LapGM：一个多序列MR偏差校正和归一化模型
LapGM: A Multisequence MR Bias Correction and Normalization Model
摘要：
提出了一种空间正则化高斯混合模型LapGM，用于偏场校正和磁共振归一化问题。所提出的空间正则化器为从业者提供了平衡偏置场去除和保留多序列磁共振图像的图像对比度保留之间的微调控制。LapGM的拟合高斯参数用作控制值，其可用于跨不同患者扫描归一化图像强度。在单序列和多序列设置中，将LapGM与公知的去偏置算法N4ITK进行比较。作为标准化过程，LapGM与已知技术进行比较，例如：最大标准化、Z分数标准化和水掩蔽感兴趣区域标准化。最后，作者提供了一个CUDA加速的Python包$\texttt{lapgm}$供使用。
英文摘要：
A spatially regularized Gaussian mixture model, LapGM, is proposed for the bias field correction and magnetic resonance normalization problem. The proposed spatial regularizer gives practitioners fine-tuned control between balancing bias field removal and preserving image contrast preservation for multi-sequence, magnetic resonance images. The fitted Gaussian parameters of LapGM serve as control values which can be used to normalize image intensities across different patient scans. LapGM is compared to well-known debiasing algorithm N4ITK in both the single and multi-sequence setting. As a normalization procedure, LapGM is compared to known techniques such as: max normalization, Z-score normalization, and a water-masked region-of-interest normalization. Lastly a CUDA-accelerated Python package $\texttt{lapgm}$ is provided from the authors for use.
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