学杂志，2018,31(2):391 - 391。©2018国际医学磁共振学会。
PurposeTo improve liver mapping by incorporating adaptive neighborhood regularization into pixel‐wise curve fitting.MethodsMagnetic resonance imaging mapping remains challenging because of the serial images with low signal‐to‐noise ratio. In this study, we proposed to exploit the neighboring pixels as regularization terms and adaptively determine the regularization parameters according to the interpixel signal similarity. The proposed algorithm, called the pixel‐wise curve fitting with adaptive neighborhood regularization (PCANR), was compared with the conventional nonlinear least squares (NLS) and nonlocal means filter‐based NLS algorithms on simulated, phantom, and in vivo data.ResultsVisually, the PCANR algorithm generates maps with significantly reduced noise and well‐preserved tiny structures. Quantitatively, the PCANR algorithm produces maps with lower root mean square errors at varying values and signal‐to‐noise‐ratio levels compared with the NLS and nonlocal means filter‐based NLS algorithms. For the high values under low signal‐to‐noise‐ratio levels, the PCANR algorithm outperforms the NLS and nonlocal means filter‐based NLS algorithms in the accuracy and precision, in terms of mean and standard deviation of measurements in selected region of interests, respectively.ConclusionsThe PCANR algorithm can reduce the effect of noise on liver mapping, and the improved measurement precision will benefit the assessment of hepatic iron in clinical practice. Magn Reson Med 80:792–801, 2018. © 2018 International Society for Magnetic Resonance in Medicine.